A practical workflow for diagnosing weak semantic retrieval in AI applications.
Troubleshooting
Relevant documents exist but vector search repeatedly returns weak or irrelevant results.
Correct the confirmed retrieval weakness and validate it against representative queries.
Compare top-k relevance and downstream answer quality before and after the change.
Maintain retrieval evaluation data and monitor relevance regressions.
Inspect the retrieval result itself before blaming generation.
Review top-k results for representative queries. Check chunk boundaries, metadata filters, embedding configuration, index freshness, and whether reranking is needed.
Change one retrieval variable at a time, compare relevance against a fixed test set, and keep the winning configuration versioned.
Learning checkpoint
Mark this guide complete to include it in your local Engineering Journey.
Knowledge path
