TL;DR: The most useful provenance is actionable provenance. Instead of storing prompts like a dusty audit log, surface them where decision-makers work: the code review. Recent UX and correlation work in LineageLens — sidebar captures, drag/drop, click-to-insert, and a confidence engine — demonstrate how provenance can shorten review cycles and reduce reverts.
The problem (why it matters)
By 2026, AI is a first-class development tool. Good suggestions become accepted edits, then commits. When reviewers see unfamiliar code they ask the obvious questions: who wrote this, why was it accepted, and was it audited? Git blame shows an author, but not the conversational context that generated the code. That missing context causes three predictable costs:
Time to reproduce: reviewers re-run prompts or attempt to reproduce edits.
Conservative reverts: unknown edits get reverted, losing useful fixes.
