About the Book
Build reliable, real world RAG systems with Claude, LangChain, and modern vector databases that scale from prototype to production. Enterprises want accurate, traceable answers, not guesswork. This handbook shows how to design retrieval augmented generation that is fast, auditable, and cost aware, so teams in finance, healthcare, manufacturing, and retail can ship with confidence.
You will move from concepts to hands-on patterns: ingestion, embeddings, vector search, reranking, evaluation, governance, and production operations. Every chapter is engineered for practical use, with defaults that work, pitfalls to avoid, and checklists you can run in CI.
What you will learn
How to design end to end RAG pipelines with LangChain and Claude, from loaders and splitters to retrievers, rerankers, and prompts
Which vector database to pick and why: FAISS for on premise control, Pinecone for managed scale, Milvus and Qdrant for cloud native or cost sensitive stacks
Chunking, hybrid retrieval, contextual compression, and cross-encoder reranking that raise hit rate and precision
Evaluation that sticks: faithfulness, groundedness, hit rate, latency budgets, and how to monitor them with dashboards and alerts
Security and compliance in production: guardrails, PII redaction, RBAC, audit logging, and data residency routing
Scaling patterns that last: GPU indexing, sharding, replication, multi-region and multi-cloud federation
Cost control that does not hurt quality: prompt caching, index compression, caching strategies, and SLO driven design
Long term maintenance: templates, ADRs, and SLOs that make pipelines repeatable and resilient
Included extras
Yes, this book ships with practical add-ons that speed learning and adoption:
Cheat Sheet: copy-ready rules of thumb, code snippets, and defaults you can paste into your pipeline
Flashcards: quick checks to reinforce core ideas before go-live
Key Takeaways: concise recaps at chapter ends for fast review
Index: precise definitions with section references, so you can jump straight to the right page
Common Mistakes and Application Tips: what to avoid, what to try next
Code content
Yes, it is a code friendly guide. You will find working snippets for FAISS GPU indexing, product quantization, LangChain retrievers and rerankers, Pinecone and Milvus setup, prompt templates, alert rules, and evaluation harnesses. Use them as is, then adapt to your stack. Why this handbook stands out
Short build cycles at the end of each topic help you implement as you read. Real industry scenarios show how to apply the same patterns in finance, healthcare, manufacturing, and retail. Production topics are treated as first class: observability, incident alerting, compliance, and cost are baked into every design choice.
Ready to deliver trustworthy AI in production
If you want fluent answers that are grounded, auditable, and fast, this book is your roadmap. Grab your copy today and start shipping RAG systems your teams and customers can trust.
