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 Prompt Engineering Tutorial
DeepSeek-specific prompt engineering guide: chain-of-thought reasoning, few-shot learning, structured output, and function calling. Covers prompt templates and optimization strategies for scenarios such as mathematical reasoning, code generation, and text analysis.
阅读教程 →Prompt Compression and Semantic Caching Engineering in Practice
Overly long prompts increase costs and reduce response speed. This article provides an in-depth explanation of prompt compression techniques and semantic caching practices, teaching you how to compress repetitive and lengthy prompts by over 60% and speed up similar requests by 10x through cache hits.
阅读教程 →Structured Prompt Design Patterns
Prompting is not just about writing a few instructions to get high-quality output. This article systematically explains six proven structured prompt design patterns, including role pattern, template pattern, constraint pattern, step-by-step pattern, reflection pattern, and combination pattern, helping you upgrade prompt engineering from intuition to methodology.
阅读教程 →Multi-turn Dialogue Prompt Strategies
Multi-turn dialogue is the most common interaction form in AI applications, but maintaining the quality and consistency of long conversations is a huge challenge. This article deeply analyzes core technologies such as context window management, memory compression, topic tracking, and dialogue state management to help you build smooth and intelligent multi-turn dialogue systems.
阅读教程 →Prompt Version Management and A/B Testing
Prompts are the soul of AI applications and, like code, require version management and A/B testing. This article explains a complete solution for prompt version control, experiment design, effect evaluation, and progressive rollout.
阅读教程 →Best Practices for System Prompt Engineering
System prompts are a key mechanism for controlling AI behavior. This article explains the design principles, role setting, constraint expression, structured output, and common pitfalls of system prompts, helping you write more precise prompts.
阅读教程 →Chain-of-Thought (CoT) and Few-shot Prompting Techniques
Chain-of-Thought and Few-shot prompting are key techniques for improving the reasoning ability of large models. This article provides an in-depth explanation of the principles, usage methods, and best practices of these two techniques, helping developers build smarter AI applications.
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