AI changes are different to typical software changes. A prompt tweak can change tone, refusal behaviour, and how a system explains uncertainty. If users discover these shifts through surprise, trust drops quickly. Release notes are a simple control that prevents confusion and improves adoption.
What AI release notes should include
Keep them short and user-oriented:
- What changed. Model update, prompt update, new data sources, new tools, or new policies.
- Who is impacted. Which workflows, roles, tenants or regions.
- What to expect. Changes in speed, tone, citations, or fallback behaviour (see UX patterns).
- Known limitations. What is not improved yet and how users should work around it.
- How to report issues. A clear path for feedback and escalation.
Classify changes by risk
Not every change needs the same visibility. Classify changes as:
- Low-risk. UI copy, minor prompt wording, non-behavioural tweaks.
- Medium-risk. Prompt policy changes, retrieval adjustments, new guardrails.
- High-risk. New tools, higher autonomy, or new data domains (see risk appetite).
Pair release notes with controlled rollouts
Release notes do not replace operational controls. Use canary rollouts and feature flags so changes are reversible (see canary rollouts and feature flags). For high-risk changes, require explicit approvals (see approvals).
Use metrics to confirm impact
Release notes are strongest when they match observed outcomes. Track usage, escalation, refusal rates and user feedback (see usage analytics). If negative signals spike, stabilise quickly and communicate clearly (see change freeze playbooks).
Clear communication is part of delivery. Good AI release notes make change safer, adoption smoother, and governance easier.