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 15 篇教程
DeepSeek RAG Retrieval-Augmented Tutorial
Complete tutorial for building a RAG system based on DeepSeek: document loading, text splitting, vector retrieval, re-ranking, and multi-turn dialogue integration. Includes LangChain and LlamaIndex practical code and enterprise-level RAG architecture design.
阅读教程 →RAG Re-ranking and Hybrid Search in Practice: From BM25 to Cross-Encoder
Vector retrieval alone has limited recall; reranking is a key step in improving RAG effectiveness. This article systematically explains BM25 sparse retrieval, vector retrieval with RRF fusion, and complete practices for Cross-Encoder reranking.
阅读教程 →Vector Database Selection in Practice
Vector databases are the core infrastructure of RAG systems, but with many options like Milvus, Pinecone, Weaviate, Qdrant, and Chroma, how do you make the optimal selection decision? This article comprehensively compares performance, cost, ease of use, and scalability, and provides selection recommendations based on real business scenarios.
阅读教程 →Deep Optimization of Document Chunking Strategies
Document chunking is the most underestimated critical step in RAG systems. The quality of the chunking strategy directly affects retrieval quality and the accuracy of the final answer. This article deeply analyzes strategies such as fixed-size chunking, semantic chunking, recursive chunking, and sentence-level chunking to help you find the optimal solution.
阅读教程 →Getting Started with Multimodal RAG
Traditional RAG can only handle text, but real-world data is far more than text. Multimodal RAG incorporates non-text data such as images, tables, and audio into the retrieval-augmented scope, enabling AI to understand richer information forms. This article takes you from scratch to understand the design philosophy and implementation path of multimodal RAG.
阅读教程 →RAG Evaluation and Quality Metrics System
Without measurement, there is no improvement. Quality evaluation of RAG systems involves three levels: retrieval quality, generation quality, and end-to-end quality. This article systematically explains core evaluation metrics and frameworks such as Recall@k, MRR, Faithfulness, and RAGAS to help you establish a scientific RAG quality measurement system.
阅读教程 →Agentic RAG: Let AI Retrieve Autonomously
Traditional RAG is passive—the system retrieves what the user asks. Agentic RAG gives AI autonomous decision-making ability, enabling it to proactively determine whether retrieval is needed, what to retrieve, and how to integrate multiple retrieval results. This article deeply analyzes the architecture design and implementation path of Agentic RAG.
阅读教程 →Graph-Enhanced RAG (GraphRAG) in Practice
Traditional RAG only retrieves text fragments; GraphRAG introduces knowledge graphs on this basis, enabling multi-hop reasoning and relation-aware retrieval. This article explains the construction and practical application of GraphRAG from scratch.
阅读教程 →Design of Multilingual RAG Systems
Building a RAG system that supports multiple languages such as Chinese, English, Japanese, and Korean presents unique challenges. This article explains a complete solution for multilingual embeddings, cross-lingual retrieval, translation strategies, and performance optimization.
阅读教程 →RAG Caching and Performance Optimization
The performance bottleneck of a RAG system is often not the model but the retrieval. This article explains multi-level caching strategies, Redis acceleration, semantic caching, and batch optimization techniques to increase your RAG system's response speed by 10 times.
阅读教程 →Large Model Hallucination: Root Cause Analysis and Mitigation Strategies
Hallucination is one of the most troublesome problems in large model applications. This article provides an in-depth analysis of the causes of hallucination and offers systematic mitigation strategies such as RAG, fact-checking, and uncertainty quantification to help build more reliable AI applications.
阅读教程 →Building an Enterprise-Grade AI Knowledge Base System from Scratch
Enterprise knowledge bases are the best entry point for AI implementation. This article comprehensively explains how to build an enterprise-level AI knowledge base system that supports multi-format documents, permission management, and continuous updates, from requirements analysis to system deployment.
阅读教程 →Advanced RAG: Multi-hop Retrieval and Self-Query
Basic RAG cannot handle complex problems that require cross-document reasoning. This article provides an in-depth explanation of advanced RAG techniques such as multi-hop retrieval, self-query retrieval, and context compression, helping you elevate your RAG system to production-grade level.
阅读教程 →Vector Database Selection: Chroma vs Pinecone vs Milvus
Vector databases are core components of RAG systems. This article compares three mainstream vector databases—Chroma, Pinecone, and Milvus—from four dimensions: performance, ease of use, cost, and scalability, helping you make the right selection decision.
阅读教程 →RAG from Beginner to Production: Complete Tech Stack
RAG (Retrieval-Augmented Generation) is the core paradigm for current AI application development. Starting from scratch, this article systematically explains the principles, architecture, and technology stack of RAG, taking you from conceptual understanding to building a usable RAG application.
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