The standard narrative about AI-assisted software development is seductively unidirectional: describe what you want, the AI writes the code, and you ship faster. This narrative is not wrong. It is merely incomplete. Over six weeks of building an iOS app with Claude Code, I discovered that the highest-impact practice was not writing better prompts. It was maintaining the bidirectional feedback loop: correcting the AI’s persistent misconceptions and curating the shared documentation that governs every future session.

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One of the primary duties of a software developer is enhancing and fixing existing codebases. We do this by raising pull requests (PRs), getting them approved, and merging them to the codebase. I have been performing this duty for the entirety of my fifteen-year career as a software developer, and I’ve amassed a toolkit for this process. One tool is raising error-free PRs. I wrote about that here. The post you are reading is about another tool: writing a high-quality PR description. The tips in this post, if adopted, will help you get PRs approved more quickly, spark joy in your PR-reviewer coworkers, and facilitate debugging far into the future.

My target audience is primarily software developers. But non-developers who are curious about what we do might enjoy this post. Endnotes following it define terms that are likely unfamiliar to the developer-curious.

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One of the most-read posts on this blog is this one about typical iOS take-home coding exercises. The post has 3,797 views at time of writing, and several readers have privately thanked me for writing it. But, in my experience, the application process for many companies involves not a take-home coding exercise but rather a live-coding exercise. The candidate typically has forty-five minutes to implement an app from scratch that is similar to the app described in the post mentioned above but without unit tests or dependency injection.

The live-coding exercise is a different beast. Much of the knowledge required for a take-home coding exercise is applicable to a live-coding exercise, but the extreme time constraint of a live-coding exercise means that success is unlikely without extreme practice, preparation, and time-saving. Worse, the competitiveness of the job market means that, even if you complete 80% of the requirements of a live-coding exercise, you will be rejected in favor of another candidate who completes 100%.

In this post, I describe practice, preparation, and execution that make success in a live-coding exercise more likely. In an accompanying YouTube video, I apply this knowledge and complete a live-coding exercise within forty-five minutes.

This post is not about preparing for and succeeding in data-structure-and-algorithm interviews. Learning materials for those interviews are available elsewhere.

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