Question

A logistics company processes three distinct document types (bills of lading, customs declarations, and delivery receipts) that arrive mixed together in a single intake queue. Each type has a different set of fields to extract, but all must be routed to the same downstream RAG grounding store as clean, structured output. You are building the ingestion stage with Azure AI Content Understanding. What is the most appropriate design to extract the correct fields per document type while minimizing misclassified extractions?

Accepted Answer

Create a separate custom analyzer with its own field schema for each document type, add a classification step to route each incoming document to the matching analyzer, then merge the structured outputs into the grounding store.

Answer

Build one analyzer whose field schema is the union of all fields across the three document types, and let it extract every field from every document regardless of type.

Answer

Use the prebuilt document analyzer to produce markdown for all documents and rely on the downstream LLM to infer document type and pull the correct fields at query time.

Answer

Configure a single analyzer with the highest confidence threshold so that only fields present in a given document are returned, eliminating the need to distinguish document types.