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AI Coding Glossary
Key terms in AI-assisted development, explained clearly with practical context.
Agentic 程式設計
Agentic 程式設計是一種軟體開發方式，AI 代理能夠自主讀取程式碼庫、撰寫程式碼、執行指令並迭代結果，無需手動複製貼上。與對話式 AI 不同，代理直接在你的開發環境中執行操作，完成多步驟任務。
Claude Code
Claude Code 是 Anthropic 基於終端機的 AI 程式設計代理，直接在你的開發環境中運作。它能讀取整個專案、跨多個檔案撰寫程式碼、執行 Shell 指令、管理 Git 工作流程，並自主迭代處理錯誤——全程透過命令列完成。
模型脈絡協議（MCP）
模型脈絡協議（MCP）是 Anthropic 建立的開放標準，提供了一種將 AI 模型連接到外部工具、資料來源和 API 的通用方式。它像「AI 的 USB 介面」一樣充當標準化介面，使任何相容 MCP 的工具都能與任何相容 MCP 的 AI 代理協作。
CLAUDE.md
CLAUDE.md 是放置在專案根目錄中的 Markdown 設定檔，為 Claude Code 提供持久化的專案專屬指示。它向代理說明程式碼規範、架構、常用指令和規則，充當適用於該專案每次工作階段的長期記憶。
AI 配對程式設計
AI 配對程式設計是一種開發工作流程，人類開發者與 AI 工具即時協作撰寫程式碼。開發者提供方向、脈絡和判斷，AI 則貢獻程式碼建議、捕捉 Bug 並處理重複性實作任務。
脈絡視窗
脈絡視窗是 AI 模型在單次互動中能處理的最大 Token（詞語、程式碼字元和符號）數量。它定義了 AI 能同時在記憶中保留的資訊上限——包括你的提示詞、程式碼和模型的回覆。
程式設計代理
程式設計代理是由 AI 驅動的工具，能夠自主讀取檔案、撰寫程式碼、執行終端機指令並迭代結果，以完成程式設計任務。與被動的程式碼建議工具不同，程式設計代理在你的開發環境中採取獨立行動，以達成既定目標。
Vibe 程式設計
Vibe 程式設計是一種非正式的軟體開發方式，開發者用自然語言描述想要的效果，讓 AI 工具處理實作細節。不同於撰寫精確規格，開發者透過隨意的對話傳達意圖，並根據結果迭代調整。
AI 程式碼審查
AI 程式碼審查是利用人工智慧自動分析原始碼中的 Bug、安全漏洞、風格不一致和品質問題的過程。AI 審查工具可以檢查拉取請求、提出改進建議，並發現人工審查者因疲勞或時間壓力可能遺漏的問題。
程式碼提示工程
程式碼提示工程是精心設計清晰、具體指示的實踐，幫助 AI 程式設計工具產出準確、相關的結果。它涉及以適當的脈絡、限制條件和範例來建構你的請求，以便 AI 理解你想要什麼以及你希望如何實現。
無頭 AI 代理
無頭 AI 代理是指在沒有面向使用者介面或即時互動的情況下運作的程式設計代理。它在背景程序、CI/CD 流水線或排程任務中自主執行任務——讀取程式碼、做出修改、執行測試並回報結果，全程無需人工輸入。
子代理
子代理是由主 AI 程式設計代理派生的並行子程序，用於同時處理複雜任務中相互獨立的部分。主代理不必按順序處理所有任務，而是將子任務委派給專門的子代理並行執行，各子代理完成後將結果回報給父代理。
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.