AI release readiness guide

Step 1

Set release criteria

Write down the user, decision and unacceptable failure before choosing a metric. This prevents a polished demo from becoming the evaluation standard.

Treat missing evidence as an explicit state. A result should explain what supports it, how confident the team can be and what to collect next.

Step 2

Separate score from confidence

Use representative evidence and keep the tested provider, model, operation, account, region and time window attached to every conclusion.

Treat missing evidence as an explicit state. A result should explain what supports it, how confident the team can be and what to collect next.

Step 3

Review safety and operations

Use representative evidence and keep the tested provider, model, operation, account, region and time window attached to every conclusion.

Treat missing evidence as an explicit state. A result should explain what supports it, how confident the team can be and what to collect next.

Step 4

Record an accountable decision

Use representative evidence and keep the tested provider, model, operation, account, region and time window attached to every conclusion.

Treat missing evidence as an explicit state. A result should explain what supports it, how confident the team can be and what to collect next.

• Decision owner named
• Evidence scope recorded
• Limitations visible
• Next review scheduled

Next step

Turn the framework into one representative evaluation.

Use a starter template or request a reviewed benchmark. Do not send credentials or confidential production data through a public form.