AI Glossary

Plain-English definitions for AI terms every builder should know. Updated weekly.

ABCDE FGHIJKL MNOPQRSTUVWXYZ

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AI Agent

An autonomous AI system that perceives its environment, makes decisions, and takes actions to complete a goal. AI agents use LLMs as their reasoning engine and have access to tools (web search, code execution, APIs) to act on the world.

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Context Window

The maximum amount of text (in tokens) that a language model can process in one prompt-response cycle. Both input (prompt) and output (completion) count against the limit.

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Embeddings

Numerical vector representations of text, images, or other data that capture semantic meaning. Embeddings allow AI systems to measure similarity between concepts by comparing positions in high-dimensional vector space.

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Fine-Tuning

Further training a pre-trained LLM on a domain-specific dataset to improve performance on specialized tasks. Fine-tuning adjusts model weights to better match a target behavior or domain.

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Hallucination

When an AI language model generates confident-sounding information that is factually incorrect, fabricated, or nonsensical. Hallucinations are a fundamental risk in any AI system that generates text.

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Large Language Model

A type of AI trained on massive text data that can understand, generate, and manipulate human language. LLMs are the foundation of Claude, ChatGPT, Gemini, and similar tools.

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A

AI AgentAn autonomous AI system that perceives its environment, makes decisions, and takes actions to complete a goal. AI agents use LLMs as their reasoning engine and have access to tools (web search, code execution, APIs) to act on the world.

C

Context WindowThe maximum amount of text (in tokens) that a language model can process in one prompt-response cycle. Both input (prompt) and output (completion) count against the limit.

E

EmbeddingsNumerical vector representations of text, images, or other data that capture semantic meaning. Embeddings allow AI systems to measure similarity between concepts by comparing positions in high-dimensional vector space.

F

Fine-TuningFurther training a pre-trained LLM on a domain-specific dataset to improve performance on specialized tasks. Fine-tuning adjusts model weights to better match a target behavior or domain.

H

HallucinationWhen an AI language model generates confident-sounding information that is factually incorrect, fabricated, or nonsensical. Hallucinations are a fundamental risk in any AI system that generates text.

L

Large Language ModelA type of AI trained on massive text data that can understand, generate, and manipulate human language. LLMs are the foundation of Claude, ChatGPT, Gemini, and similar tools.

M

Model Context ProtocolAn open standard developed by Anthropic that defines how AI models like Claude communicate with external tools, data sources, and services. MCP enables AI assistants to read files, query databases, and call APIs in a standardized way.

Multi-Agent SystemAn AI architecture where multiple specialized AI agents collaborate to complete complex tasks. Each agent handles a specific subtask (research, writing, review) and passes results to the next agent in the pipeline.

P

Prompt EngineeringThe practice of designing and optimizing input prompts to reliably elicit desired outputs from an LLM. Effective prompts combine clear instructions, relevant context, output format specification, and examples.

R

Retrieval-Augmented GenerationA technique that enhances LLM outputs by retrieving relevant information from an external knowledge base before generating a response, combining generative power with accurate, up-to-date data.

RLHFReinforcement Learning from Human Feedback — the training technique used to align language models with human preferences. Human raters score model outputs, and the model is trained to produce outputs that humans prefer.

T

TokenThe basic unit of text that language models process. Tokens are roughly 3-4 characters or 0.75 words in English. LLM pricing and context window limits are measured in tokens.

Tool CallingA capability that allows LLMs to invoke external functions, APIs, or services during a conversation. The model decides when to call a tool, formulates the input, and incorporates the tool's output into its response.

Transformer ArchitectureThe neural network architecture underlying all modern large language models. Introduced by Google in the 2017 paper "Attention Is All You Need," the Transformer uses self-attention mechanisms to process text in parallel rather than sequentially.

V

Vector DatabaseA specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. Vector databases power semantic search, RAG pipelines, and similarity-based recommendation systems.

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