AI Glossary

Think of a massive language model as a fully built Lego castle. An adapter is a small plug‑in block you snap onto the existing walls to give the castl...

An AI system that can take actions autonomously — reading files, calling APIs, running code, making decisions — not just generating text. Agents do wo...

A process where AI operates with autonomy: planning steps, using tools, evaluating results, and iterating without human intervention at each step. The...

A structured way for software to talk to other software. When you send a prompt to Claude through code instead of a chat window, you're using an API.

The mechanism that lets AI focus on relevant parts of the input when generating output. It's why AI can connect "the cat sat on the mat" to a question...

A prompting technique where you ask AI to reason step by step instead of jumping to the answer. It improves accuracy on complex tasks by forcing the m...

Splitting documents into smaller pieces for storage in a vector database. Chunk size is a critical design choice: too big and you retrieve irrelevant ...

The text that an AI model generates in response to a prompt. You send a prompt; you get back a completion.

The practice of carefully curating what information you feed to AI — not just how you ask, but what data you include. More strategic than prompt engin...

The maximum amount of text an AI can "see" at once — both your input and its output combined. Think of it as the model's working memory. Bigger window...

An AI that generates images by starting with random noise and gradually refining it into a picture. Used by DALL-E, Midjourney, and Stable Diffusion.

A way to convert text (or images) into a list of numbers that captures meaning. Similar concepts get similar numbers, which is how AI can understand t...

A prompting technique where you provide 2-5 examples of the desired input/output format before asking AI to process new input. Dramatically improves c...

Training an existing AI model on your own specific data to make it better at a particular task. Like teaching a general-purpose chef to specialize in ...

Anchoring AI responses in real data (retrieved documents, search results, database records) rather than relying on training data alone. RAG is the mos...

Rules and constraints you set to keep AI output safe, on-topic, and within boundaries. "Do NOT modify existing tests" is a guardrail. They prevent AI ...

When AI generates information that sounds confident but is factually wrong. It doesn't "know" it's making things up — it's pattern-matching, not fact-...

A pattern where sensitive AI actions (send email, delete files, deploy code) require human approval before execution. The agent proposes, the human ap...

The process of running a trained AI model to generate output. When you send a prompt and get a response, that's inference. Training teaches the model;...

Instruction tuning is like giving a student a cheat sheet of how to answer different types of questions before the exam. You train a language model on...

The time between sending a prompt and receiving the first token of the response. Lower latency means faster responses. Affected by model size, server ...

A massive AI model trained on enormous amounts of text data. Claude, GPT, and Gemini are all LLMs. "Large" refers to the billions of parameters (adjus...

An open protocol that lets AI models connect to external tools and data sources in a standardized way. Like USB for AI — one protocol, many tools. Cre...

A lightweight program that exposes tools to AI via the Model Context Protocol. One MCP server for Gmail means every AI app can send/read email. Build ...

Model drift happens when a model's performance slides downhill because the real‑world data it encounters gradually differs from the data it was traine...

A model registry is like a library catalog for your AI models. It records each model version, its training data, performance metrics, and who approved...

An AI that can process multiple types of input — text, images, audio, video — not just words. A multimodal model can look at a photo and describe what...

An AI agent that coordinates other agents, breaking complex tasks into subtasks and assigning them. Like a project manager for AI teams. In multi-agen...

The adjustable numbers inside a neural network that the model learns during training. More parameters generally means more capability, but also more c...

A RAG system built on your own knowledge base — blog posts, talks, documents, investor memos — so an AI can answer questions grounded in your actual e...

The input you give to an AI model — the question, instruction, or context that tells it what to do. Better prompts get better outputs. This is the ski...

The art and science of crafting prompts that get the best results from AI. Includes techniques like role prompting, chain-of-thought, few-shot example...

A security attack where malicious instructions are hidden in data that AI processes. Like SQL injection, but for AI prompts. Important to understand i...

Quantization is like shrinking a high‑resolution photo into a smaller file by reducing the number of colors, but for neural nets. It drops the precisi...

A technique that gives AI access to external data (documents, databases) at query time. Instead of relying only on training data, the model retrieves ...

A cap on how many API requests you can make in a given time period. Prevents abuse and ensures fair access. If you hit one, you need to slow down or u...

A training technique where humans rate AI outputs, and the model learns to produce responses humans prefer. It's why modern AI feels more helpful and ...

A collection of tools, libraries, and documentation that makes it easier to build with an API. The Anthropic SDK lets you use Claude in your Python or...

A multi-step workflow an agent knows how to execute by chaining tools together. "Deploy a website" is a skill (read files → build → deploy → verify). ...

A secondary AI agent spawned by a primary agent to handle a specific subtask. Like delegating work to a team member. Multiple subagents can run in par...

Hidden instructions given to an AI that set its behavior, personality, and rules before the user starts chatting. It's the difference between "generic...

A setting that controls how random or creative AI output is. Low temperature (0.0-0.3) = deterministic and focused. High temperature (0.7-1.0) = more ...

The basic unit AI uses to process text. A token is roughly 3/4 of a word. "Hamburger" is 3 tokens. Tokens determine cost (you pay per token) and fit w...

The process of splitting text into tokens — the fundamental units an LLM processes. Different models use different tokenizers, so the same text can pr...

The ability of AI to call external functions — search the web, read files, run code, access APIs. This is what separates agents from chatbots. Without...

The neural network architecture behind all modern LLMs. Introduced in the 2017 paper "Attention Is All You Need." Uses attention mechanisms to process...

A database designed to store and search embeddings (number representations of text/images). Used in RAG systems to find relevant documents quickly. Ex...

Synonym for parameters. The numerical values inside a neural network that determine how it processes input. Training adjusts the weights; inference us...

Asking AI to do something without giving it any examples. "Translate this to French" is zero-shot. Contrast with few-shot (providing examples first). ...