Production AI Readiness Gate (QA)

Production AI Readiness Gate (QA)

1. 01

   Define evaluation criteria with the client before any model goes live

   Agree on accuracy thresholds, latency budgets, and cost ceilings per interaction so the evaluation pipeline measures what the client actually pays for.

2. 02

   Instrument every LLM call with tracing from day one

   Use a platform like Langfuse or Braintrust to capture hierarchical traces of prompts, tool calls, and retrieval steps, ensuring you can replay any production failure.

3. 03

   Build a golden dataset of representative client scenarios

   Collect at least 50 real or synthetic inputs that cover edge cases, off-topic queries, and adversarial phrasing to serve as the baseline for regression testing.

4. 04

   Run automated evaluations with LLM-as-judge and human spot checks

   Score response quality on every release using a mix of programmatic metrics and human review on a 10% sample to catch judge bias or drift.

5. 05

   Set up drift and cost alerts on production dashboards

   Monitor token usage, latency percentiles, and output quality scores weekly, with alerts triggered when any metric deviates more than 20% from the baseline.

6. 06

   Document a rollback plan for failed deployments

   Maintain versioned prompts and model configs so you can revert to the last known-good state within minutes if a client-facing agent starts misbehaving.

7. 07

   Review evaluation results with the client monthly

   Present a scorecard showing pass rates, cost per conversation, and incident count, turning observability data into a retention tool that justifies your premium.