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“Monitor RLS test failures and filtered-search recall” concerns Top-k results may contain another tenant's content; the reported problem instead is ANN tuning could miss a few true nearest matches. Monitor recall loss after index changes.

“Monitor feature changes and migration incompatibilities” concerns Vector feature support and preview status differ by platform; the reported problem instead is ANN tuning could miss a few true nearest matches. Monitor recall loss after index changes.

“Monitor ANN recall, index health and plan selection” concerns Exact full scans miss API latency target; the reported problem instead is ANN tuning could miss a few true nearest matches. Monitor recall loss after index changes.

“Inspect vector norms and scoring requirement” concerns Magnitude dominates scores when direction matters; the reported problem instead is Vector-only retrieval misses exact part number matches. Inspect queries requiring exact tokens and semantic paraphrases.

“Inspect engine/version and ANN index availability and chosen distance metric” concerns Exact full scans miss API latency target; the reported problem instead is Vector-only retrieval misses exact part number matches. Inspect queries requiring exact tokens and semantic paraphrases.

“Inspect index metric and query plan warnings” concerns Compatible ANN index may not be selected; the reported problem instead is Vector-only retrieval misses exact part number matches. Inspect queries requiring exact tokens and semantic paraphrases.

“Implement supported SQL full-text catalog/index and CONTAINS queries” concerns LIKE contains pattern scans large text columns; the reported problem instead is Vector-only retrieval misses exact part number matches. Combine full-text search with vector search and fuse ranked results.

“Use full-text or equality search for exact regulatory identifiers” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Vector-only retrieval misses exact part number matches. Combine full-text search with vector search and fuse ranked results.

“Use supported vector index with VECTOR_SEARCH latest syntax when applicable” concerns Exact full scans miss API latency target; the reported problem instead is Vector-only retrieval misses exact part number matches. Combine full-text search with vector search and fuse ranked results.

“Query synthetic vectors from tenant A and B under An identity” addresses the tenant-scoped vector retrieval situation—top-k results may contain another tenant's content. The observed failure is: Vector-only retrieval misses exact part number matches. Query synthetic corpus with exact error IDs plus paraphrases.

“Benchmark both against a synthetic ground truth corpus” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is Vector-only retrieval misses exact part number matches. Query synthetic corpus with exact error IDs plus paraphrases.

“Measure recall prior to and after controlled reembedding of test corpus” addresses the embedding index rebuild after model change situation—new query embeddings and historical vectors are incompatible semantically. The observed failure is: Vector-only retrieval misses exact part number matches. Query synthetic corpus with exact error IDs plus paraphrases.

“Chosen retrieval meets pre-agreed relevance and latency thresholds” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Vector-only retrieval misses exact part number matches. Both token-exact and semantically relevant records appear.

“Only exact intended identifier is treated as match” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Vector-only retrieval misses exact part number matches. Both token-exact and semantically relevant records appear.

“Exact nearest item and distance order match reference calculation” concerns Approximate index recall is unnecessary for this dataset; the reported problem instead is Vector-only retrieval misses exact part number matches. Both token-exact and semantically relevant records appear.

“Monitor zero vectors and normalization failures” concerns Magnitude dominates scores when direction matters; the reported problem instead is Vector-only retrieval misses exact part number matches. Monitor hybrid precision and token query failures.

“Monitor RLS test failures and filtered-search recall” concerns Top-k results may contain another tenant's content; the reported problem instead is Vector-only retrieval misses exact part number matches. Monitor hybrid precision and token query failures.

“Monitor recall loss after index changes” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is Vector-only retrieval misses exact part number matches. Monitor hybrid precision and token query failures.

“Inspect vector norms and scoring requirement” concerns Magnitude dominates scores when direction matters; the reported problem instead is Raw score addition unfairly favors one system. Inspect result ranks and score distributions from both retrievers.

“Inspect language, stopword and search requirements” concerns LIKE contains pattern scans large text columns; the reported problem instead is Raw score addition unfairly favors one system. Inspect result ranks and score distributions from both retrievers.

“Inspect index metric and query plan warnings” concerns Compatible ANN index may not be selected; the reported problem instead is Raw score addition unfairly favors one system. Inspect result ranks and score distributions from both retrievers.

“Gate index usage on documented target-platform capability” concerns Vector feature support and preview status differ by platform; the reported problem instead is Raw score addition unfairly favors one system. Use reciprocal rank fusion with documented rank offset.

“Use full-text or equality search for exact regulatory identifiers” concerns Semantic nearest-neighbor may return related but incorrect codes; the reported problem instead is Raw score addition unfairly favors one system. Use reciprocal rank fusion with documented rank offset.

“Use matching cosine metric consistently in index and query” concerns Euclidean distance changes ranking for varying-length vectors; the reported problem instead is Raw score addition unfairly favors one system. Use reciprocal rank fusion with documented rank offset.

“Run query with matching versus mismatching metric in sandbox” concerns Compatible ANN index may not be selected; the reported problem instead is Raw score addition unfairly favors one system. Fuse two small ranked lists and check resulting order.

“Compare vector, full-text and hybrid against synthetic labeled queries” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Raw score addition unfairly favors one system. Fuse two small ranked lists and check resulting order.

“Compare sample ranked pairs under cosine and euclidean” concerns Euclidean distance changes ranking for varying-length vectors; the reported problem instead is Raw score addition unfairly favors one system. Fuse two small ranked lists and check resulting order.

“Compatible index path is selected when available” concerns Compatible ANN index may not be selected; the reported problem instead is Raw score addition unfairly favors one system. Final ranking reflects both signals without mixing incompatible raw units.

“Normalized similarity reflects intended angular relationship” concerns Magnitude dominates scores when direction matters; the reported problem instead is Raw score addition unfairly favors one system. Final ranking reflects both signals without mixing incompatible raw units.

“Chosen retrieval meets pre-agreed relevance and latency thresholds” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Raw score addition unfairly favors one system. Final ranking reflects both signals without mixing incompatible raw units.

“Monitor drift in search quality and zero-result rates” concerns Latency metric alone says nothing about retrieval quality; the reported problem instead is Raw score addition unfairly favors one system. Monitor fusion parameter effects and result stability.

“Monitor recall loss after index changes” concerns ANN tuning could miss a few true nearest matches; the reported problem instead is Raw score addition unfairly favors one system. Monitor fusion parameter effects and result stability.

“Monitor model dimension drift” concerns Queries fail during similarity calculation; the reported problem instead is Raw score addition unfairly favors one system. Monitor fusion parameter effects and result stability.