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“Monitor config validation and service startup errors” concerns No configuration maps entities to SQL objects or runtime connections; the reported problem instead is One-off SQL triggers cannot safely call external systems. Monitor retries, dead-letter states and authorization.

“Monitor GraphQL query cost and relationship failures” concerns GraphQL schema lacks relationship mapping despite SQL foreign key; the reported problem instead is One-off SQL triggers cannot safely call external systems. Monitor retries, dead-letter states and authorization.

“Monitor missing correlation and sampling errors” concerns HTTP response latency is available but SQL dependency duration is missing; the reported problem instead is One-off SQL triggers cannot safely call external systems. Monitor retries, dead-letter states and authorization.

“Review model hosting, region and data processing terms” concerns A third-party model region is outside allowed residency; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Review fields for semantic content and sensitive information.

“Inspect document identifiers, content hashes, model and timestamp metadata” concerns Expired policy versions may be presented as current; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Review fields for semantic content and sensitive information.

“Inspect modality of source content and model input capabilities” concerns Text-only embeddings do not reflect visual diagrams; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Review fields for semantic content and sensitive information.

“Use a lightweight trigger to enqueue work instead of blocking on model inference” concerns Synchronous model calls in a DML trigger would slow transactions; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Embed relevant title/body text while excluding identifiers and confidential fields.

“Use approved Logic Apps workflow for gated embedding generation” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Embed relevant title/body text while excluding identifiers and confidential fields.

“Use Azure Function triggered by SQL changes to compute and write embeddings” concerns Heavy embedding calls cannot safely run in every SQL write transaction; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Embed relevant title/body text while excluding identifiers and confidential fields.

“Reembed a test document after changing one paragraph” concerns Expired policy versions may be presented as current; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Compare retrieval on synthetic document title-only versus title-plus-body embeddings.

“Try insert/search with two test embeddings of known lengths” concerns A replacement embedding model produces vectors of a different length; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Compare retrieval on synthetic document title-only versus title-plus-body embeddings.

“Compare individual and small-batch requests using synthetic strings” concerns Endpoint overhead dominates ingestion time; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Compare retrieval on synthetic document title-only versus title-plus-body embeddings.

“Only approved content is embedded with audit evidence” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Relevant support paragraphs rank higher without embedding protected details.

“Relevant diagrams are retrievable without mislabeling text” concerns Text-only embeddings do not reflect visual diagrams; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Relevant support paragraphs rank higher without embedding protected details.

“Transaction completes quickly and embedding job eventually updates” concerns Synchronous model calls in a DML trigger would slow transactions; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Relevant support paragraphs rank higher without embedding protected details.

“Monitor capture lag and duplicate events” concerns Embedding jobs miss deleted records; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Monitor retrieval misses and unintended sensitive fields.

“Monitor language-level recall drift” concerns The original embedding model performs poorly on non-English text; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Monitor retrieval misses and unintended sensitive fields.

“Monitor unapproved workflows and approval latency” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Embedding customer IDs and secrets would add noise and risk. Monitor retrieval misses and unintended sensitive fields.

“Inspect approval path and data governance policy” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Whole-document embedding obscures section-specific answers. Inspect document structure, headings and typical query granularity.

“Inspect document identifiers, content hashes, model and timestamp metadata” concerns Expired policy versions may be presented as current; the reported problem instead is Whole-document embedding obscures section-specific answers. Inspect document structure, headings and typical query granularity.

“Inspect CDC capture and downstream idempotency handling” concerns Embedding jobs miss deleted records; the reported problem instead is Whole-document embedding obscures section-specific answers. Inspect document structure, headings and typical query granularity.

“Use Azure Function triggered by SQL changes to compute and write embeddings” concerns Heavy embedding calls cannot safely run in every SQL write transaction; the reported problem instead is Whole-document embedding obscures section-specific answers. Split material into meaningful overlapping chunks with stable references.

“Use approved Logic Apps workflow for gated embedding generation” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Whole-document embedding obscures section-specific answers. Split material into meaningful overlapping chunks with stable references.

“Use supported CDC stream to update or delete corresponding vectors” concerns Embedding jobs miss deleted records; the reported problem instead is Whole-document embedding obscures section-specific answers. Split material into meaningful overlapping chunks with stable references.

“Reembed a test document after changing one paragraph” concerns Expired policy versions may be presented as current; the reported problem instead is Whole-document embedding obscures section-specific answers. Test paragraph and section chunking on fictional policy text.

“Insert/update/delete test items through the CDC path” concerns Embedding jobs miss deleted records; the reported problem instead is Whole-document embedding obscures section-specific answers. Test paragraph and section chunking on fictional policy text.

“Benchmark bilingual query/document pairs with fictional examples” concerns The original embedding model performs poorly on non-English text; the reported problem instead is Whole-document embedding obscures section-specific answers. Test paragraph and section chunking on fictional policy text.

“Only approved content is embedded with audit evidence” concerns Automated embedding updates currently ignore content classification; the reported problem instead is Whole-document embedding obscures section-specific answers. Queries retrieve the right section without losing required context.

“Vector store reflects each committed source operation” concerns Embedding jobs miss deleted records; the reported problem instead is Whole-document embedding obscures section-specific answers. Queries retrieve the right section without losing required context.

“Stored vector version corresponds to newest source text” concerns Heavy embedding calls cannot safely run in every SQL write transaction; the reported problem instead is Whole-document embedding obscures section-specific answers. Queries retrieve the right section without losing required context.

“Monitor language-level recall drift” concerns The original embedding model performs poorly on non-English text; the reported problem instead is Whole-document embedding obscures section-specific answers. Monitor long chunks, orphan citations and recall.

“Monitor capture lag and duplicate events” concerns Embedding jobs miss deleted records; the reported problem instead is Whole-document embedding obscures section-specific answers. Monitor long chunks, orphan citations and recall.

“Monitor modality-specific recall and processing errors” concerns Text-only embeddings do not reflect visual diagrams; the reported problem instead is Whole-document embedding obscures section-specific answers. Monitor long chunks, orphan citations and recall.