Free Resource
The LLM Production-Readiness Checklist
A practical, 20-point checklist for deciding whether an AI feature is ready to ship. Built from the metrics and thresholds our engineers use on real production systems. No fluff, no sales pitch.
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1. Scope the risk
- You have written down the three most costly ways this AI feature can fail a user or the business.
- Each failure is rated by impact, so testing effort goes where a wrong answer is most expensive.
- You know which parts are yours to test (prompts, retrieval, guardrails, integration), separate from the base model.
2. Build the evaluation set
- You have a golden dataset of at least 50 examples for an early read, growing toward 500 or more for a production-grade result.
- The set includes normal cases that reflect the most common real usage.
- The set includes edge cases: rare scenarios, empty or malformed input, very long context, and ambiguous requests.
- The set includes adversarial inputs: jailbreak attempts, prompt injection, and scope-violation prompts.
3. Set thresholds before you look at results
- Hallucination rate target is defined: around 5 percent or lower for general use, around 1 percent or lower for high-stakes domains.
- Groundedness or faithfulness to retrieved context is measured for any retrieval-augmented system.
- Output consistency across repeated runs of the same prompt is tracked.
- An acceptable ceiling for successful adversarial attacks is set, and any regression above it blocks release.
- Latency and cost targets under expected load are defined.
4. Choose methods that hold up
- Regression is caught by rerunning the golden dataset whenever a prompt or model changes.
- If you use LLM-as-a-judge, the judge is calibrated against human labels and audited periodically.
- Humans review a sample of outputs for the highest-risk categories.
- Evaluation runs automatically in CI and blocks merges that fall below threshold.
5. Operate after launch
- The live system is monitored for drift, latency spikes, and quality degradation.
- You have a plan to re-evaluate after any model or provider update.
- Test suites, datasets, and results live in your own repository, in a format you can maintain without any single vendor.
