This generative AI production checklist follows one question through the app across 14 checks in four groups. Also, each check names the proof to ask for and who answers for it. Also, the verdict says who may use the app first, not whether it passes or fails.
Last reviewed 2026-11-14 by Manish Sharma.
A generative AI application is software that writes answers from company data. However, its main risk is a wrong answer written with confidence. Because of that, the checks below look at what the app reads, how its answers get checked, what it can be tricked into and who answers when it fails. Also, the examples use Amazon Bedrock on AWS and Azure OpenAI Service on Azure, with no winner named.
Generative AI Production Checklist: How the Release Verdict Is Reached
First, each check gets one of three marks. Exercised means someone ran it on the app, and then kept the result. Designed means someone wrote a plan, but nobody has run it. Unaddressed, however, means there is no answer.
| Mark | What counts as evidence | Meaning |
|---|---|---|
| Exercised | A run on the app, with the result kept | Done and shown |
| Designed | A written plan, nothing run yet | Planned only |
| Unaddressed | No answer, or no owner named | Missing |
The release verdict depends on who may use the app, but there are no points or totals. Then “Open to all users” needs no Unaddressed mark anywhere and every check in groups A and C Exercised. “Open to a pilot group” allows no more than two Unaddressed marks, and none in A or C. Also, D1 and D3 need at least Designed. Otherwise, anything else is “Keep in build”.
When you read the marks, the verdict names an audience, not a pass or fail.
Groups A and C come first for the widest audience, because reading and trickery are what any user can exploit. Signisys working view: a gap should shrink the audience, not end the project. Also, these floors are a starting rule to adjust with your own CIO and write down.
Key Takeaway The verdict names who may use the app first, but a gap shrinks that audience.
Generative AI Production Checklist: The Path From Question to Answer
First, the four groups follow one question through the app. Also, each table lists what to ask the team to show and who answers. Then mark the groups in order, A to D.
What It May Read
| Check | Evidence to ask for | Owner |
|---|---|---|
| A1 Answers come only from named, signed-off sources | A source list with a named owner for each | Product owner |
| A2 Retrieval respects each user’s document permissions | A run where a user without access gets nothing from that document | Security lead |
| A3 Data location for India and the GCC, for the model and the stores | Region settings | Compliance lead |
However, this guide names no residency rule for A3. A rule you lean on needs its own regulator source, and until then the owner answers A3 from the settings.
For A2 and C1, ask the team to show the run once, then keep the result with the mark.
How Answers Get Checked
| Check | Evidence to ask for | Owner |
|---|---|---|
| B1 An evaluation set with an agreed pass mark | The set and its latest result | Product owner |
| B2 The set re-run on every prompt or model change | Run history, by change | Lead engineer |
| B3 Each answer cites its source or says none was found | Sampled answers | Product owner |
B1 matters first, because it is the check that catches a confident wrong answer. Also, B3 lets a reader see where an answer came from.
What It May Be Tricked Into
| Check | Evidence to ask for | Owner |
|---|---|---|
| C1 Hostile text in a question is handled | A test log of hostile inputs and what the app did | Security lead |
| C2 Guardrails and content filters set for allowed and prohibited topics | The configuration and a refusal run | Security lead |
| C3 Actions limited, with a person approving important ones | The action list and an approval record, reviewed and dated | Product owner |
Hostile text, also called prompt injection, is text in a question or a document that tries to make the app ignore its instructions. Next, for security steps on a generative AI application, read the AWS seven-step security checklist. Dated 27 June 2025 on the page and read on 14 November 2026, it names prompt injection and Amazon Bedrock Guardrails among its steps. Then it ends with monitoring and incident response.
Also, an app that takes no actions marks C3 by listing none. However, an empty action list earns Exercised only with that reviewed, dated evidence.
When an app logs nothing, D1 and D3 have no proof, and those two checks cannot rest on a promise.
Who Answers When It Fails
| Check | Evidence to ask for | Owner |
|---|---|---|
| D1 Logging of question, answer and sources | One traced answer | Platform lead |
| D2 Monitoring and alerts on answer quality, errors and quota | The alert list and a test alert | Platform lead |
| D3 A named owner and an incident route | A runbook with names | IT head |
| D4 A fallback to a person, and a rollback | A dated rollback rehearsal | Lead engineer |
| D5 An owner for cost per answer, and a quota set for expected load | A named owner and the quota setting, no figures | Finance partner with platform lead |
D1 and D3 carry the pilot floor, so ask for them first. Also, D5 asks who answers for spend and what limit stops a surprise.
Generative AI Production Checklist: Reading the Verdict on an HR Policy Assistant
First, treat the example below as made up: no Signisys client or project sits behind it. Also, the assistant answers staff questions from HR documents.
| Group | Marks | Reason |
|---|---|---|
| A What it may read | All three Exercised | Source list, permission run and region settings all on file |
| B How answers get checked | B1 and B3 Exercised, B2 Designed | Evaluation set run once; re-run plan written, not yet run |
| C What it may be tricked into | All three Exercised | Hostile test log, refusal run, and a dated empty action list |
| D Who answers when it fails | D1 and D3 Exercised; D2 and D5 Designed; D4 Unaddressed | Traced answer and runbook on file; no rollback rehearsal |
One mark reads Unaddressed, but it sits outside groups A and C. Also, D1 and D3 both carry an Exercised mark. Then the verdict is “Open to a pilot group”. Next, the fix is the rollback rehearsal (D4), owned by the lead engineer.
Key Takeaway One Unaddressed mark outside groups A and C still allows a pilot group.
Generative AI Production Checklist: Widening the Audience Step by Step
First, move the audience in steps and read the marks again at each one.
Under “Keep in build”, fix groups A and C first, then D, and read the marks again.
Under “Open to a pilot group”, name the group. Then set the question that widens it: every Designed mark in group D run, D4 included. Then read two weeks of logs before widening. LLM production readiness is a standing matter, so read the marks again after each model or source change, including under “Open to all users”.
Signisys working view: keep the pilot group small and named, so each wrong answer leads to a person who can fix it.
Related Guides for This Stage
Also, four guides sit beside this one, each on a different question.
- what keeps a generative AI pilot from going live covers why pilots stall.
- the order for building each item covers the build steps first.
- readiness of the whole organization for AI work covers the organization, which this guide leaves alone.
- policy checks on staff use of AI tools covers staff use of AI tools.
However, prices sit outside this guide, and the choice of grounding method is set out in how to decide between feeding documents at answer time and retraining.
Signisys provides this work. Also, see how Signisys builds generative AI applications on Amazon Bedrock or Azure OpenAI Service, our enterprise generative AI solution and support for data and AI work. Also, a working session reads your app’s marks on your own records and does not promise an outcome. No charge, with a fixed scope.
Generative AI Production Checklist FAQ
Which check catches a wrong answer written with confidence?
What must be re-run when the model is updated?
What stops the app acting on hostile text a user pastes?
What verdict does an app with no named owner get?
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