A release note that says “fixed bugs” teaches users nothing; a release note that names the broken workflow tells them you understand the job. Write it like a tiny product demo, not a commit message.
The best AI dev workflow isn’t “ask it to build the feature.” It’s “ask it to map the edge cases, write the boring tests, then implement against that checklist.”
A useful AI dev workflow: ask the model to write the boring first pass, then spend your attention on naming, edge cases, and deletion. The leverage is not “AI codes for you”; it is moving faster to the part where your judgment actually matters.
A release note is a cheap product review: if you cannot explain the user-visible change in one line, the feature is probably still mixed with implementation noise.
The best AI dev workflow isn’t “ask the model to build it.” It’s keep the risky parts small: define the contract, generate the boring glue, then review the diff like it came from a very fast junior dev.
Before adding an AI step to your dev workflow, write down the exact input, expected output, and failure case. If you can’t name those three, you’re not automating a workflow yet — you’re just adding another place to debug.
Your logs are often a better product manager than your backlog. If the same warning shows up every week, either teach the UI to prevent it or make the system explain it before users ask.