AI Feature Readiness Checklist: 12 Checks Before You Ship
The gap between an impressive AI demo and a dependable product feature is mostly engineering. The model is only one component; permissions, evaluation, fallbacks, monitoring, and product language decide whether users trust the result. Use this checklist before making an AI feature available to real customers.
Written from Vedwix project experience. Product capabilities, pricing, and platform policies change; verify current details with the linked primary sources before making a business or engineering decision.
Key takeaways
- Define what the feature is allowed to do before writing prompts.
- Test real failure cases, not only polished examples.
- Treat permissions, logging, and data retention as product requirements.
- Ship a fallback path so the model is never the only way forward.
1. Define the job and the boundary
Write the user outcome in one sentence, then write what the feature must refuse or hand back to a human. “Draft a reply using our help centre” is a bounded job. “Answer anything about our business” is not. A narrow contract makes evaluation, UI copy, and support training much easier.
Decide whether the feature is advisory or action-taking. Suggestions can be reviewed; an agent that changes a record, sends a message, or triggers a payment needs explicit confirmation, audit logs, and a way to undo the action.
2. Map data access before model access
The model should receive only the information the current user is allowed to use. Enforce tenant, role, and object-level permissions before retrieval or tool execution. Do not rely on the prompt to tell a model that a document is private; the application must make the private document unavailable.
Document what is stored, for how long, and who can inspect it. Prompts, retrieved passages, tool results, and user feedback can all contain sensitive data. Make retention and redaction decisions before you turn on verbose logs in production.
3. Build an evaluation set from real work
Collect representative inputs from support tickets, workflows, or user interviews, then label what a good answer must contain and what would make it unsafe or unhelpful. Include ambiguous, incomplete, adversarial, and out-of-scope inputs. Twenty carefully chosen cases are more useful than a hundred flattering demos.
Track retrieval quality, factuality, instruction following, refusal quality, latency, and cost as separate measurements. A single thumbs-up score hides the reason a result failed and makes regressions hard to diagnose.
4. Design for hostile and broken inputs
Treat prompt injection as an expected input class, especially when the model reads web pages, uploaded files, emails, or customer-generated text. Separate instructions from retrieved content, restrict tools, validate tool arguments, and require confirmation for consequential actions.
Also test boring failures: empty search results, malformed JSON, provider timeouts, rate limits, oversized documents, and a model returning a valid-looking but incomplete answer. The fallback should tell the user what happened and what they can do next.
- No retrieved answer: say that the source was not found.
- Provider timeout: retry within a budget, then offer a manual path.
- Tool call rejected: show the issue without exposing internal credentials.
- Low confidence: ask a focused follow-up instead of guessing.
5. Put cost, latency, and observability in the release criteria
Set a cost ceiling per task and a latency target that matches the user experience. Cache safe, repeatable work, trim unnecessary context, and choose the smallest model that passes the evaluation set. A feature that is accurate but too slow or expensive is not production-ready.
Log request IDs, model versions, evaluation scores, latency, token usage, tool calls, and user feedback without logging secrets or raw personal data by default. When quality changes, you need to know whether the cause was the model, prompt, retrieval index, source content, or application code.
6. Make the user experience honest
Tell users what the feature does, where its information comes from, and when they should verify an answer. Avoid language that implies certainty when the system is generating a suggestion. Citations, “last updated” labels, edit controls, and a clear report path are practical trust features, not decoration.
Launch to a small audience first. Compare the feature against the existing workflow, review failures with the people who do the work, and keep a kill switch. A staged rollout gives you evidence before a bad answer becomes a support or reputation problem.
Sources and further reading
Frequently asked questions
Do I need human review for every AI output?
Not for every low-risk suggestion, but you should define risk tiers. Require review or confirmation for actions that affect money, permissions, customer communication, or durable records, and provide a visible correction path for lower-risk outputs.
What should I log for an AI feature?
At minimum, log a request ID, model and prompt version, latency, token usage, retrieval or tool activity, outcome, and user feedback. Redact secrets and sensitive personal data, and set a retention period before enabling detailed production logs.
When is an AI feature ready to launch?
When it passes a representative evaluation set, has a safe fallback, respects permissions, meets cost and latency targets, and has monitoring plus an owner who can respond to regressions. A successful demo alone is not a release criterion.