AI & LLM Glossary
Plain-English definitions of the AI terms you'll see thrown around. From attention to zero-shot, with links to the models and tools where each concept matters.
A
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AI agentAn AI system that can take actions on your behalf — calling tools, browsing the web, writing files — not just answering with text.
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AlignmentThe research problem of making AI systems do what humans actually want — not just what we ask for literally.
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AttentionThe mathematical mechanism that lets transformers focus on different parts of the input when generating each output token.
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AnthropicThe AI lab behind Claude. Founded by ex-OpenAI researchers focused on AI safety. Major investors include Amazon and Google.
B
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Chain of thoughtA prompting technique where the AI explains its reasoning step by step before giving a final answer — usually more accurate than direct answers.
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ChatGPTOpenAI's flagship AI chat product. Powered by the GPT family of models (4o, 4.1, o3, etc.).
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ClaudeAnthropic's family of AI models, known for thoughtful, careful, long-form writing. Available via claude.ai or AskAI.free.
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Constitutional AIAnthropic's training method where Claude is trained against a written 'constitution' of values — rather than ad-hoc human feedback for every example.
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Context windowHow much text an AI can 'see' at once. Measured in tokens. Bigger context = can analyse longer documents.
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P
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ParameterA single number inside a neural network. LLMs have billions of parameters — the bigger the count, the more capable (usually).
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PromptWhat you type to an AI. The art of writing effective prompts is called prompt engineering.
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Prompt engineeringThe discipline of writing AI prompts that consistently produce good answers. Includes techniques like few-shot, CoT, and role assignment.
R
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RAG (Retrieval-Augmented Generation)A technique for grounding AI answers in your own documents — retrieve relevant context first, then generate the answer.
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Reasoning modelAn AI model that explicitly 'thinks' before answering by generating a long chain of thought. Better at math, code and logic — slower than chat models.
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Reinforcement learning (RL)A training technique where the AI improves by trial and error, getting rewards for good outputs. The 'F' in RLHF.
S
T
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TemperatureA setting that controls AI creativity. 0 = deterministic and predictable. 1 = more varied, creative, sometimes unhinged.
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TokenThe unit AI models read and write in. Roughly 4 characters or 0.75 words. Pricing and context windows are measured in tokens.
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TokenizerThe component that converts text into tokens (and back). Different models use different tokenizers, which is why a sentence has a different token count in GPT vs Claude.
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Training dataThe text an LLM is taught on. Typically trillions of tokens scraped from the web, books, code repos and more.
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TransformerThe neural network architecture introduced in 2017 that powers every modern LLM — ChatGPT, Claude, Gemini, all of it.
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Tool use (function calling)When an AI model can call external functions — search, calculator, database — instead of just generating text.