Every time a new feature or fix comes up, some people do the same thing. They open their favorite AI chat, copy the requirement or the PR description, paste it in, and ask "generate test cases for this feature". Then they copy the output back into their test management tool or their notes.
There is nothing wrong with using AI this way, it works. I do think there is something wrong with having a manual flow around it. You have to remember to do it, you have to copy and paste the context every single time, and the AI only knows what you decided to paste. If you forget a detail, or if the feature touches something you did not think to mention, the AI will not know either.
Instead of manually going to an AI chat every time, you can integrate that same LLM into your workflow, so it runs automatically and already has the context it needs.
One example I built recently at work is a GitHub workflow that runs on the PR level.
Every time a developer opens a PR implementing a feature or a fix, we have a label called "ai:generate-test-cases". When someone adds this label, it triggers a GitHub workflow that runs a script calling an LLM. This script reads the full PR diff, the comments, and even external links using MCPs, to gather as much context as possible. Then it drops a comment on the PR with a list of possible test scenarios, the risk level of that change, and whether the developer already added automated tests for it. This way we can focus on the areas that matter the most.
