Building a Production RAG Pipeline: Lessons from Real-World AI Apps

Building a Production RAG Pipeline: Lessons from Real-World AI Apps RAG (Retrieval-Augmented Generation) sounds simple on paper — embed your documents, store them in a vector DB, retrieve the relevant chunks, and pass them to an LLM. In practice, getting a RAG pipeline to production quality is significantly harder. Here's what I learned building RAG pipelines for real SaaS products. The Naive Implementation Most tutorials show you this flow: Chunk your documents Embed them with OpenAI Store in Pinecone Retrieve top-k chunks Pass to GPT-4 This works fine in demos. It fails in production for a few key reasons. Problem 1: Chunking Strategy Kills Retrieval Quality Naive fixed-size chunking (every 512 tokens) destroys semantic context. A paragraph about "authentication" gets split mid-sentence, and your retrieval picks up half-relevant chunks. What works: Semantic chunking — split at natural sentence and paragraph boundaries, and use overlapping windows so context carries across chunk bound