How to prove AI works in production

If your AI only works in a recorded demo, you do not have a production system. You have a performance. Production proof means a skeptical engineer can verify claims without you narrating.

Start by writing the claim in one sentence. Then list the artifact that would falsify it. If you cannot name the artifact, the claim is not ready to ship.

Reviewers ask what is running. Answer with environment attestation, signed build artifacts, version pins, and deploy provenance. Model names in a README are not enough when weights, prompts, tools, and policies all move.

Separate the inference path from the control plane. The path that can spend money, write PHI, or change customer state needs stronger evidence than a chat toy.

Tamper-evident logs turn I think into here is the chain. Hash-link events. Sign appends. Keep the audit package on every write path so a skipped log is a failed write, not a soft miss.

Scribient-style boundaries matter: encryption and audit live in packages every service can call. If logging is optional middleware, it will be skipped under pressure.

Preregistered metrics stop you from shopping for a chart after the fact. Publish the contract: activation, error budget, eval set, and decision rule. Score the system against that contract on a schedule.

Renovly's growth analyst on cron is the spirit of this idea applied to acquisition: the pipeline closes the loop against live data, not a retrospective story.

Can you re-run the critical path with the same inputs and get a comparable outcome? If tools, retrieval, and temperature make that impossible, document the bounds and the sampling policy. Reviewers accept uncertainty that is named. They reject mystery.

When an incident hits, the team that already has receipts moves. The team that has vibes starts a war room theater.