{"product_id":"rag-with-langchain-agentic-john-cutts-9798182297936","title":"RAG With Langchain \u0026 Agentic AI: LangChain Explained So Simply a 5-Year-Old Could Build An Autonomous AI Agent","description":"\u003cp\u003e\u003cb\u003eMove Beyond \"Hello World\" Tutorials. Stop Hallucinations, Fix Wrong Retrieval, and Build Production-Grade AI Systems That Don't Break at Scale.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eHave you ever followed a basic AI tutorial, watched a naive Retrieval-Augmented Generation (RAG) system work flawlessly on ten sample documents, and shipped it to production with absolute confidence-only to watch it completely fall apart three weeks later under the weight of ten thousand real-world files?\u003c\/p\u003e\u003cp\u003eWhen retrieval fails, answers hallucinate, and users get frustrated, most developers immediately blame the Large Language Model. But the brutal truth is that \u003cb\u003e90% of RAG failures are retrieval failures, not generation failures.\u003c\/b\u003e The LLM cannot synthesize a correct answer if you feed it the wrong context.\u003c\/p\u003e\u003cp\u003eIn \u003cb\u003eRAG with LangChain \u0026amp; Agentic AI: LangChain Explained So Simply a 5-Year-Old Could Build An Autonomous AI Agent\u003c\/b\u003e, software engineer and AI systems architect John Cutts delivers the definitive 2026 operational playbook for transitioning from fragile prototypes to resilient, enterprise-grade AI infrastructure. This book completely eliminates the \"chunk-and-pray\" methodology and replaces it with deterministic, self-correcting engineering patterns.\u003c\/p\u003eWhat You Will Master Inside This Complete Playbook: \u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Core RAG Architecture: \u003c\/b\u003e Master the three pillars-Indexing, Retrieval, and Generation-using Python, LangChain, and ChromaDB to separate knowledge from frozen LLM reasoning data.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Art of Chunking: \u003c\/b\u003e Learn why chunking is architecture, not pre-processing. Navigate the chunking decision framework to deploy Fixed, Recursive, and Semantic splitters that preserve critical data context.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe 5 Production Failure Modes: \u003c\/b\u003e Acquire a practical debugging playbook to diagnose bad chunking, poor prompt engineering, wrong embedding models, insufficient context ($k$ too small), and separate retrieval testing layers.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eAnthropic's Contextual Retrieval: \u003c\/b\u003e Implement cutting-edge chunk enrichment strategies that reduce retrieval failures by up to 67% by combining LLM-generated context with cross-encoder reranking.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eAgentic RAG with LangGraph: \u003c\/b\u003e Replace static pipelines with stateful, self-correcting autonomous decision-making loops that grade documents, rewrite failing queries, and execute graceful fallbacks.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eGraph RAG \u0026amp; Multi-Hop Reasoning: \u003c\/b\u003e Transcend standard vector search boundaries using Microsoft's GraphRAG framework to extract complex networks of entities and relationships for multi-document insights.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eMultimodal RAG with ColPali: \u003c\/b\u003e Stop stripping out critical corporate assets. Use vision-language models to embed page layouts, charts, schematics, and complex tables directly as visual images.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Production Runbook: \u003c\/b\u003e Gain complete system observability using LangSmith tracing, deploy massive cost-management tactics (saving up to 25x vs long-context windows), and structure hybrid text-vision pipelines.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003ci\u003e\"The gap between 'it works in the demo' and 'it works in production' is exactly as wide as the gap between naive RAG and the system you have learned to build in this book.\"\u003c\/i\u003e - John Cutts\u003c\/p\u003e\u003cp\u003eWhether you are a software engineer, data scientist, or technical builder, this book skips the theoretical fluff and delivers raw, working code derived from hard-won production experience.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e John Cutts\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798182297936\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/19\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 90\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.29lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.19d","brand":"John Cutts","offers":[{"title":"Paperback","offer_id":48874759028991,"sku":"9798182297936","price":10.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/rag-with-langchain-agentic-john-cutts-9798182297936","provider":"WR Book House","version":"1.0","type":"link"}