Create a model-change regression release process

A provider model name can hide behavior changes, and an explicit upgrade can improve average quality while damaging one critical task. Treat model changes like code changes with an immutable candidate, regression suite and controlled promotion.

Define the measurable control

Pin current and candidate versions. Freeze the dataset, prompt, tools, decoding settings and graders. Compare by critical slice, not just aggregate score. Run shadow traffic with privacy controls, inspect disagreements, rehearse rollback and record the release decision with named owners.

The AI reliability lab makes the decision reproducible with authored data:

from reliability_cases import model_release_case

result = model_release_case()
assert result["failing"] == ["finance"]
assert result["decision"] == "hold"
assert result["shadow_only"] is True
assert result["version_pinned"] is True

The candidate improves routine and unanswerable slices but falls below the finance floor. The lab therefore holds it in shadow. This is an authored comparison, not a benchmark of any real model; it demonstrates that one critical regression can override an average gain.

Challenge the result

Changing the model, prompt and retrieval stack together makes attribution weak. Migrate one controlled dimension or run a factorial experiment. Recalibrate model-based graders when their judge model changes, and keep human review on high-cost disagreements.

Keep a reviewable evidence pack:

• pinned versions and evaluation configuration
• slice-level paired comparison
• shadow disagreement review
• promotion or hold record plus rollback drill

This work aligns with the evaluation, security, cloud operations, reliability and FinOps sequence in the FDE for Professionals course. The linked course describes the learning pathway; this article’s numbers are synthetic and do not report a model, client, audit or production result.

Practice task

Create three slices for your workflow. Give each a floor before scoring a candidate and write the release decision without changing those floors.

Continue learning

This article is part of the Advanced AI reliability and assurance sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Design a circuit breaker around unreliable tools.
• Continue with Calibrate a grader before using it as a deployment gate.

Reference: OpenAI evaluation guidance.