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。
找到 14 篇教程
DeepSeek Multimodal Application Tutorial
In-depth analysis of DeepSeek's multimodal capabilities: visual understanding, image generation, document analysis, and OCR recognition. Includes tutorials for DeepSeek-VL2 and Janus series models, and practical development of multimodal RAG and visual agents.
阅读教程 →DeepSeek + LangChain Development Tutorial
DeepSeek and LangChain Integration Development Tutorial: Chain Construction, Agent Development, Memory Management, and Tool Integration. Includes complete LangChain project practice, with a progressive learning path from basic Chains to complex Agent systems.
阅读教程 →DeepSeek Open Source Project Hands-on
Collection of Practical Open-Source Projects Based on DeepSeek: Intelligent Customer Service System, Code Review Assistant, Document Q&A Bot, AI Search, and Data Analysis Assistant. Each project includes complete code, architecture design, and deployment guide, ready for production use.
阅读教程 →DeepSeek Development Tools Ecosystem
DeepSeek Development Tool Ecosystem Overview: IDE Plugins, Command-Line Tools, Debugging Tools, Prompt Management, and Evaluation Frameworks. Covers DeepSeek integration solutions and productivity tools for mainstream development environments such as VSCode, JetBrains, and Cursor.
阅读教程 →AI Workflow Engine in Practice: From Scripts to Visual Orchestration
Complex AI applications should not be a pile of coupled scripts. This article explains how to build a lightweight AI workflow engine, including node abstraction, conditional branching, loop execution, and breakpoint recovery, with a complete runnable Python implementation.
阅读教程 →RAG Agent Application Implementation: From Knowledge Base to Autonomous Decision-Making
The combination of RAG and Agents is reshaping enterprise knowledge applications. This article builds a production-grade RAG Agent step by step, from knowledge base construction, retrieval augmentation, tool calling to autonomous decision-making, and discusses evaluation and iteration methods.
阅读教程 →Test-Driven Development for LLM Applications: Practical Prompt Regression Testing
Prompts are changed repeatedly, but how can we ensure that production behavior does not regress? This article introduces TDD to LLM applications: from assertion-based unit tests, regression test suites to Golden Sets and evaluation pipelines, building a maintainable AI application quality system.
阅读教程 →Advanced Prompt Engineering: Structured Prompts, CoT, Self-Reflection
Explore advanced prompt engineering paradigms: structured prompts, chain-of-thought, self-reflection, and least-to-most. Includes DeepSeek API examples and evaluation sets for measurable, iterative optimization.
阅读教程 →DeepSeek API in Action: From Getting Started to Production Deployment
In-depth understanding of DeepSeek API features, from basic calls to production best practices. Covers core topics such as streaming output, Function Calling, rate limits, error retries, and multi-model switching.
阅读教程 →Building an AI Code Review System
Code review is an indispensable part of software development, but manual review is time-consuming and inconsistent in quality. This article teaches you step by step how to build an automated code review system using the DeepSeek API, covering security vulnerability detection, performance analysis, code style checking, and best practice suggestions.
阅读教程 →Building a Smart Customer Service Agent from Scratch
Intelligent customer service is one of the most widespread AI application scenarios. This article builds an intelligent customer service Agent from scratch with intent recognition, knowledge retrieval, ticket creation, sentiment analysis, and human-machine collaboration capabilities, fully covering the core needs of customer service scenarios.
阅读教程 →Voice AI Application Development in Practice
Voice interaction is becoming a new entry point for AI applications. This article starts from the complete chain of ASR speech recognition, LLM dialogue generation, and TTS speech synthesis, and builds an end-to-end voice AI application, covering real-time streaming processing and latency optimization.
阅读教程 →AI-Driven Automated Testing Systems
Using AI to automatically generate test cases, detect bugs, and generate test reports greatly improves software testing efficiency. This article explains the architecture design and core implementation of an AI testing system.
阅读教程 →Multimodal AI Application Development in Practice
Multimodal AI is changing the way humans interact with machines. This article explains the usage of vision-language models (VLMs), image-text understanding and generation, multimodal RAG, and the construction of multimodal agents, enabling you to master the complete skills of multimodal AI development.
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