Retrieval-augmented generation (RAG) has become the default pattern for grounding large language models in your own data. But the gap between a compelling demo and a reliable production system is wide.
The three failure modes we see most often are poor retrieval, weak evaluation, and missing guardrails. Fix these and you're most of the way to a system your team can trust.
Start with retrieval quality: chunking strategy, embedding choice, and re-ranking matter more than the model. Then invest in evaluation — you cannot improve what you do not measure. Finally, add guardrails and citations so answers stay grounded and auditable.