AI 开发 教程
系统化的 AI 开发学习路径。从提示词工程到模型微调，从 RAG 到 Agent 开发，覆盖 AI 应用开发全栈知识。共 142 篇教程，18 个分类，入门到高级全覆盖。
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DeepSeek V4 Thinking Mode: The Complete Guide
DeepSeek V4's core innovation—Thinking Mode—allows the model to reason deeply before answering. This article details the thinking.type, reasoning_effort parameters, chain-of-thought extraction, and non-thinking/thinking dual-mode switching strategies, with complete Python/Node.js code examples.
阅读教程 →FIM Code Completion: Making DeepSeek Your AI Programming Assistant
Fill-in-the-Middle (FIM) is a core technology for code completion. DeepSeek V4 supports native FIM completion and can be integrated into editors such as VS Code and Neovim. This article details FIM principles, API calls, and editor integration.
阅读教程 →Streaming Output in Practice: SSE Handling and Frontend Rendering
In production environments, streaming output is key to improving user experience. This article details DeepSeek API's SSE streaming response handling, including Python/Node.js implementations, frontend word-by-word rendering, thinking mode streaming parsing, and interruption handling.
阅读教程 →Tool Calls in Practice: The Complete Guide to DeepSeek V4 Function Calling
Function Calling is the core mechanism connecting AI to the real world. This article details DeepSeek V4's tool_calls usage, including parallel calls, strict mode, error handling, and multi-tool orchestration, with complete Python/Node.js code.
阅读教程 →JSON Mode Structured Output: From Schema to Production
DeepSeek V4 natively supports JSON Mode, ensuring model output strictly conforms to JSON Schema. This article details the response_format parameter, structured output validation, Pydantic integration, and production deployment strategies.
阅读教程 →Efficient Use of 1M Context: Strategies for Multi-Turn Dialogues and Long Document Processing
DeepSeek V4 supports a 1M token context window (approximately 700,000 Chinese characters). This article details how to efficiently utilize long contexts, including message truncation strategies, summary compression, chunked processing, and best practices for large document analysis.
阅读教程 →DeepSeek V4 Flash Performance In-Depth Review
DeepSeek V4 Flash rivals GPT-5 performance at an ultra-low price of ¥0.02/M tokens. This article provides objective selection advice based on real tests across six dimensions: coding, reasoning, mathematics, multilingual, latency, and concurrency.
阅读教程 →Long Context in Practice: KV Cache and 1M Token Application Design
The 1M context makes it possible to 'stuff in an entire manual', but processing long contexts has many engineering pitfalls. This article explains KV Cache mechanisms, long document chunking strategies, token budget management, and practical designs for multi-turn conversations.
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