What you'll learn

By the end of this you'll have a concrete pre-launch and post-launch checklist for shipping an AI feature — not the abstract "be responsible with AI" advice, but the specific gates that stop you from leaking secrets, burning budget, or shipping a UX that falls apart the first time the model returns nothing useful.

This is a resource you can copy into your team's launch doc, adapt to your stack, and run before every AI feature goes to production. The goal is not perfection on day one. The goal is knowing what you checked and what you deliberately deferred.

Who this is for

  • Engineers adding LLM-powered features to an existing product (summaries, search, drafting, classification)
  • PMs or tech leads who need a shared definition of "ready to ship" for AI work
  • Solo builders who want a sober second pass before flipping a feature flag to 100%

Skip this if you're still in prototype mode with no real users and no API keys in production. Come back when "demo works on my laptop" is about to become "customers can hit this endpoint."

What is an AI feature launch checklist?

Plain English: it's the list of things that are easy to forget when you're excited that the prompt finally returns coherent JSON.

A normal launch worries about bugs, load, and rollback. An AI feature adds non-determinism, per-token cost, and privacy questions unit tests won't catch. "It worked in the playground" is a starting point, not a launch criterion.

Prerequisites

  • A defined user-facing AI feature (not "we might use AI someday")
  • Access to your provider dashboard (OpenAI, Anthropic, etc.) for limits and logging settings
  • At least five real inputs you've tested manually — including awkward ones
  • A staging environment where you can run the feature against production-like config

Setup from zero

You don't need a fancy eval platform on day one. You need a document, a spreadsheet, or a Notion page with the sections below — and someone who owns checking each box.

Step 1 — Write the feature contract

One paragraph: what the feature does, what it must never do, and what "failure" looks like from the user's perspective. Example: "Summarize support tickets into three bullet points. Must not expose internal admin notes. Failure = empty summary with a retry option, not a hallucinated resolution."

This becomes the anchor for every other checklist item. Without it, evals drift and UX debates never end.

Step 2 — Build a golden eval set

Collect 15–30 real or realistic inputs with expected behavior described in plain language — not exact string matches. Include edge cases: empty input, very long input, non-English text, prompt-injection attempts, and inputs where the correct answer is "I don't know."

Store them in version control if you can. When you change the prompt, re-run the set before merge.

Step 3 — Wire observability before launch

Log request ID, latency, token usage, model version, and outcome class (success, empty, error, user-reported bad). Do not log raw user content if your privacy policy says you won't — log hashes or redacted excerpts instead.

Little tip: add a feature.ai.ship_checklist_version field in your internal docs and bump it when the checklist changes. Six months from now you'll want to know which launch process a live feature actually followed.

Step 4 — Define kill switches

Three switches, minimum: disable the feature entirely, force a fallback response, and cap spend. Test each in staging. "We can turn it off" is not true until someone has done it without deploying code.

Step 5 — Run a dry-run launch

Treat it like a game day. Spike traffic in staging, trigger rate limits, revoke an API key temporarily, and confirm the UI degrades gracefully. Write down what broke. Fix the top two things, not all ten — ship with known limits documented.

The mental model

The mental model for shipping AI features is containment before capability.

Capability is how good the happy path feels. Containment is what happens when the model is wrong, slow, expensive, or unavailable — and those states are guaranteed to happen in production, just not in the demo you rehearsed.

Every checklist item maps to a containment layer: privacy, evals, cost caps, empty states, human override, rollback. Optimize only for capability and you win a demo, lose a support queue.

Key terms

Golden eval set — a fixed collection of inputs with expected behavior, rerun on every prompt or model change.

Prompt injection — user input crafted to override your system instructions ("ignore previous rules and…").

Fallback — a non-AI path when the model fails: cached answer, rule-based default, or honest "try again later."

Cost cap — hard limit on spend per user, per day, or globally — enforced in code, not just monitored after the fact.

Human override — a way for users or staff to reject, edit, or disable AI output without filing a ticket.

Rollback plan — how you revert to the previous behavior in minutes, including feature flags and provider config.

Step-by-step: the launch checklist

Work through these in order. Copy the markdown into your repo if that's easier than scrolling.

Before launch — privacy, quality, UX, ops

  • [ ] API keys server-side only; user content covered by privacy policy and DPAs
  • [ ] Logs redact PII — verify with a sample request, not assumptions
  • [ ] Golden eval set passes your threshold; prompt, model, and temperature pinned
  • [ ] "Must never" behaviors tested (e.g. never invent order numbers)
  • [ ] Empty, error, and loading states written — distinguish "our fault" vs "try different input"
  • [ ] Users can edit, discard, or regenerate without losing original input
  • [ ] Cost cap enforced in code with alerting at 80%; feature flag off-path tested in staging
  • [ ] Rollback documented: who flips the flag, runbook location, expected recovery time

After launch — first 72 hours

  • [ ] Watch cost hourly day one; sample 20 production outputs manually
  • [ ] Track user corrections (edits, regenerates, thumbs-down) as signal
  • [ ] Hold a 30-minute retro: what failed containment, what was overbuilt

Little tip: schedule the post-launch review before you launch. If you wait until "things calm down," you won't do it. Put it on the calendar with an agenda: cost, quality samples, support tickets, one prompt tweak max.

Working examples

Minimal server-side proxy pattern

// Never call the provider from the browser
export async function POST(req: Request) {
  const { input } = await req.json();
  const trimmed = input.slice(0, 4000); // hard cap
  const result = await callModel({ system: SYSTEM_PROMPT, user: trimmed });
  return Response.json({ summary: result.text, requestId: result.id });
}

Patterns / when to use each guardrail

| Guardrail | Use when |
|-----------|----------|
| Hard input length cap | Any user-generated text feature |
| Structured output + schema validation | JSON, classifications, extracted fields |
| Human review queue | High-stakes decisions (billing, moderation, medical-adjacent) |
| Cached responses | Repeated identical queries (docs search) |
| Smaller / cheaper model for draft | Two-step flows where users edit before final |

Common mistakes

Shipping the playground prompt — production needs shorter instructions, explicit refusal rules, and output format constraints the playground didn't need.

Evaluating only happy paths — the failure modes users hit first are empty input, hostile input, and inputs longer than your context window.

Monitoring cost without capping it — dashboards tell you yesterday's mistake. Caps stop today's invoice.

No empty state — a blank box after three seconds feels broken. A sentence explaining what happened feels intentional.

Troubleshooting

Eval scores look good but users complain — your eval set is too clean. Add 10 real production failures from support tickets.

Costs jumped 10x — look for agent loops, missing max_tokens, or an uncapped endpoint a power user found.

Model returns plausible nonsense — add verification: cite sources, require a confidence field, or show "verify before use."

Checklist

  • [ ] Feature contract written (does, must not, failure UX)
  • [ ] Golden eval set in version control, rerun on prompt changes
  • [ ] Secrets server-side only; logging reviewed for PII
  • [ ] Cost cap + feature flag + rollback tested in staging
  • [ ] Empty, error, and loading states shipped — not TODOs
  • [ ] Post-launch 72-hour review scheduled with owner

Practice task

Pick one AI feature you're building or maintaining. Block 45 minutes. Walk the "Before launch" sections above and mark each item pass, fail, or N/A with one sentence of evidence. For every fail, decide: fix before launch, or document as accepted risk with an owner. That document is more valuable than another prompt tweak.

FAQ

Do we need a dedicated eval platform?
Not on first launch. A spreadsheet and a script that loops over rows is enough. Upgrade when prompt change frequency hurts or multiple people need parallel eval runs.

What pass rate should we require?
There is no universal number. High-stakes features need higher bars and human review. Low-stakes drafting aids can ship at 85% acceptable if fallbacks are good. Write the threshold down; don't debate it in Slack at midnight.

Can we ship without legal review?
If user data leaves your infrastructure to a third-party model, someone with authority should confirm policy coverage. The checklist can't answer your jurisdiction — it reminds you to ask.

Should we tell users it's AI?
Depends on product and regulation. Default to transparency for trust; your legal team may require specific wording. Don't hide it because the output sounds confident.

What to learn next

  • Lighthouse performance cheat sheet — if your AI UI lives on content-heavy pages, speed still matters
  • SEO content brief template — if the feature ships alongside new marketing pages
  • OpenAI vs Anthropic APIs — provider choice affects cost, latency, and safety defaults
  • [Lighthouse performance cheat sheet](/resources/lighthouse-perf-cheat-sheet)
  • [SEO content brief template](/resources/seo-content-brief-template)
  • [OpenAI vs Anthropic APIs](/ai/comparisons/openai-vs-anthropic-apis)

Takeaways

Shipping an AI feature is a normal software launch plus containment for non-determinism, cost, and privacy. The checklist isn't bureaucracy — it's the difference between a controlled experiment and an incident you explain to finance.

Run evals on ugly inputs, cap cost in code, write empty states like you mean them, and test the off switch before you test the demo.

If you remember only one thing: the launch isn't ready when the happy path works — it's ready when you've seen the unhappy paths fail gracefully and you know how to turn it off without a deploy.