Recovering from LLM Corner-Writing

When using LLMs for coding, you’ll inevitably hit that moment when the model writes itself into a corner. The code doesn’t work, the implementation has gone sideways, and you’re left wondering what the hell happened. Let’s talk about how to deal with this frustrating but common situation.

Preemptive Defense

The first steps to protect against this are obvious: detailed plan, decomposed into small steps, small commits, short branches. This isn’t revolutionary advice, but it’s your first line of defense against a complete derailment.

The Philosophical Shift

The next is a philosophical change. Detach from the work product. It doesn’t matter at all. Be ready to trash everything you did at a moment’s notice and start again. This mental shift is crucial—your ego can’t be tied to code an AI helped you write.

The Recovery Protocol

The real question is, how do you start again? The steps are:

1. Run git diff to get your changes
2. Make your own observations about why it failed
3. Repack your project or a relevant chunk of it
4. Send all that shit to Claude and ask for a postmortem on why it failed
5. Send your implementation plan (you do have one, RIGHT??)
6. Ask for a revised implementation plan based on what you learned

Then? Start. The. Fuck. Over.

Spotting the Rabbit Hole Before You’re Too Deep

You have to look. If you’re going fast, you aren’t reading all the code, so it better be dead ass simple. This is where most people fail—their eyes glaze over when reviewing LLM output, especially if it’s lengthy.

Signs you’re heading down a shitty rabbit hole:

1. The model keeps generating increasingly complex abstractions
2. You’re seeing repetitive patterns the model can’t seem to simplify
3. Basic functionality requires mounting layers of dependencies
4. Simple changes trigger cascading modifications across multiple files

You have to use the model to review its own work. Yes, that sounds circular, but it works. Have it explain the implementation logic back to you in plain English. If it can’t articulate a clear, coherent explanation, you’re already in trouble.

You need totally rad integration tests that test real world shit. It’s free (almost). Have the model write them. Think playwright. If your tests are just unit tests mocking everything, you’re not catching the real-world issues that will bite you later.

Run the app for god’s sake. I’m lazy and things slip past me because I don’t always do this. Actually execute the code, click the buttons, submit the forms. LLMs are notorious for creating beautiful-looking code that fails spectacularly when actually executed.

When Everything Falls Apart

Sometimes you need to recognize when you’re throwing good time after bad. LLMs will occasionally produce implementations that are fundamentally flawed in design. No amount of incremental fixing will save them.

Recognize this quickly. You’re looking for implementation difficulties that seem to multiply rather than resolve as you work through them. If every fix creates two new problems, you’re building on quicksand.

Conduct The Autopsy

When your LLM-assisted project implodes, don’t just rush to restart. Take time to understand where things went wrong. This isn’t just about fixing the current issue—it’s about improving your prompt engineering and workflow for future projects.

The difference between an LLM-coding novice and expert isn’t that experts never fail—it’s that experts learn systematically from each failure.

Reframing The Relationship

Think of the LLM as an eager but drunk junior developer who occasionally misunderstands complex instructions. Sometimes the best approach is to simplify the task, provide clearer examples, or break the work into smaller chunks.

While LLMs can produce impressive code, they lack the fundamental understanding that comes from years of engineering experience. They’re tools, not replacements for human judgment.

Conclusion

Recovering from an LLM coding disaster isn’t just about technical fixes—it’s about having the right mindset. Detach from the code, analyze the failure systematically, and be willing to start fresh with better information.

The most productive developers working with LLMs aren’t the ones who never experience failures—they’re the ones who’ve built robust recovery processes when those failures inevitably occur.