Build a RAG Pipeline in Python That Actually Works

Most RAG tutorials teach you to stuff documents into a vector store and call it a day. Then your users ask a question and get back completely wrong answers because the retriever pulled the wrong chunks. Retrieval Augmented Generation is the most common pattern in production AI systems. It lets an LLM answer questions using your own data — internal docs, codebases, knowledge bases — without fine-tuning. The concept is straightforward: retrieve relevant documents, feed them to the model, get grounded answers. The implementation is where teams struggle. Bad chunking produces fragments that lose context. Naive retrieval returns semantically similar but factually irrelevant results. And most tutorials stop before showing you how to evaluate whether your pipeline actually works. This guide walks through 4 patterns that make RAG pipelines reliable. Every code example uses LangChain (as of v0.3+, March 2026), runs on Python 3.10+, and is verified against the official documentation. What You Ne