Glossary
The AI vocabulary, decoded.
Short, plain-English definitions for every term you'll bump into when shopping for or shipping AI agents.
Agent Framework
Software that helps developers build, run, and orchestrate AI agents.
Agentic AI
AI that takes actions in the world, not just generates text.
AGI (Artificial General Intelligence)
Hypothetical AI matching or exceeding humans across most intellectual tasks.
AI Agent
An LLM wired to tools, memory, and a goal — capable of multi-step action.
Autonomous Agent
An agent that runs without per-step human supervision.
Chain of Thought
Prompting technique that asks an LLM to reason step-by-step before answering.
Computer Use
An AI's ability to control a computer's screen, mouse, and keyboard.
Context Engineering
Designing what information goes into an LLM's context to get reliable behavior.
Context Window
How much text an LLM can consider at once when generating a response.
Embeddings
Numeric vectors that represent the meaning of text, images, or other data.
Evals
Tests that measure an AI system's quality on real tasks.
Few-shot / Zero-shot
Whether you give the LLM examples (few-shot) or just instructions (zero-shot).
Fine-tuning
Adapting a pre-trained LLM by continuing training on a smaller, task-specific dataset.
Foundation Model
A large general-purpose model trained on broad data, intended to be adapted to many tasks.
Function Calling
An older name for tool use — getting an LLM to invoke defined functions.
Guardrails
Rules and checks that prevent an AI system from saying or doing harmful things.
Hallucination
When an LLM produces output that's confident-sounding but factually wrong.
Inference
The act of running a trained AI model to generate output for a given input.
Inference Cost
How much it costs to run an LLM inference call.
LLM (Large Language Model)
A neural network trained on huge text corpora to predict and generate language.
MCP (Model Context Protocol)
Open protocol for connecting AI agents to external tools and data.
Multimodal
An AI model that can process and generate multiple types of input — text, images, audio, video.
Prompt Engineering
Designing inputs to an LLM to get reliable, high-quality outputs.
RAG (Retrieval-Augmented Generation)
Giving an LLM relevant context fetched from your data at query time.
Reasoning Model
An LLM trained or designed to spend extra compute on hard problems before answering.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that aligns LLMs with human preferences using ranked outputs.
System Prompt
Top-of-conversation instructions that establish the model's role and rules.
Token
The basic unit an LLM reads and generates — roughly a fraction of a word.
Tool Use
An LLM's ability to call defined functions to fetch data or take actions.
Vector Database
A database optimized for similarity search over high-dimensional vectors.
Vibe Coding
Coding by describing what you want and letting AI generate it, without reviewing every line.