Designing RAG Systems That Hold Up in Production

Most RAG demos work because the questions are friendly and the corpus is small. Production is different: documents contradict each other, users phrase questions badly, and the system answers hundreds of queries nobody rehearsed. The difference between a demo and a dependable assistant is rarely the model — it is retrieval quality, grounding discipline, and evaluation.

Retrieval is where most quality is won or lost. Chunking should follow document structure — headings, sections, tables — rather than fixed character counts, and each chunk should carry enough metadata (source, date, section) to filter and cite. Hybrid retrieval that combines semantic search with keyword matching usually beats either alone on technical content, where exact terms like error codes and product names matter.

Grounding rules decide whether the assistant is trustworthy. Answers should cite the passages they came from, and the system needs an explicit refusal boundary: when retrieval confidence is low, saying "I could not find this in the documentation" is the correct output, not a failure. Escalation paths — to a human, a ticket, or a broader search — belong in the design from day one.

Finally, treat evaluation as part of the build, not an afterthought. A fixed set of real questions with expected sources, run against every change to prompts, chunking, or the index, catches regressions before users do. This is the discipline Symbios Lab applies to RAG and assistant projects: retrieval tuned to the corpus, citations by default, and an evaluation set handed over with the system.