The AI Feature Launch & Rollback Checklist: What to Verify Before, During, and After You Ship

AI Spec Kit

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Oct 29, 2026

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Originally published at aispeckit.substack.com

Launching an AI feature isn’t the finish line. In some ways it’s the start of the riskiest phase. Real users send inputs your test set never imagined. Provider models get updated. Costs track usage in ways your spreadsheet didn’t predict. And when something goes wrong, the output is confidently wrong in front of a customer.

The teams that handle this well decide most things before launch: what “ready” means, what triggers a rollback, who can pull the plug, and how they’ll notice gradual decline. Here’s a checklist that covers all of it.

Part 1: Pre-launch readiness

Split readiness by function, so every team owns its own items and nothing falls between them.

Product & UX

• [ ] PRD approved; non-goals and autonomy level confirmed
• [ ] Every UX state built and reviewed: loading, success, partial or low-confidence, failure/fallback, disabled by admin, unsupported input
• [ ] AI-generated content clearly labeled, with limitations explained in the product
• [ ] Feedback capture (thumbs, reason codes) live and flowing to a triage queue
• [ ] Holdout or experiment set up if you’re measuring outcome impact

Quality & evals

• [ ] Final eval on the held-out golden set meets every blocking threshold (record the run ID)
• [ ] Every failure in that run reviewed and tagged by failure mode
• [ ] Slice minimums met, with no segment quietly underperforming
• [ ] Red-team session done; findings fixed or accepted with sign-off
• [ ] LLM judge (if used) validated against human labels
• [ ] Model version pinned; prompt version tagged and linked to the eval run

Engineering & reliability

• [ ] Feature flag and staged rollout configured
• [ ] Kill switch tested in staging, with the time it takes to take effect measured and written down
• [ ] Non-AI fallback works end to end with the provider unavailable
• [ ] Timeouts, retries, and fallback model configured
• [ ] Rate limits and per-user/per-tenant spend caps set
• [ ] Provider quota confirmed for expected peak load
• [ ] Logging of inputs, outputs, and versions in line with privacy requirements
• [ ] Dashboards live and alerts routed to on-call

The kill switch item deserves emphasis. “We have a kill switch” and “we flipped it in staging and know how long it takes” are very different levels of ready.

Safety, privacy & legal

• [ ] Privacy checklist from the PRD complete
• [ ] Vendor terms (retention, training use, residency) confirmed in the contract
• [ ] Customer-facing docs updated (data use, subprocessors, admin controls) as required
• [ ] Admin opt-in/opt-out controls work as documented
• [ ] Legal sign-off on disclosures and any jurisdiction-specific requirements
• [ ] Guardrails verified with tests

Cost

• [ ] Cost model re-run with actual token counts from dogfooding
• [ ] Budget owner named; daily and monthly spend alerts configured
• [ ] Cost per user at target scale within budget

Go-to-market & support

• [ ] Support trained on how it works, known limitations, and how to escalate AI-quality issues
• [ ] Help-center article published, including “How is my data used?”
• [ ] Sales/CS talk track ready
• [ ] Release notes scheduled, with advance notice if the feature is on by default
• [ ] Incident channel and escalation path communicated

Close Part 1 with a simple go/no-go table: one named approver each for Product, Engineering, ML/Data, Privacy/Legal, and Support.

Part 2: Launch day

• [ ] Confirm the flag targets only the intended audience
• [ ] Watch dashboards closely for the first couple of hours: error rate, latency, spend, feedback
• [ ] Spot-check a sample of live outputs (10–20 is a reasonable start) against your rubric
• [ ] Send a test alert to confirm alerting works
• [ ] Post status updates in the launch channel at set times (for example T+1h, T+4h, end of day)

Part 3: Rollback

Triggers: numbers, not feelings

The most expensive rollback is the one that gets debated during an incident. Define triggers as numbers ahead of time, and for each one name who can act without asking for permission.

• Confirmed critical failure in production (e.g., fabricated commitment): threshold (set your own): Any confirmed instance with customer impact; action: Kill switch → incident process; who decides: On-call or PM, no approval needed
• Cross-tenant or permission data exposure: threshold (set your own): Any confirmed instance; action: Kill switch immediately; notify Security & Legal; who decides: Anyone on the response rota
• Sampled audit pass rate: threshold (set your own): Below [X]% for [N] periods; action: Pause rollout; investigate; who decides: PM + ML lead
• Negative feedback rate: threshold (set your own): Above [k×] trailing baseline for [24h]; action: Investigate; pause expansion; who decides: PM
• Error/timeout rate: threshold (set your own): Above [X]% for [N] min; action: Degrade to fallback; who decides: On-call
• p95 latency: threshold (set your own): Above [N]s for [N] min; action: Fallback or smaller model; who decides: On-call
• Spend: threshold (set your own): Above [X]% of daily forecast; action: Investigate; tighten limits; who decides: PM / budget owner

Notice that not every trigger means “turn it off.” Many mean degrade: move to a fallback model, or to a non-AI path. That only works if your PRD defined a degradation ladder and engineering built it.

Procedure

1. Act: flip the kill switch or roll the flag back a phase, and confirm the fallback is showing.
2. Communicate: post what triggered it, when, the scope, and the current state.
3. Preserve evidence: save example inputs and outputs, versions, and timestamps, following your privacy rules.
4. Assess impact: which users or tenants were affected, and did bad output reach end customers?
5. Notify: Support (with a macro ready), customer-facing teams, and Legal/Privacy if data or commitments are involved.
6. Fix and verify: add the failing cases to the golden set, fix, and pass a full regression run.
7. Re-enable with sign-off, starting with a smaller audience.
8. Post-incident review within a set number of business days, with owners for each action.

Step 6 is the one that compounds. Every incident should leave your test set stronger.

Part 4: Post-launch monitoring

Build a dashboard that covers:

• Quality: weekly sampled audit pass rate, negative feedback rate and top reason codes, and automated checks for format errors, refusals, language mismatch, and empty outputs
• Usage: active users, requests per user, regenerate rate
• Reliability: error, timeout, and fallback activation rates
• Latency: p50/p95 end to end, plus time to first token
• Cost: daily spend vs. forecast, cost per request, tokens per request
• Outcome: your business metric vs. the holdout

A reasonable cadence: daily checks and spot-checks for the first two weeks, weekly audits and golden-set additions for the next several weeks, then monthly drift reviews and a quarterly cost and model review.

Part 5: The monthly drift review

This is the step most teams skip, because nothing looks broken. Run it anyway. AI quality can decline with no change on your side.

• Input drift: Are users sending different things than at launch, such as longer inputs, new languages, or new use cases?
• Output drift: Is the model behaving differently, with changes in length, refusal rate, or format errors?
• Quality drift: Is the sampled audit pass rate trending down in any slice?
• Cost & latency drift: Are tokens per request creeping up?
• External changes: Has the provider announced a model update or deprecation? Has a regulation or a vendor term changed?

End every review with written decisions and owners, even if the decision is “no action.”

Get the checklist as a template

The AI Feature Spec & Eval Kit includes this launch, rollback, and monitoring checklist as an editable Google Docs/Word and Markdown file, plus a CSV version for Notion, along with the PRD, eval plan, and cost workbook it connects to.

Start with the free AI Feature PRD Template. Its failure-mode table, degradation ladder, and rollout plan are what make a fast, calm rollback possible later.

👉 Get the free AI Feature PRD Template · See the full Kit