Stop using AI as a chat box

Use AI for investigation, review, and QA reports, while keeping architecture, product risk, and ownership with the human.

Last week, I was fixing a simple bug that looked like one wrong config value.

Then I found the value was correct for some flows and wrong for others, which meant it was not as easy as replacing the value.

This is where AI would have swapped the value if I had just prompted “implement the fix for ticket JIRA-1234”. But I also found, thanks to AI, the different flows, the backend service that owns these configurations, and what the impact is on other parts of the system when changing those values.

My AI did an investigation, adversarial review, decided which follow-ups we needed and with which people, created a plan with multiple alternatives, implementation, and QA.

So what’s the difference between one AI and the other?

In this article, we’ll see how to use AI for software engineers. I’ve checked, and I consumed $20k in June in my AI workflows at work, so I’ve experimented a lot until I landed in this workflow.

When the output is an investigation report, a review checklist, a test-gap list, …