AI Common Terms

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The AI Learning Ladder: Your Step-by-Step Guide to Understanding Artificial Intelligence

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grounding - citing sources search - retrieving info from the web

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Rung 0 – The Foundation: Three Essential Building Blocks

Before we dive into AI, let's establish three fundamental concepts. Everything else in AI builds on these, so let's make sure we're crystal clear on what they mean.

Term	What It Really Means (in Simple Terms)	A Real-World Example
Data	Any information a computer can use. This includes text, photos, numbers in a spreadsheet, or even your voice.	The photos on your phone are data. The words in this sentence are data. The songs in your music library are data.
Algorithm	A precise set of instructions that tells a computer exactly what to do, step-by-step.	A recipe for baking cookies is an algorithm. It has a list of steps that must be followed in a specific order to get the right result.
Artificial Intelligence (AI)	A computer system that can perform tasks we normally think require human intelligence.	Your phone recognizing your face to unlock, Netflix recommending shows you might like, or a smart assistant understanding your questions.

Ready to climb? Now that we have our three core ingredients, let's see what happens when we combine them to create something that can actually learn.

Rung 1 – From Ingredients to Intelligence: How AI Actually Learns

Here's where it gets exciting. We're going to take our building blocks from Rung 0 and see how they work together to create systems that can learn and make predictions.

Term	What It Really Means (and How It Connects)	An Everyday Analogy
Model	The end result after an algorithm has finished learning from data. It's like a "brain" that has been trained and can now make decisions or predictions.	Think of a chef who has studied hundreds of recipes (data). The chef's knowledge and intuition is now the model—they can create new dishes without a recipe book.
Training	The learning process where we show the algorithm thousands or millions of examples so it can find patterns and improve.	It's like teaching a child to recognize animals by showing them many pictures: "This is a dog, this is a cat, this is a dog..." Eventually, they learn to tell them apart on their own.
Input / Output	Input is what you give to the model (like a question or a photo). Output is what the model gives back (like an answer or a label).	Input: You ask your smart speaker, "What's the weather today?" Output: The speaker replies, "It's sunny with a high of 75 degrees."
Weight (or Parameter)	A single adjustable number inside the model. Millions of these numbers work together to store everything the model has learned.	Think of them as the individual knobs on a giant sound mixing board. During training, the algorithm carefully adjusts each knob to get the perfect sound (output).
Loss Function	A mathematical score that measures how wrong the model's answers are during training. A lower score means better answers.	It's like a teacher grading a test. The loss function counts how many questions the model got wrong. The goal of training is to get the lowest score possible.
Gradient Descent	The clever mathematical technique that figures out exactly how to adjust each weight to reduce the loss function's score.	It's like adjusting the hot and cold water knobs in a shower. You make small, smart adjustments until the temperature (output) is just right.
Epoch	One complete pass where the model has seen all the training data from start to finish.	It's like reading an entire textbook once from cover to cover. Most training involves many epochs, so the model reviews the material multiple times to learn it well.
Batch	A small group of training examples that are processed together before the model's weights are updated.	Instead of studying one flashcard at a time, you review a small stack of 10-20 cards, then pause to let the information sink in. This makes training more efficient.

Moving up: Now you understand the mechanics of how AI learns. But just as there are different ways to teach people, there are different strategies for training AI. Let's explore them!

Rung 2 – Teaching Strategies: Different Ways AI Can Learn

Just as people learn differently—some from textbooks, others from experience—AI systems have different learning approaches depending on the goal.

Term	What It Really Means	A Real-Life Learning Parallel
Supervised Learning	Teaching an AI with a complete answer key. Every piece of training data is labeled with the correct answer, so the model learns by comparing its guesses to the truth.	This is like studying with flashcards that have the question on the front and the answer on the back. You guess, flip the card, and immediately see if you were right.
Unsupervised Learning	Letting the AI find patterns on its own without being told what's right or wrong. The data has no labels or correct answers.	It's like giving someone a huge box of mixed LEGO bricks and asking them to sort them. They might group them by color, size, or shape, finding patterns without being told which way is "correct."
Reinforcement Learning	Teaching an AI through rewards and penalties. The model (called an "agent") learns from the consequences of its actions.	This is exactly like training a dog. You give it a treat (reward) for sitting, but say "No!" (penalty) for jumping on the couch. Over time, the dog learns which behaviors lead to rewards.
Overfitting	When your model memorizes the training data instead of learning the general patterns. It does great on examples it's seen before but fails on new, unseen data.	Imagine a student who memorizes the answers to last year's exam. They'll ace those exact questions but will fail the real test if the questions are slightly different.
Underfitting	When your model is too simple to capture the important patterns in your data. It fails to learn, even with lots of training.	This is like trying to summarize a complex movie with only one sentence. No matter how you phrase it, you'll miss all the important details.
Regularization	A collection of techniques used during training to prevent overfitting. It forces the model to learn simpler, more general patterns.	It's like a teacher telling students they can only use a single, small index card for notes during an exam. It forces them to truly understand the concepts instead of just copying the book.
Dropout	A specific regularization technique where parts of the model are randomly ignored or "turned off" during each step of training.	This is like practicing a team sport with a few players randomly sitting out for each play. It forces the other players to learn how to work together in different ways and not rely on just one star player.

Moving up: Now let's explore the specific architecture that revolutionized AI—neural networks, the technology inspired by the human brain!

Rung 3 – Building Electronic Brains: Understanding Neural Networks

This is where AI gets its "neural" inspiration. While much simpler than biological brains, these networks have proven incredibly powerful for learning complex patterns.

Term	What It Really Means	How It's Like a Brain (Loosely!)
Neural Network	A network of simple computing units (called "neurons") connected in layers. Each connection has an adjustable weight that gets tuned during training.	It's like a massive telephone switchboard. Operators (neurons) receive calls (inputs), process them, and route them to other operators in the next layer.
Deep Learning	The use of neural networks with many layers (typically 3 or more, but modern ones can have hundreds).	"Deep" just means the network has many layers. More layers allow the model to learn more complex and abstract patterns from the data, like identifying a face instead of just lines and shapes.
Backpropagation	The technique for teaching neural networks by sending error signals backward through the network, from the final output to the first input.	It's like a game of telephone in reverse. If the final message is wrong, you trace it backward, asking each person what they heard, to find out where the mistake happened and correct it for next time.

Moving up: Neural networks were powerful, but the real revolution came with a specific design for understanding language. Let's explore the breakthrough that gave us today's conversational AI!

Rung 4 – The Language Revolution: How AI Learned to Chat

This is where AI made the leap from recognizing images to having conversations. These innovations led to ChatGPT, Claude, and other modern AI systems.

Term	What It Really Means	An Everyday Comparison
Token	A chunk of text that the model processes as one unit—usually a word or part of a word.	Think of breaking a sentence into Scrabble tiles. Each tile (token) is a single piece that the game (model) can work with.
Context Window	The maximum amount of text (measured in tokens) that a model can "remember" and consider at one time.	It's like your short-term memory when reading a book. You can remember what happened in the current chapter, but you might have forgotten a minor detail from 200 pages ago.
Embedding	The process of converting a token into a list of numbers that captures its meaning and relationships to other words.	It's like giving every word its own unique GPS coordinate. Words with similar meanings (like "king" and "queen") will have coordinates that are close to each other.
Vector	The actual list of numbers that represents a token's meaning (its "GPS coordinate").	This is the numerical input that a neural network can actually process. The model learns to do math on these vectors to understand language.
Transformer	A powerful neural network design that is exceptionally good at understanding context in sequential data like text.	It's like a reader who can instantly see the connections between every word in a paragraph at the same time, rather than just reading one word after another.
Attention Mechanism	The special ability of a transformer to weigh the importance of all other tokens in the context window when processing a single token.	When you read the sentence "The robot picked up the red ball," attention helps the model know that "it" in a later sentence likely refers to the "ball," not the "robot."
Large Language Model (LLM)	A massive transformer model (with billions of weights) that has been trained on enormous amounts of text to predict the next token in a sequence.	It's like a super-powered autocomplete. After reading nearly the entire internet, it has become incredibly good at predicting what word should come next in any given sentence.
Generative AI	AI systems that can create new, original content (like text, images, code, or music) rather than just analyzing existing data.	An artist who can paint a new masterpiece is a generative artist. An AI that can write a new poem or create a unique image is Generative AI.

Moving up: Training these massive models costs millions of dollars. Fortunately, we can reuse that work. Let's see how!

Rung 5 – Standing on Giants' Shoulders: Reusing Existing Models

Why spend millions training a model from scratch when you can start with one that already understands language? This is like learning a new skill faster because you already have related knowledge.

Term	What It Really Means	A Real-World Analogy
Pre-training	The initial, expensive phase where a huge model like an LLM learns general knowledge from a massive, broad dataset.	This is like getting a university degree. It's expensive and time-consuming, but it provides a broad foundation of knowledge that can be applied to many different jobs later on.
Transfer Learning	The general strategy of taking a pre-trained model and adapting it for a new, specific purpose.	It's like hiring an experienced chef who already knows how to cook (pre-trained) and just teaching them your restaurant's specific menu, rather than teaching someone how to boil water.
Fine-tuning	The actual process of continuing to train a pre-trained model, but on your own smaller, specialized dataset.	This is the hands-on training for the experienced chef. You give them your recipes (fine-tuning data) and let them practice until they master your restaurant's style. This is much faster and cheaper than starting from scratch.

Moving up: Now you have a trained model. Let's learn how to talk to it and get useful results!

Rung 6 – Having a Conversation: Interacting with AI Systems

Your model is trained and ready. But like any conversation, how you ask matters as much as what you ask. Let's master the art of AI communication.

Term	What It Really Means	A Communication Analogy
Prompt	The instruction, question, or information you give to an AI model as its input.	It's the starting line of a conversation. A clear, well-phrased question to a friend will get a much better answer than a vague, confusing one.
Prompt Engineering	The skill of carefully crafting prompts to get the best possible responses from an AI model.	This is like learning how to be a great interviewer. You learn to ask questions in a way that encourages detailed, helpful, and accurate answers.
Inference	The process of a trained model using its knowledge to generate a response to your prompt. No new learning happens during inference.	This is like asking an expert for advice. They use their existing knowledge to give you an answer, but your question doesn't change their brain or teach them anything new. Their weights are "frozen."
Temperature	A setting that controls how creative or predictable the AI's responses are. Low is safe; high is creative.	Think of it as a "risk" knob. A low temperature (e.g., 0.2) makes the model play it safe and choose the most obvious next word. A high temperature (e.g., 1.0) encourages it to take creative risks and use less common words.
Hallucination	When an AI confidently states something that is false, nonsensical, or completely made up.	It's like a person who is very confident but completely wrong. Because LLMs are designed to generate plausible-sounding text, they can sometimes invent facts that sound true but aren't.

Moving up: One major limitation is that models only know what they learned during training. Let's fix that by connecting them to current information!

Rung 7 – Keeping AI Current: Connecting to Real-World Information

How do we help AI access up-to-the-minute information and ground its answers in facts, rather than just relying on patterns from its training data?

Term	What It Really Means	A Real-World Parallel
Knowledge Cutoff	The date when the model's training data ended. It knows nothing about events that happened after this point.	It's like a history textbook printed in 2023. It can't tell you who won the 2024 World Series because that event happened after it was published.
Retrieval	The process of searching for and finding relevant documents or information from an external source to help answer a question.	This is like a librarian finding the right books and articles to help you research a topic, giving you information that goes beyond what you already know.
Vector Database	A special database designed to store embeddings and perform incredibly fast similarity searches.	It's like a magical library where books are organized by meaning, not just alphabetically. If you ask for a book about "royal rulers," it can instantly find books about "kings," "queens," and "monarchs."
RAG (Retrieval-Augmented Generation)	A three-step process: (1) Retrieve relevant info, (2) Add it to the user's prompt, then (3) Generate an answer based on that info.	It's like an open-book exam for the AI. First, it looks up the relevant facts in the textbook (retrieval), then it uses those facts to write the essay answer (generation). This drastically reduces hallucinations.
Grounded AI	An AI system that is instructed to base its answers only on the provided source documents, not its general training.	This is like a lawyer in a courtroom who can only argue based on the evidence presented, not on their own outside knowledge or opinions.
Live Web Access	The ability for an AI system to search the internet in real-time for the most current information.	This gives the AI a research assistant who can look up breaking news, stock prices, or today's weather while it's talking to you.

Moving up: Getting good information is just the first step. Let's explore how AI can think through complex problems and take real actions!

Rung 8 – Thinking and Acting: Advanced Reasoning and Real-World Actions

How do we create AI systems that don't just give quick answers, but can actually think through problems step-by-step and perform tasks beyond just generating text?

Term	What It Really Means	How It's Like Human Problem-Solving
Chain-of-Thought (CoT)	Prompting a model to explain its reasoning step-by-step before giving the final answer.	It's like asking a student to "show their work" on a math problem. The process of explaining the steps often leads to a more accurate final answer.
Tree of Thoughts (ToT)	Allowing the model to explore multiple different reasoning paths (like branches on a tree) and then choose the best one.	This is like brainstorming. You think of several possible ways to tackle a problem before committing to the one that seems most promising.
Agent	An AI system that can take real actions to achieve a goal, not just generate text. It can use tools, make plans, and execute tasks.	This is the difference between an advisor who tells you how to book a flight and a travel agent who actually books it for you.
Tool Use	An agent's ability to choose and use external software tools—like a calculator, a search engine, or an API—to solve a problem.	It's like a carpenter knowing when to use a hammer, a saw, or a drill. The agent learns to pick the right tool for the job at hand.
Autonomous Agent	An advanced agent that can break down a complex goal into sub-tasks and work independently with minimal human oversight.	This is like hiring a project manager who can take a high-level goal (e.g., "launch our new product") and manage all the smaller steps to get it done.

Moving up: All this capability needs to work reliably in the real world. Let's learn how AI systems are deployed and managed!

Rung 9 – From Lab to Life: Deploying AI in the Real World

Building a great AI model is only half the battle. How do you make it available to millions of users reliably, safely, and efficiently?

Term	What It Really Means	A Real-World Analogy
Pipeline	The complete, automated workflow from collecting data to deploying a working AI system.	It's like an assembly line in a factory. Each station performs its part automatically to create, test, and ship the final product without manual intervention.
API (Application Programming Interface)	A standardized way for different software programs to communicate with your AI model.	Think of it as a universal electrical outlet. Any compatible device can plug in and get power, without needing a custom connection. An API lets any authorized app "plug into" your AI.
Deployment	The process of moving your model from a development environment to a "production" system where real users can access it.	This is like the grand opening of a restaurant. After months of testing recipes in a private kitchen, you finally open the doors to the public.
Scaling	Ensuring your system can handle growth, working just as well for 10 million users as it does for 10 users.	It's like having a recipe that works for a small dinner party but can also be adapted to feed an entire stadium without a drop in quality.
Monitoring	Continuously tracking your AI system's performance, accuracy, and health after it has been deployed.	This is like a pilot watching the instrument panels during a flight. You need to constantly check for any signs of trouble to catch problems before they become disasters.
MVP (Minimum Viable Product)	The simplest version of a product that still provides real value to users, released to test an idea quickly.	It's like starting with a food truck to test your recipes and see if people like them, before you invest millions in building a full-scale restaurant.

Moving up: With great power comes great responsibility. Let's explore how to keep AI systems safe, fair, and beneficial for everyone.

Rung 10 – AI Safety and Ethics: Building Technology We Can Trust

As AI becomes more powerful, ensuring it helps rather than harms is the most important challenge. This is about building AI that respects human values and rights.

Term	What It Really Means	Why This Is Like Other Safety Measures
Alignment	The challenge of ensuring an AI's goals are truly in line with human values and intentions, not just the literal instructions we give it.	It's like making sure a genie grants your wish the way you intended, not in a twisted, literal way that leads to disaster.
Guardrails	Built-in safety rules that prevent an AI from generating harmful, illegal, or inappropriate outputs.	These are like the safety rails on a highway. They are there to keep you from accidentally driving off a cliff, even if you make a mistake.
Red Teaming	The practice of hiring experts to deliberately try to break an AI's safety measures to find weaknesses.	This is like a bank hiring ethical hackers to try to break into their own vault. They want to find any security holes before real criminals do.
Explainability (XAI)	The goal of making AI decisions understandable to humans. We want to know why the model gave a certain answer.	It's like requiring a judge to explain the reasoning behind their verdict. For high-stakes decisions in medicine or finance, we need to understand the "why."
Fairness	The goal of ensuring an AI model doesn't discriminate or create unfair outcomes for different groups of people.	It's like making sure a standardized test isn't biased in a way that gives one group an unfair advantage over another. AI can inherit and even amplify biases from its training data.
Privacy	Protecting personal and sensitive data that is used to train or interact with AI systems.	This is like doctor-patient confidentiality. As AI handles more of our personal information, protecting that information becomes absolutely critical.

Final climb: Let's explore the tools and organizations shaping the AI landscape today!

Rung 11 – The AI Ecosystem: Key Players, Tools, and Platforms (as of mid-2025)

Who's building the AI future, and what tools are they using? Here's your guide to the major players and platforms in the AI world.

Name / Platform	What They Do	Why They Matter in 2025
TensorFlow & PyTorch	The two dominant open-source frameworks (from Google and Meta, respectively) used by developers to build neural networks.	They are the foundational "toolkits" for AI. Nearly every model discussed in this guide is built using one of these two frameworks.
Hugging Face	A platform often called "the GitHub for AI," hosting thousands of pre-trained models, datasets, and tools.	It democratizes AI by making powerful models freely available, allowing developers to fine-tune state-of-the-art AI without starting from scratch.
OpenAI	The research and deployment company behind the GPT models (ChatGPT) and image generator DALL-E.	A key driver of the generative AI boom. In 2025, the company is heavily focused on rolling out advanced agent capabilities, allowing its models to execute complex, multi-step tasks autonomously.
Google AI (DeepMind, Gemini)	Google's AI research divisions and its family of models, Gemini, which are integrated into Google Search and other products.	A major innovator in LLMs and reinforcement learning. Google continues to compete directly with OpenAI, building its own powerful agentic systems and multimodal AI.
Anthropic	An AI safety-focused company and creator of the Claude family of models.	Known for its strong emphasis on AI safety and alignment. In 2025, Claude models feature advanced "computer use" capabilities, allowing the AI to interact with software, click buttons, and browse the web to complete tasks.
Microsoft Copilot	Microsoft's brand for AI agents integrated across its products like Windows, Office 365, and Azure.	A leader in enterprise AI. In 2025, Copilot Studio allows businesses to build and orchestrate multiple agents that can delegate tasks to one another, automating complex business workflows.
Salesforce Agentforce	An enterprise AI agent platform deeply integrated into Salesforce's CRM products.	Purpose-built for business automation. After launching in late 2024, Salesforce has rapidly released new versions in 2025 to improve agent visibility, control, and integration with other enterprise tools.
CrewAI & LangGraph	Popular open-source frameworks that help developers build complex, multi-agent systems.	These tools provide the structure for creating sophisticated applications where multiple specialized agents can collaborate to solve a problem, a major trend in 2025.
AI Agent Market	The overall market for AI agent technology.	The market was valued at over $5 billion in 2024 and is projected to grow at a rate of over 45% annually through 2030, highlighting the massive investment and focus on building autonomous AI systems.

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Comprehensive AI Terminology Guide

This section provides a detailed exploration of AI concepts to equip you with the knowledge needed to understand technical discussions about large language models (LLMs) and their applications, such as those on X or in academic papers. The concepts are organized into categories for clarity, covering model architecture, training, inference, evaluation, applications, and ethical considerations. Each category includes a table with concepts, descriptions, use cases, and examples, ensuring a thorough understanding of terms like “parameters,” “fine-tuning,” “BFCL,” and “LAMs.”

Model Architecture Concepts

The architecture of an AI model defines its structure and how it processes data. These concepts are fundamental to understanding how models like xLAM or GPT-3 are built.

Concept	Description	General Use Case	Examples
Parameters	Number of trainable weights in a model, indicating its size and capacity. Larger models often have better performance but require significant computational resources.	Determines model complexity and deployment feasibility.	GPT-3: 175B (one of the largest), LLaMa-3: 70B, xLAM-1b (smallest for efficiency).
Layers	Depth of the model, measured by the number of transformer layers, which process data sequentially.	Deeper layers enable capturing hierarchical patterns in data.	BERT: 12 layers (smaller), GPT-3: 96 layers (deep).
Attention Mechanisms	Mechanisms that allow models to weigh the importance of different input parts, crucial for understanding context.	Processes long sequences in NLP tasks effectively.	Self-attention in transformers, used in BERT, GPT, T5.
Transformer	A neural network architecture with encoder and/or decoder blocks, forming the backbone of modern LLMs.	Powers tasks like text generation and translation.	GPT (decoder-only), BERT (encoder-only), T5 (both).
Mixture-of-Experts (MoE)	Architecture using multiple specialized sub-models, activating only a subset for each task to improve efficiency.	Enables scalable, high-performance models with lower compute.	xLAM-8x22b, Mixtral by Mistral AI.
Large Action Models (LAMs)	Models designed for executing actions, such as interacting with tools or APIs, rather than just generating text.	Automates complex workflows, like booking or data retrieval.	xLAM models, watt-tool-70B for tool-use tasks.
Residual Connections	Skip connections that allow gradients to flow directly, aiding training of deep networks.	Prevents vanishing gradients in deep models.	Standard in transformers like GPT, BERT.
Positional Encoding	Adds information about token positions in sequences, enabling models to understand word order.	Critical for sequence-based tasks like NLP.	Sinusoidal encoding in original transformers.
Embeddings	Dense vector representations capturing semantic meaning of words or tokens.	Used in NLP for tasks like similarity detection.	Word2Vec, GloVe, BERT contextual embeddings.
Tokenization	Process of splitting text into tokens (e.g., words or subwords) for model input.	Prepares text for processing by LLMs.	Byte-Pair Encoding (GPT), WordPiece (BERT).

Training Concepts

Training involves preparing a model to perform tasks by learning from data. These concepts explain how models are developed and optimized.

Concept	Description	General Use Case	Examples
Pre-training	Training a model on a large, diverse dataset to learn general language or task patterns, often unsupervised.	Provides a versatile base for downstream tasks.	BERT on Wikipedia and BooksCorpus, GPT on web text.
Fine-tuning	Adapting a pre-trained model with task-specific data to improve performance on a targeted application.	Enhances model accuracy for specific use cases.	Fine-tuning GPT for chatbots, xLAM for function-calling.
Dataset Synthesis	Generating artificial data to augment training datasets, especially when real data is limited.	Enables training for niche tasks like tool-use.	Synthetic data for xLAM tool-use, OpenMathReasoning math problems.
Data Augmentation	Techniques to increase data diversity (e.g., paraphrasing text, rotating images) without collecting new samples.	Improves model robustness and generalization.	Back-translation for translation models, image flips in vision.
Supervised Learning	Training with labeled data where inputs are paired with correct outputs.	Common for classification or regression tasks.	Image classification with labeled images, NER with tagged text.
Unsupervised Learning	Training without labeled data to discover patterns, often used in pre-training.	Learns representations from raw data.	Masked language modeling in BERT, clustering in embeddings.
Reinforcement Learning	Training through rewards and penalties to optimize decision-making in dynamic environments.	Used for tasks requiring sequential decisions.	RLHF in ChatGPT, AlphaGo for game playing.
Transfer Learning	Applying knowledge learned from one task to improve performance on a related task.	Reduces training time for new tasks.	Using BERT for sentiment analysis, ImageNet for medical imaging.
Overfitting	When a model learns training data too well, including noise, and performs poorly on new data.	Avoided to ensure models generalize to unseen data.	Regularization techniques like dropout prevent this.
Regularization	Methods like weight penalties or dropout to prevent overfitting by constraining model complexity.	Ensures models perform well on test data.	L1/L2 regularization, dropout in neural networks.
Hyperparameters	Settings like learning rate or batch size that control the training process, tuned before training.	Optimizes training efficiency and model performance.	Learning rate of 0.001, batch size of 32.
Learning Rate	Step size for updating model weights during training, balancing speed and stability.	Affects convergence and training quality.	Adam optimizer with adaptive learning rates.
Optimizer	Algorithm to update model weights by minimizing the loss function, like Adam or SGD.	Drives efficient training of neural networks.	Adam in most LLMs, SGD in simpler models.
Gradient Descent	Iterative process to minimize the loss function by updating weights in the direction of the gradient.	Core mechanism for training neural networks.	Batch gradient descent, stochastic gradient descent.
Loss Function	Measures the difference between predicted and actual outputs, guiding model optimization.	Defines the training objective.	Cross-entropy for classification, MSE for regression.

Inference Concepts

Inference is the process of using a trained model to generate outputs. These terms cover how models are deployed and optimized for real-world use.

Concept	Description	General Use Case	Examples
Inference	Running a trained model to produce predictions or outputs based on new inputs.	Powers applications like chatbots or image recognition.	Generating text with GPT, classifying images with ResNet.
Quantization	Reducing the precision of model weights (e.g., from 32-bit to 8-bit) to lower memory and compute needs.	Enables deployment on edge devices or faster inference.	INT8 quantization for LLMs, used in mobile AI apps.
Distillation	Training a smaller “student” model to replicate a larger “teacher” model’s behavior.	Creates lightweight models for resource-constrained environments.	DistilBERT (from BERT), TinyML models.
Latency	Time taken for a model to process an input and produce an output.	Critical for real-time applications like voice assistants.	Sub-second response times in chatbots.
Throughput	Number of inputs a model can process per unit time, measuring system efficiency.	Important for high-traffic services like web APIs.	100 requests/second in cloud-based LLMs.
Beam Search	A decoding strategy that explores multiple sequence paths to generate high-quality text.	Improves coherence in text generation tasks.	Used in machine translation, summarization with T5.
Top-k Sampling	Selecting from the top k most probable tokens during text generation to balance creativity and accuracy.	Generates diverse yet coherent text outputs.	Used in GPT-3, LLaMa for creative writing.
Batch Size	Number of inputs processed simultaneously during inference, affecting speed and memory.	Optimizes resource use in deployment.	Batch size of 32 for text generation in production.
ONNX	Open Neural Network Exchange, a format for representing models to enable cross-framework use.	Allows models to run on different platforms.	Converting PyTorch models to ONNX for deployment.
TensorRT	NVIDIA library for optimizing inference on GPUs, reducing latency and increasing throughput.	Accelerates inference for real-time applications.	Faster LLM inference on NVIDIA hardware.

Evaluation Concepts

Evaluation measures how well models perform. These terms include benchmarks and metrics used to compare models like xLAM or watt-tool-70B.

Concept	Description	General Use Case	Examples
Benchmarks	Standardized datasets or tasks to evaluate model performance across consistent conditions.	Enables fair comparison of models.	GLUE, SuperGLUE, MMLU, GSM8K for math.
Leaderboards	Public rankings of model performance on specific benchmarks, tracking state-of-the-art.	Highlights top-performing models in the field.	BFCL, Hugging Face Open LLM Leaderboard.
BFCL	Berkeley Function-Calling Leaderboard, assessing models’ ability to invoke functions correctly.	Evaluates tool-use and function-calling skills.	xLAM-2-70b-fc-r, watt-tool-70B lead BFCL.
τ-bench	A benchmark for evaluating agentic tool-use in multi-turn, real-world-like tasks.	Tests complex agent interactions and planning.	xLAM-2 outperforms GPT-4o on τ-bench.
AIMO	AI Mathematical Olympiad, a competition for models solving advanced math problems.	Assesses mathematical reasoning capabilities.	OpenMathReasoning excels in AIMO-2 challenges.
Accuracy	Proportion of correct predictions, a basic metric for classification tasks.	Measures model correctness in straightforward tasks.	95% accuracy on image classification test sets.
F1 Score	Harmonic mean of precision and recall, useful for imbalanced datasets.	Evaluates performance in tasks like NER or sentiment analysis.	F1 score in named entity recognition tasks.
Perplexity	Measures how well a language model predicts a text sample; lower is better.	Assesses language model quality in generation tasks.	Perplexity of 20 on held-out text data.
Human Evaluation	Using human judges to assess model outputs, often for subjective quality.	Validates outputs in tasks like dialogue or creativity.	Evaluating chatbot coherence or translation quality.
Cross-Validation	Splitting data into training and validation sets to estimate model generalization.	Ensures robust performance across data splits.	5-fold cross-validation in machine learning.
Hyperparameter Tuning	Adjusting settings like learning rate to optimize model performance.	Improves model accuracy and training efficiency.	Grid search for optimal learning rate in LLMs.
BLEU Score	Metric for evaluating machine translation by comparing generated text to references.	Measures translation quality in NLP tasks.	BLEU score for Google Translate outputs.

Application Concepts

Applications show what AI models can achieve in real-world scenarios, from tool-use to reasoning.

Concept	Description	General Use Case	Examples
Tool-use	Ability to interact with external tools or APIs to perform tasks.	Automates workflows like data retrieval or calculations.	xLAM calling APIs, watt-tool-70B for tool tasks.
Function Calling	Invoking predefined functions based on user input, a subset of tool-use.	Enables structured interactions with software systems.	xLAM-2, watt-tool-70B for function-calling tasks.
Multi-turn Conversation	Maintaining context and coherence over multiple dialogue exchanges.	Powers interactive chatbots and virtual assistants.	ChatGPT, Grok, customer service bots.
Reasoning	Performing logical deductions or solving problems, often in math or logic.	Solves complex tasks requiring step-by-step thinking.	OpenMathReasoning for math, DeepMind’s AlphaCode.
Code Generation	Writing code based on natural language descriptions or prompts.	Assists developers, automates coding tasks.	GitHub Copilot, CodeLLaMa, xLAM for scripts.
Machine Translation	Translating text from one language to another automatically.	Facilitates cross-lingual communication and content access.	Google Translate, DeepL, T5 for translation.
Summarization	Condensing long texts into concise summaries while retaining key points.	Generates news digests, research abstracts, or reports.	BART, T5, Pegasus for text summarization.
Question Answering	Providing accurate answers to user questions, often from a context or knowledge base.	Powers search engines, virtual assistants, and FAQs.	BERT on SQuAD, GPT-4 for open-domain QA.
Sentiment Analysis	Determining the emotional tone (e.g., positive, negative) in text data.	Analyzes customer feedback, social media, or reviews.	VADER, BERT-based sentiment classifiers.
Named Entity Recognition (NER)	Identifying and classifying entities like names, organizations, or locations in text.	Extracts structured information from unstructured text.	SpaCy, BERT for NER tasks in NLP pipelines.

Ethical Considerations

Ethical considerations ensure AI is developed and used responsibly, addressing societal impacts.

Concept	Description	General Use Case	Examples
Bias	Unfair prejudices in model outputs, often from biased training data.	Can lead to discriminatory outcomes in hiring or policing.	Gender bias in language models, racial bias in facial recognition.
Fairness	Ensuring models treat all groups equitably, avoiding discrimination.	Critical for applications like loan approvals or hiring.	Fair algorithms in credit scoring, equitable AI frameworks.
Transparency	Making model decisions and processes understandable to users.	Builds trust and enables auditing of AI systems.	Explainable AI techniques, model cards on Hugging Face.
Accountability	Holding developers and organizations responsible for AI behavior.	Ensures ethical deployment and compliance with regulations.	GDPR compliance, AI ethics boards in companies.
Privacy	Protecting user data during training and inference to prevent leaks.	Maintains user trust in AI applications like health or finance.	Differential privacy in training, federated learning.

============

To provide a comprehensive comparison that helps you understand discussions about new AI models and their inferences, especially in the context of posts on X, I’ve created a detailed table comparing the four provided models/collections: xLAM-2, xLAM Models, watt-tool-70B, and OpenMathReasoning. The table covers key attributes like dataset, parameters, model details, and specific terms like τ-bench, BFCL, LAM, and AIMO, ensuring you can follow technical discussions about model capabilities, inference, and performance.

Attribute
Purpose
Model
Parameters
LLM Base
Dataset
Dataset Synthesis
Multi-Turn Conversation
Tool Use
Function Calling
Inference
Optimization
State-of-the-Art
τ-bench
Similar Benchmarks
LAM (Large Action Model)
Reasoning
AIMO
BFCL (Berkeley Function-Calling Leaderboard)
Open-Source
Key Features
Use Case Example
Limitations
Recent Updates

===========

1. Key Concepts in AI Models and Usage

Concept	Definition	Notes
Token	Roughly a word-piece (about ¾ of a word on average)	"computer" is one token, "fantastic" is one, but "fantas-tic" might split into two
Context Window	Maximum number of tokens the model can read at once	Input + output tokens must fit within this window
Input Tokens	Tokens sent to the model when asking a question	Counts toward token usage
Output Tokens	Tokens the model returns as its answer	Counts toward token usage
Quantization	Technique to reduce model size (e.g., 4-bit)	Reduces RAM and CPU demands for local inference
Multi-modal	Model can process more than one type of data	Includes text, images, audio, video
Agent Mode	AI can autonomously plan and perform multi-step tasks	Often seen in coding assistants
Open Source Model	Model weights and architecture are publicly available	Allows for customization and local deployment
Proprietary Model	Model details are kept confidential by the developer	Accessed via API or dedicated platforms

==============

https://help.kagi.com/kagi/ai/llm-benchmark.html

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score

=======

1 token ≈ 3.5 characters average in English. 1 million tokens is approximately equivalent to: 30 hours of a podcast ( ~150 words per minute), 1,000 pages of a book (~500 words per page), 60,000 lines of code (~60 characters per line)

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time to first token, generation time

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image gen

https://pollinations.ai/p/an apple?height=512&width=512&model=flux
https://pollinations.ai/p/an orange?height=512&width=512&model=flux

LLM API with the lowest cost per million tokens - Gemini 1.5 Flash: Input cost is $0.075 per million tokens up to 128K, and $0.15 for longer than 128K inputs. [1, 2] - OpenAI o4-mini: Input cost is $1.10 per million tokens. [3, 4] - OpenAI gpt-3.5-turbo-0125: Input cost is $0.005 per million tokens. [5] - OpenAI gpt-4: Input cost is $0.03 per million tokens. [5] - Anthropic Claude 3.5 Sonnet: Input cost is $3.00 per million tokens. [2] - OpenAI gpt-4o: Input cost is $5.00 per million tokens. [5] - OpenAI gpt-4-turbo: Input cost is $10.00 per million tokens. [5]

cot

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score
o3 Y 76.29 502 2.57191 6056 12 29 15
claude-3-7-extended-thinking Y 71.34 847 2.20567 81931 96 32 8
gemini-2-5-pro Y 68.72 381 0.257 9905 25 267 18
qwen-qwq-32b Y 65.94 763 0.11994 340400 446 553 8
o1 Y 65.44 502 6.55213 3678 7 9 13
o3-mini Y 65.16 502 0.52675 10333 20 123 12
deepseek-r1 Y 64.06 301 1.16229 101071 335 55 21
o4-mini Y 62.27 502 0.41746 4253 8 149 12
deepseek-r1-distill-llama-70b Y 54.41 381 0.40643 91634 240 133 14
o1-pro Y 44.38 502 59.5752 2628 5 0 8
claude-3-7-sonnet Y 42.94 301 0.30431 10852 36 141 14

accuracy

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score
o3 Y 76.29 502 2.57191 6056 12 29 15
claude-3-7-extended-thinking Y 71.34 847 2.20567 81931 96 32 8
gemini-2-5-pro Y 68.72 381 0.257 9905 25 267 18
qwen-qwq-32b Y 65.94 763 0.11994 340400 446 553 8
o1 Y 65.44 502 6.55213 3678 7 9 13
o3-mini Y 65.16 502 0.52675 10333 20 123 12
deepseek-r1 Y 64.06 301 1.16229 101071 335 55 21
o4-mini Y 62.27 502 0.41746 4253 8 149 12

Meta-Llama-3.1-405B-Instruct

code

Model Percent completed correctly Percent using correct edit format Command Edit format
o1 84.2% 99.2% aider --model openrouter/openai/o1 diff
claude-3-5-sonnet-20241022 84.2% 99.2% aider --model anthropic/claude-3-5-sonnet-20241022 diff
gemini-exp-1206 (whole) 80.5% 100.0% aider --model gemini/gemini-exp-1206 whole
o1-preview 79.7% 93.2% aider --model o1-preview diff

WebDev Leaderboard

1	Claude 3.7 Sonnet (20250219)	1356.70	+7.95 / -7.08	7,481	Anthropic	Proprietary

2	GPT-4.1-2025-04-14				1283.42	+23.61 / -13.07	1,250	OpenAI	Proprietary

2	Gemini-2.5-Pro-Exp-03-25		1275.55	+8.64 / -6.33	7,836	Google	Proprietary

4	Claude 3.5 Sonnet (20241022)	1239.33	+4.77 / -3.45	25,309	Anthropic	Proprietary

5	DeepSeek-V3-0324				1207.01	+17.32 / -19.14	1,097	DeepSeek	MIT

Intelligence Index incorporates 7 evaluations: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME, MATH-500

Reasoning Model

```
o4-mini (high)
```
```
Gemini 2.5 Pro
```
```
o3
```
```
Grok 3 miniReasoning(high)
```
```
	Llama 3.1Nemotron Ultra253B Reasoning
```
```
Gemini 2.5Flash(Reasoning)
```
```
	DeepSeek R1
```
```
Claude 3.7SonnetThinking
```

Non-Reasoning Model

```
	DeepSeek V3(Mar' 25)
```
```
GPT-4.1 mini
```
```
GPT-4.1
```
```
Grok 3
```
```
	Llama 4Maverick
```
```
	Llama 4 Scout
```
```
GPT-4o (Nov'24)
```
```
Mistral Large2 (Nov '24)
```
```
	Gemma 3 27B
```
```
Nova Pro
```

Cheapest API Provider : Llama 3.3 70B Input Cost

```
1			Lambda			`$`0.20 / 1M tokens
```
```
2			DeepInfra			`$`0.23 / 1M tokens
```
```
3			Hyperbolic			`$`0.40 / 1M tokens
```

Best LLM - Code : HumanEval benchmark

```
1	Claude 3.5 Sonnet	93.7
```
```
2	Qwen2.5-Coder 32B Instruct	92.7
```
```
3	o1-mini	92.4
```

Benchmarks Leaderboards about code, reasoning and general knowledge

MMLU Leaderboard Knowledge and reasoning across science, math, and humanities.
MMLU-Pro Leaderboard Advanced version of MMLU with more complex reasoning tests.
GPQA Leaderboard 448 "Google-proof" questions in biology, physics, and chemistry.
HumanEval Leaderboard Performance on Python coding tasks like sorting, searching, etc.
DROP Leaderboard Reading comprehension with reasoning over paragraphs.
MATH Leaderboard Performance on high school mathematics problems.

======

Embeddings & Models Platform

======

AI Common Terms

0:00

The AI Learning Ladder: Your Step-by-Step Guide to Understanding Artificial Intelligence

==============

grounding - citing sources search - retrieving info from the web

==============

Rung 0 – The Foundation: Three Essential Building Blocks

Before we dive into AI, let's establish three fundamental concepts. Everything else in AI builds on these, so let's make sure we're crystal clear on what they mean.

Term	What It Really Means (in Simple Terms)	A Real-World Example
Data	Any information a computer can use. This includes text, photos, numbers in a spreadsheet, or even your voice.	The photos on your phone are data. The words in this sentence are data. The songs in your music library are data.
Algorithm	A precise set of instructions that tells a computer exactly what to do, step-by-step.	A recipe for baking cookies is an algorithm. It has a list of steps that must be followed in a specific order to get the right result.
Artificial Intelligence (AI)	A computer system that can perform tasks we normally think require human intelligence.	Your phone recognizing your face to unlock, Netflix recommending shows you might like, or a smart assistant understanding your questions.

Ready to climb? Now that we have our three core ingredients, let's see what happens when we combine them to create something that can actually learn.

Rung 1 – From Ingredients to Intelligence: How AI Actually Learns

Here's where it gets exciting. We're going to take our building blocks from Rung 0 and see how they work together to create systems that can learn and make predictions.

Term	What It Really Means (and How It Connects)	An Everyday Analogy
Model	The end result after an algorithm has finished learning from data. It's like a "brain" that has been trained and can now make decisions or predictions.	Think of a chef who has studied hundreds of recipes (data). The chef's knowledge and intuition is now the model—they can create new dishes without a recipe book.
Training	The learning process where we show the algorithm thousands or millions of examples so it can find patterns and improve.	It's like teaching a child to recognize animals by showing them many pictures: "This is a dog, this is a cat, this is a dog..." Eventually, they learn to tell them apart on their own.
Input / Output	Input is what you give to the model (like a question or a photo). Output is what the model gives back (like an answer or a label).	Input: You ask your smart speaker, "What's the weather today?" Output: The speaker replies, "It's sunny with a high of 75 degrees."
Weight (or Parameter)	A single adjustable number inside the model. Millions of these numbers work together to store everything the model has learned.	Think of them as the individual knobs on a giant sound mixing board. During training, the algorithm carefully adjusts each knob to get the perfect sound (output).
Loss Function	A mathematical score that measures how wrong the model's answers are during training. A lower score means better answers.	It's like a teacher grading a test. The loss function counts how many questions the model got wrong. The goal of training is to get the lowest score possible.
Gradient Descent	The clever mathematical technique that figures out exactly how to adjust each weight to reduce the loss function's score.	It's like adjusting the hot and cold water knobs in a shower. You make small, smart adjustments until the temperature (output) is just right.
Epoch	One complete pass where the model has seen all the training data from start to finish.	It's like reading an entire textbook once from cover to cover. Most training involves many epochs, so the model reviews the material multiple times to learn it well.
Batch	A small group of training examples that are processed together before the model's weights are updated.	Instead of studying one flashcard at a time, you review a small stack of 10-20 cards, then pause to let the information sink in. This makes training more efficient.

Moving up: Now you understand the mechanics of how AI learns. But just as there are different ways to teach people, there are different strategies for training AI. Let's explore them!

Rung 2 – Teaching Strategies: Different Ways AI Can Learn

Just as people learn differently—some from textbooks, others from experience—AI systems have different learning approaches depending on the goal.

Term	What It Really Means	A Real-Life Learning Parallel
Supervised Learning	Teaching an AI with a complete answer key. Every piece of training data is labeled with the correct answer, so the model learns by comparing its guesses to the truth.	This is like studying with flashcards that have the question on the front and the answer on the back. You guess, flip the card, and immediately see if you were right.
Unsupervised Learning	Letting the AI find patterns on its own without being told what's right or wrong. The data has no labels or correct answers.	It's like giving someone a huge box of mixed LEGO bricks and asking them to sort them. They might group them by color, size, or shape, finding patterns without being told which way is "correct."
Reinforcement Learning	Teaching an AI through rewards and penalties. The model (called an "agent") learns from the consequences of its actions.	This is exactly like training a dog. You give it a treat (reward) for sitting, but say "No!" (penalty) for jumping on the couch. Over time, the dog learns which behaviors lead to rewards.
Overfitting	When your model memorizes the training data instead of learning the general patterns. It does great on examples it's seen before but fails on new, unseen data.	Imagine a student who memorizes the answers to last year's exam. They'll ace those exact questions but will fail the real test if the questions are slightly different.
Underfitting	When your model is too simple to capture the important patterns in your data. It fails to learn, even with lots of training.	This is like trying to summarize a complex movie with only one sentence. No matter how you phrase it, you'll miss all the important details.
Regularization	A collection of techniques used during training to prevent overfitting. It forces the model to learn simpler, more general patterns.	It's like a teacher telling students they can only use a single, small index card for notes during an exam. It forces them to truly understand the concepts instead of just copying the book.
Dropout	A specific regularization technique where parts of the model are randomly ignored or "turned off" during each step of training.	This is like practicing a team sport with a few players randomly sitting out for each play. It forces the other players to learn how to work together in different ways and not rely on just one star player.

Moving up: Now let's explore the specific architecture that revolutionized AI—neural networks, the technology inspired by the human brain!

Rung 3 – Building Electronic Brains: Understanding Neural Networks

This is where AI gets its "neural" inspiration. While much simpler than biological brains, these networks have proven incredibly powerful for learning complex patterns.

Term	What It Really Means	How It's Like a Brain (Loosely!)
Neural Network	A network of simple computing units (called "neurons") connected in layers. Each connection has an adjustable weight that gets tuned during training.	It's like a massive telephone switchboard. Operators (neurons) receive calls (inputs), process them, and route them to other operators in the next layer.
Deep Learning	The use of neural networks with many layers (typically 3 or more, but modern ones can have hundreds).	"Deep" just means the network has many layers. More layers allow the model to learn more complex and abstract patterns from the data, like identifying a face instead of just lines and shapes.
Backpropagation	The technique for teaching neural networks by sending error signals backward through the network, from the final output to the first input.	It's like a game of telephone in reverse. If the final message is wrong, you trace it backward, asking each person what they heard, to find out where the mistake happened and correct it for next time.

Moving up: Neural networks were powerful, but the real revolution came with a specific design for understanding language. Let's explore the breakthrough that gave us today's conversational AI!

Rung 4 – The Language Revolution: How AI Learned to Chat

This is where AI made the leap from recognizing images to having conversations. These innovations led to ChatGPT, Claude, and other modern AI systems.

Term	What It Really Means	An Everyday Comparison
Token	A chunk of text that the model processes as one unit—usually a word or part of a word.	Think of breaking a sentence into Scrabble tiles. Each tile (token) is a single piece that the game (model) can work with.
Context Window	The maximum amount of text (measured in tokens) that a model can "remember" and consider at one time.	It's like your short-term memory when reading a book. You can remember what happened in the current chapter, but you might have forgotten a minor detail from 200 pages ago.
Embedding	The process of converting a token into a list of numbers that captures its meaning and relationships to other words.	It's like giving every word its own unique GPS coordinate. Words with similar meanings (like "king" and "queen") will have coordinates that are close to each other.
Vector	The actual list of numbers that represents a token's meaning (its "GPS coordinate").	This is the numerical input that a neural network can actually process. The model learns to do math on these vectors to understand language.
Transformer	A powerful neural network design that is exceptionally good at understanding context in sequential data like text.	It's like a reader who can instantly see the connections between every word in a paragraph at the same time, rather than just reading one word after another.
Attention Mechanism	The special ability of a transformer to weigh the importance of all other tokens in the context window when processing a single token.	When you read the sentence "The robot picked up the red ball," attention helps the model know that "it" in a later sentence likely refers to the "ball," not the "robot."
Large Language Model (LLM)	A massive transformer model (with billions of weights) that has been trained on enormous amounts of text to predict the next token in a sequence.	It's like a super-powered autocomplete. After reading nearly the entire internet, it has become incredibly good at predicting what word should come next in any given sentence.
Generative AI	AI systems that can create new, original content (like text, images, code, or music) rather than just analyzing existing data.	An artist who can paint a new masterpiece is a generative artist. An AI that can write a new poem or create a unique image is Generative AI.

Moving up: Training these massive models costs millions of dollars. Fortunately, we can reuse that work. Let's see how!

Rung 5 – Standing on Giants' Shoulders: Reusing Existing Models

Why spend millions training a model from scratch when you can start with one that already understands language? This is like learning a new skill faster because you already have related knowledge.

Term	What It Really Means	A Real-World Analogy
Pre-training	The initial, expensive phase where a huge model like an LLM learns general knowledge from a massive, broad dataset.	This is like getting a university degree. It's expensive and time-consuming, but it provides a broad foundation of knowledge that can be applied to many different jobs later on.
Transfer Learning	The general strategy of taking a pre-trained model and adapting it for a new, specific purpose.	It's like hiring an experienced chef who already knows how to cook (pre-trained) and just teaching them your restaurant's specific menu, rather than teaching someone how to boil water.
Fine-tuning	The actual process of continuing to train a pre-trained model, but on your own smaller, specialized dataset.	This is the hands-on training for the experienced chef. You give them your recipes (fine-tuning data) and let them practice until they master your restaurant's style. This is much faster and cheaper than starting from scratch.

Moving up: Now you have a trained model. Let's learn how to talk to it and get useful results!

Rung 6 – Having a Conversation: Interacting with AI Systems

Your model is trained and ready. But like any conversation, how you ask matters as much as what you ask. Let's master the art of AI communication.

Term	What It Really Means	A Communication Analogy
Prompt	The instruction, question, or information you give to an AI model as its input.	It's the starting line of a conversation. A clear, well-phrased question to a friend will get a much better answer than a vague, confusing one.
Prompt Engineering	The skill of carefully crafting prompts to get the best possible responses from an AI model.	This is like learning how to be a great interviewer. You learn to ask questions in a way that encourages detailed, helpful, and accurate answers.
Inference	The process of a trained model using its knowledge to generate a response to your prompt. No new learning happens during inference.	This is like asking an expert for advice. They use their existing knowledge to give you an answer, but your question doesn't change their brain or teach them anything new. Their weights are "frozen."
Temperature	A setting that controls how creative or predictable the AI's responses are. Low is safe; high is creative.	Think of it as a "risk" knob. A low temperature (e.g., 0.2) makes the model play it safe and choose the most obvious next word. A high temperature (e.g., 1.0) encourages it to take creative risks and use less common words.
Hallucination	When an AI confidently states something that is false, nonsensical, or completely made up.	It's like a person who is very confident but completely wrong. Because LLMs are designed to generate plausible-sounding text, they can sometimes invent facts that sound true but aren't.

Moving up: One major limitation is that models only know what they learned during training. Let's fix that by connecting them to current information!

Rung 7 – Keeping AI Current: Connecting to Real-World Information

How do we help AI access up-to-the-minute information and ground its answers in facts, rather than just relying on patterns from its training data?

Term	What It Really Means	A Real-World Parallel
Knowledge Cutoff	The date when the model's training data ended. It knows nothing about events that happened after this point.	It's like a history textbook printed in 2023. It can't tell you who won the 2024 World Series because that event happened after it was published.
Retrieval	The process of searching for and finding relevant documents or information from an external source to help answer a question.	This is like a librarian finding the right books and articles to help you research a topic, giving you information that goes beyond what you already know.
Vector Database	A special database designed to store embeddings and perform incredibly fast similarity searches.	It's like a magical library where books are organized by meaning, not just alphabetically. If you ask for a book about "royal rulers," it can instantly find books about "kings," "queens," and "monarchs."
RAG (Retrieval-Augmented Generation)	A three-step process: (1) Retrieve relevant info, (2) Add it to the user's prompt, then (3) Generate an answer based on that info.	It's like an open-book exam for the AI. First, it looks up the relevant facts in the textbook (retrieval), then it uses those facts to write the essay answer (generation). This drastically reduces hallucinations.
Grounded AI	An AI system that is instructed to base its answers only on the provided source documents, not its general training.	This is like a lawyer in a courtroom who can only argue based on the evidence presented, not on their own outside knowledge or opinions.
Live Web Access	The ability for an AI system to search the internet in real-time for the most current information.	This gives the AI a research assistant who can look up breaking news, stock prices, or today's weather while it's talking to you.

Moving up: Getting good information is just the first step. Let's explore how AI can think through complex problems and take real actions!

Rung 8 – Thinking and Acting: Advanced Reasoning and Real-World Actions

How do we create AI systems that don't just give quick answers, but can actually think through problems step-by-step and perform tasks beyond just generating text?

Term	What It Really Means	How It's Like Human Problem-Solving
Chain-of-Thought (CoT)	Prompting a model to explain its reasoning step-by-step before giving the final answer.	It's like asking a student to "show their work" on a math problem. The process of explaining the steps often leads to a more accurate final answer.
Tree of Thoughts (ToT)	Allowing the model to explore multiple different reasoning paths (like branches on a tree) and then choose the best one.	This is like brainstorming. You think of several possible ways to tackle a problem before committing to the one that seems most promising.
Agent	An AI system that can take real actions to achieve a goal, not just generate text. It can use tools, make plans, and execute tasks.	This is the difference between an advisor who tells you how to book a flight and a travel agent who actually books it for you.
Tool Use	An agent's ability to choose and use external software tools—like a calculator, a search engine, or an API—to solve a problem.	It's like a carpenter knowing when to use a hammer, a saw, or a drill. The agent learns to pick the right tool for the job at hand.
Autonomous Agent	An advanced agent that can break down a complex goal into sub-tasks and work independently with minimal human oversight.	This is like hiring a project manager who can take a high-level goal (e.g., "launch our new product") and manage all the smaller steps to get it done.

Moving up: All this capability needs to work reliably in the real world. Let's learn how AI systems are deployed and managed!

Rung 9 – From Lab to Life: Deploying AI in the Real World

Building a great AI model is only half the battle. How do you make it available to millions of users reliably, safely, and efficiently?

Term	What It Really Means	A Real-World Analogy
Pipeline	The complete, automated workflow from collecting data to deploying a working AI system.	It's like an assembly line in a factory. Each station performs its part automatically to create, test, and ship the final product without manual intervention.
API (Application Programming Interface)	A standardized way for different software programs to communicate with your AI model.	Think of it as a universal electrical outlet. Any compatible device can plug in and get power, without needing a custom connection. An API lets any authorized app "plug into" your AI.
Deployment	The process of moving your model from a development environment to a "production" system where real users can access it.	This is like the grand opening of a restaurant. After months of testing recipes in a private kitchen, you finally open the doors to the public.
Scaling	Ensuring your system can handle growth, working just as well for 10 million users as it does for 10 users.	It's like having a recipe that works for a small dinner party but can also be adapted to feed an entire stadium without a drop in quality.
Monitoring	Continuously tracking your AI system's performance, accuracy, and health after it has been deployed.	This is like a pilot watching the instrument panels during a flight. You need to constantly check for any signs of trouble to catch problems before they become disasters.
MVP (Minimum Viable Product)	The simplest version of a product that still provides real value to users, released to test an idea quickly.	It's like starting with a food truck to test your recipes and see if people like them, before you invest millions in building a full-scale restaurant.

Moving up: With great power comes great responsibility. Let's explore how to keep AI systems safe, fair, and beneficial for everyone.

Rung 10 – AI Safety and Ethics: Building Technology We Can Trust

As AI becomes more powerful, ensuring it helps rather than harms is the most important challenge. This is about building AI that respects human values and rights.

Term	What It Really Means	Why This Is Like Other Safety Measures
Alignment	The challenge of ensuring an AI's goals are truly in line with human values and intentions, not just the literal instructions we give it.	It's like making sure a genie grants your wish the way you intended, not in a twisted, literal way that leads to disaster.
Guardrails	Built-in safety rules that prevent an AI from generating harmful, illegal, or inappropriate outputs.	These are like the safety rails on a highway. They are there to keep you from accidentally driving off a cliff, even if you make a mistake.
Red Teaming	The practice of hiring experts to deliberately try to break an AI's safety measures to find weaknesses.	This is like a bank hiring ethical hackers to try to break into their own vault. They want to find any security holes before real criminals do.
Explainability (XAI)	The goal of making AI decisions understandable to humans. We want to know why the model gave a certain answer.	It's like requiring a judge to explain the reasoning behind their verdict. For high-stakes decisions in medicine or finance, we need to understand the "why."
Fairness	The goal of ensuring an AI model doesn't discriminate or create unfair outcomes for different groups of people.	It's like making sure a standardized test isn't biased in a way that gives one group an unfair advantage over another. AI can inherit and even amplify biases from its training data.
Privacy	Protecting personal and sensitive data that is used to train or interact with AI systems.	This is like doctor-patient confidentiality. As AI handles more of our personal information, protecting that information becomes absolutely critical.

Final climb: Let's explore the tools and organizations shaping the AI landscape today!

Rung 11 – The AI Ecosystem: Key Players, Tools, and Platforms (as of mid-2025)

Who's building the AI future, and what tools are they using? Here's your guide to the major players and platforms in the AI world.

Name / Platform	What They Do	Why They Matter in 2025
TensorFlow & PyTorch	The two dominant open-source frameworks (from Google and Meta, respectively) used by developers to build neural networks.	They are the foundational "toolkits" for AI. Nearly every model discussed in this guide is built using one of these two frameworks.
Hugging Face	A platform often called "the GitHub for AI," hosting thousands of pre-trained models, datasets, and tools.	It democratizes AI by making powerful models freely available, allowing developers to fine-tune state-of-the-art AI without starting from scratch.
OpenAI	The research and deployment company behind the GPT models (ChatGPT) and image generator DALL-E.	A key driver of the generative AI boom. In 2025, the company is heavily focused on rolling out advanced agent capabilities, allowing its models to execute complex, multi-step tasks autonomously.
Google AI (DeepMind, Gemini)	Google's AI research divisions and its family of models, Gemini, which are integrated into Google Search and other products.	A major innovator in LLMs and reinforcement learning. Google continues to compete directly with OpenAI, building its own powerful agentic systems and multimodal AI.
Anthropic	An AI safety-focused company and creator of the Claude family of models.	Known for its strong emphasis on AI safety and alignment. In 2025, Claude models feature advanced "computer use" capabilities, allowing the AI to interact with software, click buttons, and browse the web to complete tasks.
Microsoft Copilot	Microsoft's brand for AI agents integrated across its products like Windows, Office 365, and Azure.	A leader in enterprise AI. In 2025, Copilot Studio allows businesses to build and orchestrate multiple agents that can delegate tasks to one another, automating complex business workflows.
Salesforce Agentforce	An enterprise AI agent platform deeply integrated into Salesforce's CRM products.	Purpose-built for business automation. After launching in late 2024, Salesforce has rapidly released new versions in 2025 to improve agent visibility, control, and integration with other enterprise tools.
CrewAI & LangGraph	Popular open-source frameworks that help developers build complex, multi-agent systems.	These tools provide the structure for creating sophisticated applications where multiple specialized agents can collaborate to solve a problem, a major trend in 2025.
AI Agent Market	The overall market for AI agent technology.	The market was valued at over $5 billion in 2024 and is projected to grow at a rate of over 45% annually through 2030, highlighting the massive investment and focus on building autonomous AI systems.

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Comprehensive AI Terminology Guide

Model Architecture Concepts

The architecture of an AI model defines its structure and how it processes data. These concepts are fundamental to understanding how models like xLAM or GPT-3 are built.

Concept	Description	General Use Case	Examples
Parameters	Number of trainable weights in a model, indicating its size and capacity. Larger models often have better performance but require significant computational resources.	Determines model complexity and deployment feasibility.	GPT-3: 175B (one of the largest), LLaMa-3: 70B, xLAM-1b (smallest for efficiency).
Layers	Depth of the model, measured by the number of transformer layers, which process data sequentially.	Deeper layers enable capturing hierarchical patterns in data.	BERT: 12 layers (smaller), GPT-3: 96 layers (deep).
Attention Mechanisms	Mechanisms that allow models to weigh the importance of different input parts, crucial for understanding context.	Processes long sequences in NLP tasks effectively.	Self-attention in transformers, used in BERT, GPT, T5.
Transformer	A neural network architecture with encoder and/or decoder blocks, forming the backbone of modern LLMs.	Powers tasks like text generation and translation.	GPT (decoder-only), BERT (encoder-only), T5 (both).
Mixture-of-Experts (MoE)	Architecture using multiple specialized sub-models, activating only a subset for each task to improve efficiency.	Enables scalable, high-performance models with lower compute.	xLAM-8x22b, Mixtral by Mistral AI.
Large Action Models (LAMs)	Models designed for executing actions, such as interacting with tools or APIs, rather than just generating text.	Automates complex workflows, like booking or data retrieval.	xLAM models, watt-tool-70B for tool-use tasks.
Residual Connections	Skip connections that allow gradients to flow directly, aiding training of deep networks.	Prevents vanishing gradients in deep models.	Standard in transformers like GPT, BERT.
Positional Encoding	Adds information about token positions in sequences, enabling models to understand word order.	Critical for sequence-based tasks like NLP.	Sinusoidal encoding in original transformers.
Embeddings	Dense vector representations capturing semantic meaning of words or tokens.	Used in NLP for tasks like similarity detection.	Word2Vec, GloVe, BERT contextual embeddings.
Tokenization	Process of splitting text into tokens (e.g., words or subwords) for model input.	Prepares text for processing by LLMs.	Byte-Pair Encoding (GPT), WordPiece (BERT).

Training Concepts

Training involves preparing a model to perform tasks by learning from data. These concepts explain how models are developed and optimized.

Concept	Description	General Use Case	Examples
Pre-training	Training a model on a large, diverse dataset to learn general language or task patterns, often unsupervised.	Provides a versatile base for downstream tasks.	BERT on Wikipedia and BooksCorpus, GPT on web text.
Fine-tuning	Adapting a pre-trained model with task-specific data to improve performance on a targeted application.	Enhances model accuracy for specific use cases.	Fine-tuning GPT for chatbots, xLAM for function-calling.
Dataset Synthesis	Generating artificial data to augment training datasets, especially when real data is limited.	Enables training for niche tasks like tool-use.	Synthetic data for xLAM tool-use, OpenMathReasoning math problems.
Data Augmentation	Techniques to increase data diversity (e.g., paraphrasing text, rotating images) without collecting new samples.	Improves model robustness and generalization.	Back-translation for translation models, image flips in vision.
Supervised Learning	Training with labeled data where inputs are paired with correct outputs.	Common for classification or regression tasks.	Image classification with labeled images, NER with tagged text.
Unsupervised Learning	Training without labeled data to discover patterns, often used in pre-training.	Learns representations from raw data.	Masked language modeling in BERT, clustering in embeddings.
Reinforcement Learning	Training through rewards and penalties to optimize decision-making in dynamic environments.	Used for tasks requiring sequential decisions.	RLHF in ChatGPT, AlphaGo for game playing.
Transfer Learning	Applying knowledge learned from one task to improve performance on a related task.	Reduces training time for new tasks.	Using BERT for sentiment analysis, ImageNet for medical imaging.
Overfitting	When a model learns training data too well, including noise, and performs poorly on new data.	Avoided to ensure models generalize to unseen data.	Regularization techniques like dropout prevent this.
Regularization	Methods like weight penalties or dropout to prevent overfitting by constraining model complexity.	Ensures models perform well on test data.	L1/L2 regularization, dropout in neural networks.
Hyperparameters	Settings like learning rate or batch size that control the training process, tuned before training.	Optimizes training efficiency and model performance.	Learning rate of 0.001, batch size of 32.
Learning Rate	Step size for updating model weights during training, balancing speed and stability.	Affects convergence and training quality.	Adam optimizer with adaptive learning rates.
Optimizer	Algorithm to update model weights by minimizing the loss function, like Adam or SGD.	Drives efficient training of neural networks.	Adam in most LLMs, SGD in simpler models.
Gradient Descent	Iterative process to minimize the loss function by updating weights in the direction of the gradient.	Core mechanism for training neural networks.	Batch gradient descent, stochastic gradient descent.
Loss Function	Measures the difference between predicted and actual outputs, guiding model optimization.	Defines the training objective.	Cross-entropy for classification, MSE for regression.

Inference Concepts

Inference is the process of using a trained model to generate outputs. These terms cover how models are deployed and optimized for real-world use.

Concept	Description	General Use Case	Examples
Inference	Running a trained model to produce predictions or outputs based on new inputs.	Powers applications like chatbots or image recognition.	Generating text with GPT, classifying images with ResNet.
Quantization	Reducing the precision of model weights (e.g., from 32-bit to 8-bit) to lower memory and compute needs.	Enables deployment on edge devices or faster inference.	INT8 quantization for LLMs, used in mobile AI apps.
Distillation	Training a smaller “student” model to replicate a larger “teacher” model’s behavior.	Creates lightweight models for resource-constrained environments.	DistilBERT (from BERT), TinyML models.
Latency	Time taken for a model to process an input and produce an output.	Critical for real-time applications like voice assistants.	Sub-second response times in chatbots.
Throughput	Number of inputs a model can process per unit time, measuring system efficiency.	Important for high-traffic services like web APIs.	100 requests/second in cloud-based LLMs.
Beam Search	A decoding strategy that explores multiple sequence paths to generate high-quality text.	Improves coherence in text generation tasks.	Used in machine translation, summarization with T5.
Top-k Sampling	Selecting from the top k most probable tokens during text generation to balance creativity and accuracy.	Generates diverse yet coherent text outputs.	Used in GPT-3, LLaMa for creative writing.
Batch Size	Number of inputs processed simultaneously during inference, affecting speed and memory.	Optimizes resource use in deployment.	Batch size of 32 for text generation in production.
ONNX	Open Neural Network Exchange, a format for representing models to enable cross-framework use.	Allows models to run on different platforms.	Converting PyTorch models to ONNX for deployment.
TensorRT	NVIDIA library for optimizing inference on GPUs, reducing latency and increasing throughput.	Accelerates inference for real-time applications.	Faster LLM inference on NVIDIA hardware.

Evaluation Concepts

Evaluation measures how well models perform. These terms include benchmarks and metrics used to compare models like xLAM or watt-tool-70B.

Concept	Description	General Use Case	Examples
Benchmarks	Standardized datasets or tasks to evaluate model performance across consistent conditions.	Enables fair comparison of models.	GLUE, SuperGLUE, MMLU, GSM8K for math.
Leaderboards	Public rankings of model performance on specific benchmarks, tracking state-of-the-art.	Highlights top-performing models in the field.	BFCL, Hugging Face Open LLM Leaderboard.
BFCL	Berkeley Function-Calling Leaderboard, assessing models’ ability to invoke functions correctly.	Evaluates tool-use and function-calling skills.	xLAM-2-70b-fc-r, watt-tool-70B lead BFCL.
τ-bench	A benchmark for evaluating agentic tool-use in multi-turn, real-world-like tasks.	Tests complex agent interactions and planning.	xLAM-2 outperforms GPT-4o on τ-bench.
AIMO	AI Mathematical Olympiad, a competition for models solving advanced math problems.	Assesses mathematical reasoning capabilities.	OpenMathReasoning excels in AIMO-2 challenges.
Accuracy	Proportion of correct predictions, a basic metric for classification tasks.	Measures model correctness in straightforward tasks.	95% accuracy on image classification test sets.
F1 Score	Harmonic mean of precision and recall, useful for imbalanced datasets.	Evaluates performance in tasks like NER or sentiment analysis.	F1 score in named entity recognition tasks.
Perplexity	Measures how well a language model predicts a text sample; lower is better.	Assesses language model quality in generation tasks.	Perplexity of 20 on held-out text data.
Human Evaluation	Using human judges to assess model outputs, often for subjective quality.	Validates outputs in tasks like dialogue or creativity.	Evaluating chatbot coherence or translation quality.
Cross-Validation	Splitting data into training and validation sets to estimate model generalization.	Ensures robust performance across data splits.	5-fold cross-validation in machine learning.
Hyperparameter Tuning	Adjusting settings like learning rate to optimize model performance.	Improves model accuracy and training efficiency.	Grid search for optimal learning rate in LLMs.
BLEU Score	Metric for evaluating machine translation by comparing generated text to references.	Measures translation quality in NLP tasks.	BLEU score for Google Translate outputs.

Application Concepts

Applications show what AI models can achieve in real-world scenarios, from tool-use to reasoning.

Concept	Description	General Use Case	Examples
Tool-use	Ability to interact with external tools or APIs to perform tasks.	Automates workflows like data retrieval or calculations.	xLAM calling APIs, watt-tool-70B for tool tasks.
Function Calling	Invoking predefined functions based on user input, a subset of tool-use.	Enables structured interactions with software systems.	xLAM-2, watt-tool-70B for function-calling tasks.
Multi-turn Conversation	Maintaining context and coherence over multiple dialogue exchanges.	Powers interactive chatbots and virtual assistants.	ChatGPT, Grok, customer service bots.
Reasoning	Performing logical deductions or solving problems, often in math or logic.	Solves complex tasks requiring step-by-step thinking.	OpenMathReasoning for math, DeepMind’s AlphaCode.
Code Generation	Writing code based on natural language descriptions or prompts.	Assists developers, automates coding tasks.	GitHub Copilot, CodeLLaMa, xLAM for scripts.
Machine Translation	Translating text from one language to another automatically.	Facilitates cross-lingual communication and content access.	Google Translate, DeepL, T5 for translation.
Summarization	Condensing long texts into concise summaries while retaining key points.	Generates news digests, research abstracts, or reports.	BART, T5, Pegasus for text summarization.
Question Answering	Providing accurate answers to user questions, often from a context or knowledge base.	Powers search engines, virtual assistants, and FAQs.	BERT on SQuAD, GPT-4 for open-domain QA.
Sentiment Analysis	Determining the emotional tone (e.g., positive, negative) in text data.	Analyzes customer feedback, social media, or reviews.	VADER, BERT-based sentiment classifiers.
Named Entity Recognition (NER)	Identifying and classifying entities like names, organizations, or locations in text.	Extracts structured information from unstructured text.	SpaCy, BERT for NER tasks in NLP pipelines.

Ethical Considerations

Ethical considerations ensure AI is developed and used responsibly, addressing societal impacts.

Concept	Description	General Use Case	Examples
Bias	Unfair prejudices in model outputs, often from biased training data.	Can lead to discriminatory outcomes in hiring or policing.	Gender bias in language models, racial bias in facial recognition.
Fairness	Ensuring models treat all groups equitably, avoiding discrimination.	Critical for applications like loan approvals or hiring.	Fair algorithms in credit scoring, equitable AI frameworks.
Transparency	Making model decisions and processes understandable to users.	Builds trust and enables auditing of AI systems.	Explainable AI techniques, model cards on Hugging Face.
Accountability	Holding developers and organizations responsible for AI behavior.	Ensures ethical deployment and compliance with regulations.	GDPR compliance, AI ethics boards in companies.
Privacy	Protecting user data during training and inference to prevent leaks.	Maintains user trust in AI applications like health or finance.	Differential privacy in training, federated learning.

============

Attribute
Purpose
Model
Parameters
LLM Base
Dataset
Dataset Synthesis
Multi-Turn Conversation
Tool Use
Function Calling
Inference
Optimization
State-of-the-Art
τ-bench
Similar Benchmarks
LAM (Large Action Model)
Reasoning
AIMO
BFCL (Berkeley Function-Calling Leaderboard)
Open-Source
Key Features
Use Case Example
Limitations
Recent Updates

===========

1. Key Concepts in AI Models and Usage

Concept	Definition	Notes
Token	Roughly a word-piece (about ¾ of a word on average)	"computer" is one token, "fantastic" is one, but "fantas-tic" might split into two
Context Window	Maximum number of tokens the model can read at once	Input + output tokens must fit within this window
Input Tokens	Tokens sent to the model when asking a question	Counts toward token usage
Output Tokens	Tokens the model returns as its answer	Counts toward token usage
Quantization	Technique to reduce model size (e.g., 4-bit)	Reduces RAM and CPU demands for local inference
Multi-modal	Model can process more than one type of data	Includes text, images, audio, video
Agent Mode	AI can autonomously plan and perform multi-step tasks	Often seen in coding assistants
Open Source Model	Model weights and architecture are publicly available	Allows for customization and local deployment
Proprietary Model	Model details are kept confidential by the developer	Accessed via API or dedicated platforms

==============

https://help.kagi.com/kagi/ai/llm-benchmark.html

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score

=======

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time to first token, generation time

======

image gen

https://pollinations.ai/p/an apple?height=512&width=512&model=flux
https://pollinations.ai/p/an orange?height=512&width=512&model=flux

cot

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score
o3 Y 76.29 502 2.57191 6056 12 29 15
claude-3-7-extended-thinking Y 71.34 847 2.20567 81931 96 32 8
gemini-2-5-pro Y 68.72 381 0.257 9905 25 267 18
qwen-qwq-32b Y 65.94 763 0.11994 340400 446 553 8
o1 Y 65.44 502 6.55213 3678 7 9 13
o3-mini Y 65.16 502 0.52675 10333 20 123 12
deepseek-r1 Y 64.06 301 1.16229 101071 335 55 21
o4-mini Y 62.27 502 0.41746 4253 8 149 12
deepseek-r1-distill-llama-70b Y 54.41 381 0.40643 91634 240 133 14
o1-pro Y 44.38 502 59.5752 2628 5 0 8
claude-3-7-sonnet Y 42.94 301 0.30431 10852 36 141 14

accuracy

model CoT accuracy time cost tokens speed (t/s) accuracy/$ score accuracy/sec score
o3 Y 76.29 502 2.57191 6056 12 29 15
claude-3-7-extended-thinking Y 71.34 847 2.20567 81931 96 32 8
gemini-2-5-pro Y 68.72 381 0.257 9905 25 267 18
qwen-qwq-32b Y 65.94 763 0.11994 340400 446 553 8
o1 Y 65.44 502 6.55213 3678 7 9 13
o3-mini Y 65.16 502 0.52675 10333 20 123 12
deepseek-r1 Y 64.06 301 1.16229 101071 335 55 21
o4-mini Y 62.27 502 0.41746 4253 8 149 12

Meta-Llama-3.1-405B-Instruct

code

Model Percent completed correctly Percent using correct edit format Command Edit format
o1 84.2% 99.2% aider --model openrouter/openai/o1 diff
claude-3-5-sonnet-20241022 84.2% 99.2% aider --model anthropic/claude-3-5-sonnet-20241022 diff
gemini-exp-1206 (whole) 80.5% 100.0% aider --model gemini/gemini-exp-1206 whole
o1-preview 79.7% 93.2% aider --model o1-preview diff

WebDev Leaderboard

1	Claude 3.7 Sonnet (20250219)	1356.70	+7.95 / -7.08	7,481	Anthropic	Proprietary

2	GPT-4.1-2025-04-14				1283.42	+23.61 / -13.07	1,250	OpenAI	Proprietary

2	Gemini-2.5-Pro-Exp-03-25		1275.55	+8.64 / -6.33	7,836	Google	Proprietary

4	Claude 3.5 Sonnet (20241022)	1239.33	+4.77 / -3.45	25,309	Anthropic	Proprietary

5	DeepSeek-V3-0324				1207.01	+17.32 / -19.14	1,097	DeepSeek	MIT

Intelligence Index incorporates 7 evaluations: MMLU-Pro, GPQA Diamond, Humanity's Last Exam, LiveCodeBench, SciCode, AIME, MATH-500

Reasoning Model

```
o4-mini (high)
```
```
Gemini 2.5 Pro
```
```
o3
```
```
Grok 3 miniReasoning(high)
```
```
	Llama 3.1Nemotron Ultra253B Reasoning
```
```
Gemini 2.5Flash(Reasoning)
```
```
	DeepSeek R1
```
```
Claude 3.7SonnetThinking
```

Non-Reasoning Model

```
	DeepSeek V3(Mar' 25)
```
```
GPT-4.1 mini
```
```
GPT-4.1
```
```
Grok 3
```
```
	Llama 4Maverick
```
```
	Llama 4 Scout
```
```
GPT-4o (Nov'24)
```
```
Mistral Large2 (Nov '24)
```
```
	Gemma 3 27B
```
```
Nova Pro
```

Cheapest API Provider : Llama 3.3 70B Input Cost

```
1			Lambda			`$`0.20 / 1M tokens
```
```
2			DeepInfra			`$`0.23 / 1M tokens
```
```
3			Hyperbolic			`$`0.40 / 1M tokens
```

Best LLM - Code : HumanEval benchmark

```
1	Claude 3.5 Sonnet	93.7
```
```
2	Qwen2.5-Coder 32B Instruct	92.7
```
```
3	o1-mini	92.4
```

Benchmarks Leaderboards about code, reasoning and general knowledge

MMLU Leaderboard Knowledge and reasoning across science, math, and humanities.
MMLU-Pro Leaderboard Advanced version of MMLU with more complex reasoning tests.
GPQA Leaderboard 448 "Google-proof" questions in biology, physics, and chemistry.
HumanEval Leaderboard Performance on Python coding tasks like sorting, searching, etc.
DROP Leaderboard Reading comprehension with reasoning over paragraphs.
MATH Leaderboard Performance on high school mathematics problems.

======

Embeddings & Models Platform

======

The AI Learning Ladder: Your Step-by-Step Guide to Understanding Artificial Intelligence

Rung 0 – The Foundation: Three Essential Building Blocks

Rung 1 – From Ingredients to Intelligence: How AI Actually Learns

Rung 2 – Teaching Strategies: Different Ways AI Can Learn

Rung 3 – Building Electronic Brains: Understanding Neural Networks

Rung 4 – The Language Revolution: How AI Learned to Chat

Rung 5 – Standing on Giants' Shoulders: Reusing Existing Models

Rung 6 – Having a Conversation: Interacting with AI Systems

Rung 7 – Keeping AI Current: Connecting to Real-World Information

Rung 8 – Thinking and Acting: Advanced Reasoning and Real-World Actions

Rung 9 – From Lab to Life: Deploying AI in the Real World

Rung 10 – AI Safety and Ethics: Building Technology We Can Trust

Rung 11 – The AI Ecosystem: Key Players, Tools, and Platforms (as of mid-2025)

Comprehensive AI Terminology Guide

Model Architecture Concepts

Training Concepts

Inference Concepts

Evaluation Concepts

Application Concepts

Ethical Considerations

1. Key Concepts in AI Models and Usage

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Code Review

The AI Learning Ladder: Your Step-by-Step Guide to Understanding Artificial Intelligence

Rung 0 – The Foundation: Three Essential Building Blocks

Rung 1 – From Ingredients to Intelligence: How AI Actually Learns

Rung 2 – Teaching Strategies: Different Ways AI Can Learn

Rung 3 – Building Electronic Brains: Understanding Neural Networks

Rung 4 – The Language Revolution: How AI Learned to Chat

Rung 5 – Standing on Giants' Shoulders: Reusing Existing Models

Rung 6 – Having a Conversation: Interacting with AI Systems

Rung 7 – Keeping AI Current: Connecting to Real-World Information

Rung 8 – Thinking and Acting: Advanced Reasoning and Real-World Actions

Rung 9 – From Lab to Life: Deploying AI in the Real World

Rung 10 – AI Safety and Ethics: Building Technology We Can Trust

Rung 11 – The AI Ecosystem: Key Players, Tools, and Platforms (as of mid-2025)

Comprehensive AI Terminology Guide

Model Architecture Concepts

Training Concepts

Inference Concepts

Evaluation Concepts

Application Concepts

Ethical Considerations

1. Key Concepts in AI Models and Usage