Make a release and regression decision
Course overview · 4 min reading + 12 min practice, estimated
Principles and method
Compare results with the predefined gates and decide to approve a bounded use, revise or stop. Record limitations and monitoring requirements. Keep a regression set to rerun after prompt, model, retrieval, schema or supplier changes. Track drift in real inputs and operator behaviour, not only model versions. A passed test is evidence for a scope at a time, not a permanent guarantee. Use controlled rollout and a pause route when residual uncertainty remains. Record failures honestly so later decisions learn from them.
Worked example
A new model improves readability but fails two evidence-reference checks. The team retains the earlier approved version for the task while investigating, rather than upgrading solely because the model is newer.
Put it into practice
Write a release decision for mixed fictional results and a regression schedule.
Use fictional information and keep your work in your own notes.
Compare your approach: self-review guidance
Reference the gates, unresolved risks and permitted scope. Define what change triggers retesting and who can stop use. Do not alter the gates after seeing the result merely to obtain a pass.
Sources and further reading
Original Academy teaching and fictional examples. These references provide context, not endorsement. Edition 2026.09; updated 2026-09-24.
- GOV.UK: Responsible AI in recruitment
UK guidance on procuring and deploying recruitment AI.
- NIST: AI Risk Management Framework
Voluntary framework for organising AI risks and controls.