Backfill denormalized column live
You're using an AI agent to backfill a new denormalized `last_active_at` column across a 50M-row users table in a live SQL database, then keep it updated going forward. Decompose this into steps with checkpoints, and specify where a human must verify before letting the agent run the next step against production data. The system is read-heavy and can't take downtime.
Implement
audit_backfill_plan(total_rows: int, batch_size: int, batch_ms: int, pause_ms: int, window_minutes: int, blocking_migration: bool) → list[str]Examples
in
[50000000,10000,200,100,120,false]out[]in
[50000000,50000000,900000,0,120,true]out["blocking_migration","batch_too_large","no_throttle"]in
[50000000,10000,200,100,20,false]out["exceeds_window"]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 24 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.