How to Escape AI Doom Loops: A Practical Guide

We've all been there—you prompt and prompt and no matter what the AI just gets more and more stuck! I wrote this article as a practical guide to get unstuck when your AI is circling the drain.

I think people sometimes think I’m better at AI than I am. I get stuck in doom loops too! I wanted to write this to give everyone a practical guide to how I’ve evolved ways to escape those doom loops. I hope you find it helpful!

Isn’t it fun to circle the drain?

If you’ve ever tried an AI coding assistant—ChatGPT, Claude, Bolt.new, Lovable.dev, Cursor, or even Google’s experimental Gemini—you know the initial spark of excitement can quickly fizzle into a loop of frustration: half-finished components, overwritten code, or repeated “fixes” that never quite solve the underlying issue. These tools are powerful, but they’re far from perfect, and many developers find themselves stuck in doom loops: cycles of endless re-prompting that yield little real progress.

In this guide, we’ll look at why these pitfalls happen and how you can adapt your workflow to make AI a helpful co-pilot rather than a source of never-ending friction. We’ll dig into the deeper reasons behind these loops—particularly the clash between high-level architecture and low-level implementation. Then, we’ll examine practical strategies that separate the two and keep your projects on track.