Most RAG systems have a retrieval problem. The language model gets the blame, but the real culprit is upstream in the search layer deciding what context the model ever sees in the first place. Fix the retrieval, and the answers fix themselves.

This talk is for developers and ML engineers who have shipped a RAG system with OpenSearch and are wondering why the answers aren't as good as the demos suggested. No retrieval evaluation experience needed.

Through live demos on a real OpenSearch index, the session will show you how to see exactly what your retrieval layer is returning, identify the failure modes that silently kill answer quality, and apply targeted fixes without rearchitecting your stack. You’ll leave knowing where to look, what to measure, and what to do about it.