Building a RAG Pipeline From Scratch With LangChain + Pinecone + Claude: A Real Implementation

Building a RAG Pipeline From Scratch With LangChain + Pinecone + Claude: A Real Implementation Most RAG tutorials use a 10-page PDF about Shakespeare and call it a day. You get a working demo in 20 minutes, deploy nothing, and learn the one thing that least resembles production: that RAG is easy. It isn't. The demo is easy. Production RAG — where your retrieval actually returns the right chunks, your answers are grounded in the source, and the system doesn't hallucinate when it can't find an answer — takes deliberate engineering at every stage of the pipeline. This is a real implementation guide. We'll build a RAG pipeline using LangChain, Pinecone, and Claude that could actually serve a client product. Every decision explained, every gotcha documented. What you'll have at the end: A working RAG system that ingests a document corpus, chunks it intelligently, embeds it into Pinecone, retrieves with hybrid search, generates grounded answers with Claude, and evaluates itself. Prerequisite