Advanced RAG for Web Developers: From Chunking Optimization to Production Architecture (AI for Web Developers Series)

Take Your RAG System from Prototype to Production ScaleYou built a RAG demo with LangChain. It works on 10 documents. But what happens with 10 million? This advanced guide solves the hard problems: retrieval quality, latency, cost, and scale. Every technique is backed by real production metrics.Advanced Techniques CoveredChunking optimization: semantic, recursive, agentic chunking strategiesHybrid search: combining dense vectors with BM25 keyword searchReranking: Cohere Rerank, ColBERT, cross-encoders for accuracyQuery transformation: HyDE, query expansion, multi-query retrievalMulti-modal RAG: text + images + tables with GPT-4V and Claude 3Agentic RAG: self-correcting retrieval with feedback loopsProduction observability: tracing, evaluation, drift detectionArchitecture PatternsScaling vector search to 100M+ embeddings (sharding, indexing)Caching strategies that cut latency by 80%Cost optimization: when to use small models vs GPT-4Multi-tenant RAG with data isolationA/B testing retrieval pipelines in productionWho This Book Is ForEngineers who built a basic RAG and need to scale itTech leads architecting production AI systemsML engineers optimizing retrieval pipelinesAnyone serious about building AI products that don't hallucinateWhat Makes This Book DifferentNot "RAG 101" repackaged. This is the missing manual for everyone who completed a tutorial and hit the production wall. Every chapter solves a real problem the author encountered building RAG systems at scale.PrerequisitesBasic understanding of RAG concepts (or read "AI RAG for Web Developers" first). Comfortable with Python and Web APIs.Stop fighting your RAG system. Make it work. Read more