AI coding glossary

43 terms from the world of AI coding tools, defined in plain English with what each one actually means when you are prompting.

Agents 10 terms

Agent loop: The agent loop is the repeating cycle an AI agent runs: decide what to do next, take an action such as reading a file or running a command, observe the result, and repeat.
Agent skills: Agent skills are reusable packages of instructions, context, and sometimes code that an agent loads when a task calls for them.
Agentic coding: Agentic coding is using an AI tool that plans and executes a whole task in your codebase rather than completing one line at a time.
Autonomous coding agent: An autonomous coding agent completes a described task without step-by-step supervision: it plans, edits, runs, and verifies on its own, reporting back when done.
CLI coding agent: A CLI coding agent is a coding agent you invoke from the command line.
Coding agent: A coding agent is an AI system that carries out programming tasks end to end: reading a codebase, editing multiple files, running commands, and reacting to the output.
Headless agent: A headless agent runs without an interactive interface — invoked by a script, a CI job, or a scheduler rather than a person typing.
Subagent: A subagent is an agent spawned by another agent to handle a scoped piece of work in its own context window.
Terminal AI agent: A terminal AI agent runs in your shell rather than inside an editor.
Tool calling: Tool calling is the mechanism that lets a model do things beyond producing text.

Agent mode: Agent mode is the setting in an AI coding tool where the model can act rather than only answer: creating and editing files, running terminal commands, and iterating on the result.
Ask mode: Ask mode is the read-only setting in an AI coding tool: the model answers questions about your code and explains what it finds, but does not edit files or run commands.
Composer mode: Composer is Cursor's multi-file editing interface, where you describe a change and the tool proposes coordinated edits across several files at once, presented as diffs you review together rather than one file at a time..
Plan mode: Plan mode makes an AI coding tool propose an approach before it edits anything.
YOLO mode: YOLO mode is the informal name for running an AI agent with approvals disabled, letting it edit files and run commands without asking each time.

Codebase indexing: Codebase indexing is the process an AI editor uses to build a searchable representation of your repository, usually by splitting files into chunks and storing vector embeddings so it can retrieve code by meaning rather than exact text match..
Context engineering: Context engineering is the practice of deciding what information an AI model sees for a given task — which files, which conventions, which prior results — and in what form.
Context retrieval: Context retrieval is how an AI coding tool decides which parts of your codebase to read for a given request.
Context window: The context window is the maximum amount of text a model can consider at once, measured in tokens and covering everything in the request: system prompt, your instructions, the files supplied, prior conversation, and the response being generated..
Repo map: A repo map is a compressed outline of a codebase — its files, key symbols, and how they relate — given to a model so it understands the shape of a project without reading every file.

AGENTS.md: AGENTS.md is a tool-neutral convention for a Markdown file in a repository that gives AI coding agents the project context they need — build commands, conventions, architecture, and constraints.
CLAUDE.md: CLAUDE.md is a Markdown file in your repository that Claude Code reads at the start of every session.
Cursor rules: Cursor rules are project-level instructions stored in your repository that Cursor applies to every AI request — conventions, framework versions, patterns to follow, things never to do.
Custom instructions: Custom instructions are standing directions you give an AI tool that apply to every request rather than a single one — preferred language and framework, response style, conventions to follow.

Meta-prompting: Meta-prompting is using a model to write or improve a prompt rather than to do the task directly.
Prompt injection: Prompt injection is an attack where instructions hidden in content a model reads are treated as commands rather than data.
Prompt template: A prompt template is a reusable prompt with placeholders for the parts that change — the file, the framework, the requirement — so a structure that works can be applied to many tasks without being rewritten each time..
System prompt: A system prompt is the instruction set given to a model before any user message, defining its role, constraints, and behaviour for the whole conversation.

AI code completion: AI code completion predicts and suggests the code you are about to write, usually as inline grey text you accept with Tab.
AI code generation: AI code generation is producing working code from a natural-language description — a function, a component, a module, or an entire application.
AI code review: AI code review is using a model to examine a diff or a file for bugs, security problems, and quality issues before a human reviews it.
AI pair programming: AI pair programming is working alongside an AI assistant the way you would with a human pair: it suggests, you steer, and you review continuously rather than delegating a task and checking back later.
AI refactoring: AI refactoring is using an AI tool to restructure existing code without changing its behaviour — extracting functions, renaming concepts across a codebase, splitting large modules, or migrating between patterns.
PRD for AI: A PRD for AI is a product requirements document written to be consumed by an AI coding tool rather than by a team.
Spec-driven development: Spec-driven development means writing a precise specification before generating any code, then using that spec as the prompt.
Test-driven development with AI: Test-driven development with AI means writing or generating the tests first, then letting an agent implement until they pass.

Design to code: Design to code is generating working interface code from a visual input or a description of one — a screenshot, a Figma file, or written art direction.
Text to app: Text to app describes tools that generate a working application from a written description — pages, data model, and often auth and deployment — without you writing the scaffolding.

LLM evals: LLM evals are systematic tests of a model or AI feature's output quality: a set of inputs, a definition of a good response, and a scoring method.
SWE-bench: SWE-bench is a benchmark that tests whether AI systems can resolve real GitHub issues from open-source Python repositories.

Input tokens: Input tokens are the units of text a model reads: your prompt, the files supplied, the system prompt, and the conversation so far.
Output tokens: Output tokens are the text a model generates: explanations, code, and — with reasoning models — internal thinking that is billed even when it is not shown.
Token limit: A token limit is a cap on how much text can be processed in a request or a period.