Production LLM Launch Readiness Checklist
Catch the rollout gaps that cause wrong answers, bill spikes, and silent regressions before your chatbot or LLM workflow reaches real users.
Simple preview
The checklist is designed to help teams review wrong-answer risk, cost drift, and release safety in one pass.
What you will review
Wrong answers and trust risk
Check risky intents, citation expectations, refusal rules, and what the system should do when evidence is weak or conflicting.
Inference cost and ROI risk
Check token attribution, model routing, retry budgets, and whether you can measure cost per successful resolution instead of raw usage.
Regression and governance risk
Check prompt, model, KB, and guardrail release controls so one change does not quietly break production behavior.
Observability and incident readiness
Check trace IDs, version tags, retrieval evidence, and whether one reported bad answer can be reproduced quickly.
Security and logging hygiene
Check prompt-injection exposure, document trust boundaries, entitlements, and whether logs avoid storing more than reviewers need.
Owners and rollback
Check who approves risky changes, who owns incidents, and whether rollout rollback criteria exist before launch pressure hits.
Want the full audit path instead? Start with the LLM Audit Hub or go straight to AI Production Audit.
Checklist Access
Get the rollout checklist
We will take you straight to the checklist page after you submit. If the workflow sounds urgent, we may follow up with an audit recommendation.