A professional translator produces roughly 2,000 to 3,000 words per day. At that rate, localizing 65,000 words of app content from English to German would take a single translator three to four weeks. Seven AI agents, running in parallel with a fan-out/fan-in architecture, completed the same work in thirty-three minutes. The effective rate was 216 times faster than a human translator. This post describes how that happened, what went wrong, and what the speedup actually means.

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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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