Question

You maintain an Azure AI Search enrichment pipeline that ingests 200,000 scanned PDFs. The skillset runs OCR, a custom Web API skill for entity extraction, and text splitting. During development you frequently modify only the text-splitting skill's parameters and re-run the indexer, but every run re-executes the expensive OCR and custom Web API skills against all documents, driving up costs and runtime. You need to reduce redundant billed skill executions across iterative runs while keeping unchanged upstream enrichments intact. What should you do?

Accepted Answer

Enable incremental enrichment by configuring an enrichment cache (cache storage connection) on the indexer definition so only changed skills and their downstream skills re-execute.

Answer

Set the indexer schedule to run less frequently and increase the batch size so fewer total runs occur.

Answer

Add a conditional skill before OCR that skips documents already present in the index based on their document key.

Answer

Switch the OCR and custom Web API skills to run in a separate skillset and merge the outputs with a knowledge store projection.