Why every RAG project I've built ends up fighting the pipeline — and what I'm doing about it

The pattern that keeps repeating If you've built a RAG application, this probably sounds familiar: You pick an embedding model You set up a vector store You write chunking logic You wire everything together You realize the chunking doesn't work for your use case You rewrite half the pipeline The models are the easy part. The pipeline glue is where projects slow down — and where most teams burn weeks they didn't plan for. A support chatbot needs sentence-level chunks. A legal search tool needs paragraph-level with overlap. An internal knowledge base needs something in between. But every time you change one component, you're rewiring the whole thing. The actual problem It's not that building a RAG pipeline is hard. It's that iterating on one is painful. You pick a chunking strategy, embed a few thousand documents, and your retrieval quality is... okay. Not great. So you want to try a different approach. But that means: Re-processing all your documents Re-generating all your embeddings Ho