E15 · Trace the evidence
Synthetic practice, not company performance.
One decision
Which records support your recommendation?
Run the exact query
WITH b AS (SELECT * FROM v_task WHERE ('all'='all' OR persona='all') AND ('all'='all' OR workflow='all') AND ('all'='all' OR substr(signup_at,1,7)='all') AND date(created_at)>='2026-05-01' AND date(created_at)<'2026-10-01') SELECT task_id,user_id,account_id,persona,workflow,created_at,render_success,reviewed_at,accepted,confirmed_use_at,confirmed_quality_use,direct_vendor_usd FROM b ORDER BY task_id;
Download SQL · Result CSV · Metabase saved question
Chart and reading
Inspect one task per row and follow its review/use/cost IDs.
Challenge your recommendation
Do rows duplicate users, accounts or money?
Guardrails: Fictional task IDs; no customer identity or private payload.
Next evidence: Real event provenance and task-version integrity.
PRD sections: 10, 11, 14. Open proposed PRD
Your spoken decision
State one takeaway, one caveat and one action in 30 seconds. Then stop.
Connect this finding
What this chart helps decide · Follow the connected decision · Canonical metric definitions
Optional model answer — try your own first
E15 · Trace the evidence
Decision: Which records support your recommendation?
Readout: Inspect one task per row and follow its review/use/cost IDs.
Actual query output (synthetic), first rows:
| task_id | user_id | account_id | persona | workflow | created_at | render_success | reviewed_at | accepted | confirmed_use_at | confirmed_quality_use | direct_vendor_usd |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | training | document_avatar | 2026-08-27 13:30:00 | 1 | 0 | 0.08607999999999999 | |||
| 2 | 1 | 1 | training | document_avatar | 2026-08-31 06:13:00 | 1 | 0 | 0.07039999999999999 | |||
| 3 | 1 | 1 | training | document_avatar | 2026-09-19 22:44:00 | 1 | 2026-09-20 09:56:24 | 1 | 2026-09-22 15:56:24 | 1 | 1.98572 |
Model spoken answer: I would show the supporting task records and keep their grain explicit before defending the aggregate.
Caveat/counterargument: Do rows duplicate users, accounts or money? Fictional task IDs; no customer identity or private payload.
Next evidence: Real event provenance and task-version integrity.
Deliberately left out: A tour of every field instead of the evidence that changes the decision.