Release notes look like the easiest place to start with AI. They are short, repetitive and written against a deadline. Most documentation teams try it, and many stop.
The problem
When a writer pastes a list of ticket titles into a chat window and asks for release notes, three things go wrong.
Thin input. Ticket titles are written by engineers for engineers. The model fills the gaps with plausible guesses about what changed and why it matters.
Inconsistent output. Each writer prompts differently, so each release reads differently. Customers notice.
Review takes longer than writing. Without a defined check, reviewers reread everything against the tickets.
The solution
Treat the prompt as part of the documentation system. Fix the inputs, the template and the checks, and let the writer's judgement go into the parts that need it: what customers must know, and how to say it.
The approach
Structure the input. For each change, collect a fixed set of fields: ticket ID, change type (new, improved, fixed, removed), affected product area, who is affected, and the engineer's description. A shared spreadsheet or ticket export is enough.
Write one prompt template for the team. It names the audience, the release note format, your product terms, and what to do with gaps: mark them for the writer instead of guessing. Store it where everyone uses the same version.
Draft in batches by change type. Fixes, new features and breaking changes need different treatment. Drafting them separately keeps the model on one pattern at a time.
Check against the source, line by line. Every note should trace to a ticket ID. Anything the model added that the ticket doesn't support comes out. Use the team's AI review checklist for facts, terms and style.
Route breaking changes to an SME. Anything that changes behaviour customers depend on gets an engineer's sign-off before publishing.
Measure two things from the first release: time from code freeze to published notes, and the number of corrections at review. Both should fall as the template improves. If corrections don't fall, the template is missing a rule.
Where we fit. AI Workflow Transformation (Programme 03) builds workflows like this around your own tickets, style guide and review process, with 30 days of implementation support afterwards. Book a discovery call.