Building a RAG Pipeline That Actually Works

Most RAG tutorials show 20 lines of LangChain and call it production-ready. Then you try it on real documents and get garbage results. Here's what every tutorial shows: // The "tutorial RAG" that doesn't work const loader = new DirectoryLoader ( ' ./docs ' ); const documents = await loader . load (); const textSplitter = new CharacterTextSplitter ({ chunkSize : 1000 , chunkOverlap : 0 }); const docs = await textSplitter . splitDocuments ( documents ); const vectorstore = await Chroma . fromDocuments ( docs , new OpenAIEmbeddings ()); const retriever = vectorstore . asRetriever (); const chain = RetrievalQAChain . fromLLM ( new OpenAI (), retriever ); This works great on the AI papers the tutorial author tested it on. Try it on your company's actual documents and you'll get irrelevant results, missed information, and confused users. At my startup, we process thousands of documents daily. Contracts, manuals, reports, presentations. I've spent 6 months building RAG that actually works in