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AI Coding Glossary
Key terms in AI-assisted development, explained clearly with practical context.
Agentic Coding
Agentic coding is a software development approach where an AI agent autonomously reads your codebase, writes code, runs commands, and iterates on results without manual copy-paste. Unlike chat-based AI, the agent takes direct action in your development environment to complete multi-step tasks.
Claude Code
Claude Code is Anthropic's terminal-based AI coding agent that operates directly in your development environment. It reads your entire project, writes code across multiple files, runs shell commands, manages git workflows, and iterates on errors autonomously—all from the command line.
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard created by Anthropic that provides a universal way to connect AI models to external tools, data sources, and APIs. It acts as a standardized interface—like USB for AI—so any MCP-compatible tool can work with any MCP-compatible AI agent.
CLAUDE.md
CLAUDE.md is a markdown configuration file placed in your project root that provides Claude Code with persistent, project-specific instructions. It tells the agent about your coding conventions, architecture, common commands, and rules—acting as a form of long-term memory that applies to every session in that project.
AI Pair Programming
AI pair programming is a development workflow where a human developer works alongside an AI tool to write code collaboratively in real-time. The developer provides direction, context, and judgment while the AI contributes code suggestions, catches bugs, and handles repetitive implementation tasks.
Context Window
A context window is the maximum number of tokens (words, code characters, and symbols) that an AI model can process in a single interaction. It defines the upper limit of how much information—including your prompt, code, and the model's response—the AI can hold in memory at once.
Coding Agent
A coding agent is an AI-powered tool that can autonomously read files, write code, execute terminal commands, and iterate on results to complete programming tasks. Unlike passive code suggestion tools, a coding agent takes independent action in your development environment to achieve a stated goal.
Vibe Coding
Vibe coding is an informal approach to software development where a developer describes what they want in natural language and lets an AI tool handle the implementation details. Instead of writing precise specifications, the developer communicates intent through casual conversation and iterates based on results.
AI Code Review
AI code review is the process of using artificial intelligence to automatically analyze source code for bugs, security vulnerabilities, style inconsistencies, and quality issues. AI reviewers can examine pull requests, suggest improvements, and catch problems that human reviewers might miss due to fatigue or time pressure.
Prompt Engineering for Code
Prompt engineering for code is the practice of crafting clear, specific instructions that help AI coding tools produce accurate, relevant output. It involves structuring your requests with the right level of context, constraints, and examples so the AI understands both what you want and how you want it done.
Headless AI Agent
A headless AI agent is a coding agent that runs without a human-facing interface or real-time interaction. It executes tasks autonomously in background processes, CI/CD pipelines, or scheduled jobs—reading code, making changes, running tests, and reporting results without waiting for human input at any step.
Sub-Agents
Sub-agents are parallel child processes spawned by a main AI coding agent to handle independent parts of a complex task simultaneously. Instead of processing everything sequentially, the main agent delegates sub-tasks to specialized child agents that run in parallel and report results back to the parent.
AI Code Completion
AI code completion is a feature in development tools that uses machine learning models to predict and suggest code as you type. It ranges from single-line autocomplete to multi-line function generation, analyzing the surrounding code context to offer relevant suggestions that match your intent and coding style.
Large Language Model (LLM)
A large language model (LLM) is a deep learning system with billions of parameters, trained on vast datasets of text and code to understand, generate, and reason about natural language and programming languages. LLMs like Claude, GPT-4, and Gemini are the foundation of modern AI coding tools.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is an AI architecture that improves the accuracy of language model responses by retrieving relevant information from external knowledge sources before generating an answer. Instead of relying solely on what the model memorized during training, RAG fetches up-to-date, domain-specific data and includes it in the model's context.
Tool Use
Tool use (also called tool calling) is the capability of a large language model to invoke external functions, APIs, or system commands as part of generating a response. Instead of being limited to producing text, a model with tool use can read files, run code, query databases, and interact with services—making it the foundation of agentic AI systems.
Function Calling
Function calling is an AI model capability where the model generates structured JSON arguments to invoke external functions instead of producing plain text. This enables LLMs to interact with APIs, databases, file systems, and other tools in a reliable, programmatic way—turning a conversational model into one that can take real-world actions.
System Prompt
A system prompt is a set of instructions provided to an AI model before the user's message that defines the model's behavior, persona, constraints, and capabilities. It acts as a configuration layer that shapes every response the model produces, without the user needing to repeat these instructions in each message.
Temperature
Temperature is a parameter in large language models that controls the randomness of the output. A temperature of 0 makes the model deterministic, always choosing the most probable next token. Higher temperatures (up to 1.0 or 2.0) increase randomness, making less probable tokens more likely to be selected. For coding tasks, lower temperatures generally produce more reliable, consistent code.
Token
A token is the fundamental unit of text that a large language model processes. Tokenization splits text into chunks—sometimes whole words, sometimes subwords, sometimes individual characters—that the model can work with. In English text, one token is roughly 3-4 characters or 0.75 words. In code, tokens map to keywords, operators, variable names, and whitespace.
Fine-Tuning
Fine-tuning is the process of further training a pre-trained large language model on a smaller, task-specific dataset to adapt its behavior for a particular use case. The model's weights are updated to specialize in a domain—such as a specific programming language, codebase, or output format—while retaining its general capabilities from pre-training.
Code Generation
AI code generation is the process of using artificial intelligence to produce source code from natural language descriptions, specifications, or existing code context. Modern code generation powered by LLMs can write entire functions, classes, tests, and even full applications from high-level instructions, across virtually any programming language.
AI Refactoring
AI refactoring is the use of artificial intelligence to automatically restructure, simplify, and improve existing source code without changing its external behavior. AI refactoring tools analyze code for complexity, duplication, poor naming, and anti-patterns, then apply transformations that make the code cleaner, more maintainable, and easier to understand.
AI Testing
AI testing is the application of artificial intelligence to software testing workflows—including generating unit tests, integration tests, and end-to-end tests from source code; identifying untested edge cases; analyzing test failures; and suggesting fixes. AI testing tools understand code semantics to write meaningful tests that go beyond basic coverage.
Multi-Modal AI
Multi-modal AI refers to artificial intelligence systems that can process, understand, and generate multiple types of data—text, images, audio, video, and code—within a single model. Unlike single-modal models that only handle text, multi-modal models can analyze a screenshot of a UI, read the associated code, and generate modifications based on both visual and textual understanding.
Chain-of-Thought
Chain-of-thought (CoT) prompting is a technique that encourages a large language model to break down complex problems into intermediate reasoning steps before producing a final answer. Instead of jumping to a conclusion, the model "thinks out loud," explaining each step of its logic. This significantly improves accuracy on tasks that require multi-step reasoning, including debugging, algorithm design, and code architecture decisions.
Few-Shot Prompting
Few-shot prompting is a technique where you include a small number of example input-output pairs in your prompt to demonstrate the pattern you want the AI to follow. By showing the model 2-5 examples of the desired behavior, it learns the format, style, and logic you expect—without any model training or fine-tuning. This is one of the most effective techniques for getting consistent, formatted output from LLMs.
Zero-Shot Prompting
Zero-shot prompting is a technique where you instruct an AI model to perform a task without providing any examples of the desired input-output format. You describe what you want in natural language, and the model relies entirely on its pre-trained knowledge to produce the output. It is the most natural way to interact with AI—just tell it what to do.
Embeddings
Embeddings are dense numerical vectors (arrays of floating-point numbers) that represent text, code, or other data in a high-dimensional space where semantically similar items are positioned close together. They enable AI systems to measure similarity between pieces of code, search codebases by meaning rather than keywords, and power retrieval-augmented generation (RAG) systems.
Vector Database
A vector database is a specialized database designed to store, index, and search high-dimensional embedding vectors efficiently. Unlike traditional databases that match exact values or keywords, vector databases find the most similar vectors to a query vector—enabling semantic search, recommendation systems, and the retrieval component of RAG (retrieval-augmented generation) architectures.
Technical Debt
Technical debt is the implied cost of future rework caused by choosing a quick, expedient solution now instead of a better approach that would take longer. Like financial debt, it accumulates interest: the longer it remains unaddressed, the more time and effort future changes require. Common sources include rushed features, skipped tests, outdated dependencies, and inconsistent architecture.