AI Systems · 8 min read
How to Build a RAG Pipeline for Enterprise Knowledge Bases
RAG works best when the retrieval layer is designed as a product system instead of a simple vector search demo.
Start with the data shape
Define what the source documents look like, how often they change, and which metadata matters for filtering later.
Treat chunking as a design decision
Chunking changes recall, cost, and citation quality. The best approach depends on document structure and user intent.
Measure retrieval quality
Track hit rate, source coverage, and answer grounding instead of assuming the model is working because it sounds fluent.