[HPSimulator]

One thing that feels different with AI-generated code is that the "design discussion" often happened inside the prompt instead of the PR.

In traditional workflows, a lot of the reasoning is visible through commit history, comments, or intermediate refactors. With LLMs, the reasoning step can be hidden because the model collapses that exploration into a single output.

What we've started doing internally is asking for two artifacts instead of just the code:

1. the prompt or task description that produced the code 2. the generated code itself

Reviewing both together gives you much better context about the intent, constraints, and tradeoffs that led to the implementation.

[captainkrtek]

I've been toying with this too.

I added a Claude skill (/gather-history) that consolidates the history of our session(s) specific to the change into a series of: decision log, involvement (how much did I write vs. AI, how many refinement iterations, reviews, etc.) that I can then include in the PR. So far this has been helpful for my colleagues to understand how I arrived at the change and how thoroughly it's been developed.