Why it matters
Raj Navakoti’s demand-driven context method turns failed tasks into a checklist for domain experts, then tests the revised answer before promoting knowledge into a shared base. The primary paper and repository expose entity templates and cycle logs. The paper’s nine-cycle retail example is synthetic, and wider convergence remains a hypothesis. It supports a curation workflow, not independent confirmation of the talk’s 300-incident claim.
My takeaway: Start with one bounded incident task. Save the initial answer, obtain expert answers to its knowledge gaps, and rerun it before reviewing the proposed context changes. Use separate held-out incidents to test reuse; track expert time, stale facts, and regressions as well as the number of new entities.