rfp-review

To capture reviewer corrections for flagged RFP responses, you can triage a prioritized review queue and log structured corrections. This process feeds a continuous learning loop to improve your knowledge base.

An RFP learning loop is a process that logs human reviewer corrections for items with FAIL or WARN gate verdicts. It surfaces mixed-confidence responses for triage and exports corrections to drive iterative knowledge base updates.

You build a review queue by consuming responses.json and gate_verdict.json to present a prioritized list of flagged RFP items. This focuses reviewer effort on mixed-confidence responses requiring structured triage.

Yes, you can render reviewer UI components including the review queue and per-item cards using Cowork Adaptive Cards. This provides an interactive interface for triaging flagged RFP responses and maintaining an audit trail.

The best way to export RFP corrections for knowledge base updates is to log entries to a corrections.jsonl file and roll them up into a corrections_export.json payload. This standardized format enables seamless iterative updates.

You should not use an automated RFP review workflow when your initial RFP responses yield high confidence and pass gate checks without warnings. The process specifically targets mixed-confidence outputs requiring human triage.