Think Like an Architect: Breaking Big Tasks Down

Why AI struggles with big tasks

When a task has multiple components, each one introduces decisions. A big task compounds those decisions: where to put things, what to call them, what to do when something goes wrong, how to organize information, how different pieces talk to each other. Claude Code makes all of these simultaneously, without confirmation, based on its best guess of your preferences.

Some of those guesses will be right. Some will be wrong. And when they're wrong in a big implementation, the fixes are expensive — because they're tangled up with everything else. Changing how the login system works after it's already connected to eight different parts of your project is a two-hour job. Changing it before those connections exist takes ten minutes.

The problem compounds with verification. When Claude Code returns 800 lines of code, how do you check it? You can read it, but you can't easily reason about whether the error handling is correct, whether the login stays working after an hour, whether the Google sign-in handles all the unusual situations. You ship it and find out in production.