Everyone is using AI in product engineering to ship more. Some people think this means lower quality products – and it's not hard to see why when people brag about their terrible vibe-coded products. But I disagree. In fact, I think AI can help set the bar for quality a lot higher than it ever was.

I'm not necessarily talking about automated testing or code review here. There are good tools for that and AI certainly is helping greatly with those.

I'm talking about the more nuanced elements of product quality. Improving the surface area of rushed features, fixing papercuts and onboarding stumbles, UX writing, documentation, removing outdated features, and generally polishing the small things no one ever has time for.

My relentless product improvement workflow

When using my own product, or when watching a user test, I keep a notes file open where I jot down every tiny thing that annoys me. Anything that's small enough to not be worth a ticket, but still annoys me enough to remember it, I jot down as a short bullet point in Markdown. If I notice anything that's more complex to fix or requires some more thought, I ask Claude Code to create a ticket in the Linear triage.

Then I take the entire list of improvements and bugs, put it into a IMPROVEMENTS_TODO.md file in the root of the project, and let Claude Code just work on it for a while. This way, everytime I use the product, we get to ship usually 20 small improvements. The entire process takes less than an hour.

This is akin to the concept of "kaizen" in Japanese business, invented by Toyota in the 1950s. Kaizen is the practice of continuously improving the small things in a process. It's a practice where a strong feedback loop is created in order to make processes better over time.

Even though a single improvement might not be worth a ticket, the cumulative effect of all these tiny improvements are really significant and compound into a much higher quality product.

I used to track a lot of these bugs and papercuts in the past too, but since they were often hard to prioritise in the day-to-day with a small team before AI, they often ended up forgotten somewhere in a massive backlog.

Continuously improving a software product every day and at every level of detail now comes with almost zero opportunity cost.