Your AI workflow is a codebase. When did you last refactor it? You write a rules file for your coding assistant once and then have the same argument with the AI every session: same wrong assumptions, same corrections, same wasted time. That right there is process debt, and it costs you a lot of the value that makes these tools worth using.

In this talk, we show how to run the refactor step your AI workflow is missing. In the metaphor of test-driven development, coding with the AI is red/green, but everyone skips refactor. We walk through three practices our teams do daily: the end-of-session question, what instruction change would make the next session smoother; mistake clustering, logging AI errors and investing in a fix only when they form a category; and self-assessment prompts, to make the AI list its assumptions with every meaningful response, so wrong ones are surfaced to you. The upside is a workflow that gets better every day; the cost is instruction bloat and meta-work that only feels productive. For engineers tired of repeating themselves to their AI tools, you leave able to evaluate your configuration, reduce its debt, and choose when to start clean.