Azure AI Search

The service organizes searchable content through schemas, indexing and query interfaces. Full-text retrieval uses text indexing, vector retrieval compares numerical representations, and hybrid retrieval combines approaches with configured ranking. Current service documentation also describes agentic retrieval, which adds planning and multi-source workflows; this is distinct from a classic query against an index. A search service supplies candidate evidence, not a guarantee that an answer generated from it is correct. Chunking, metadata, embedding versions and permission representation determine what can be found and filtered.

The practitioner defines document keys, searchable and filterable fields, chunk boundaries and update semantics. They choose query modes from representative questions, measure retrieval quality and tune ranking without losing required access filters. They configure identities and network access, monitor ingestion and handle deletions or stale content. Useful work yields a searchable corpus and retrieval interface with tested relevance and permission behavior, including evidence that updates reach the index and that unauthorized documents cannot appear through alternative query paths.

Semantic similarity can retrieve a related but inapplicable procedure, while stale indexing can preserve obsolete advice. Access control needs the selected feature's documented configuration; a metadata field alone is not enforcement. New retrieval features and capacity limits vary by service settings. Check relevance, freshness, deletion and permissions separately. Azure AI Search is a retrieval component, so grounding an assistant also requires generation evaluation and source presentation.