Six Lessons Learned Building RAG Systems in Production

Building production-ready Retrieval-Augmented Generation (RAG) systems requires focusing on real business problems, as a poorly implemented system can permanently erode user trust. Success depends heavily on meticulous data preparation and effective chunking strategies that preserve semantic context, because retrieval quality is the most critical component. Systems must also account for evolving source data by updating embeddings and implement a rigorous, multi-level evaluation framework to measure performance proactively. Ultimately, teams should prioritize value by choosing simpler, effective architectures over trendy, complex ones that may not fit the specific problem.