Comment by intrasight

It's after we come down from the Vibe coding high that we realize we still need to ship working, high-quality code. The lessons are the same, but our muscle memory has to be re-oriented. How do we create estimates when AI is involved? In what ways do we redefine the information flow between Product and Engineering?

I'm currently having Claude help me reverse engineer the wire protocol of a moderately expensive hardware device, where I have very little data about how it works. You better believe "we" do it by the book. Large, detailed plan md file laying out exactly what it will do, what it will try, what it will not try, guardrails, and so on. And a "knowledge base" md file that documents everything discovered about how the device works. Facts only. The knowledge base md file is 10x the size of the code at this point, and when I ask it to try something, I ask Claude to prove to me that our past findings support the plan.

Claude is like an intern coder-bro, eager to start crushin' it. But, you definitely can bring Claude "down to earth," have it follow actual engineering best practices, and ask it to prove to you that each step is the correct one. It requires careful, documented guardrails, and on top of it, I occasionally prompt it to show me with evidence how the previous N actions conformed to the written plan and didn't deviate.

If I were to anthropomorphize Claude, I'd say it doesn't "like" working this way--the responses I get from Claude seem to indicate impatience and a desire to "move forward and let's try it." Obviously an LLM can't be impatient and want to move fast, but its training data seem to be biased towards that.

I always feels like I'm in a fever dream when I hear about AI workflows. A lot of stuff is what I've read from software engineering books and articles.