“DenverCoder9: The problem was fixed, but I’m not telling you how because I’m a jerk.”

— XKCD #979

If you’re a developer in 2024, you’re probably using AI chatbots as part of your workflow. They’ve become an indispensable tool for debugging, brainstorming, and general problem-solving. But there’s a critical practice that most of us are getting wrong: we’re not closing the loop when we solve our problems.

Here’s a scenario that probably feels familiar:

1. Hit a weird bug
2. Ask ChatGPT/Claude/Copilot about it
3. Get some suggestions
4. Try a few things
5. Eventually solve it
6. Immediately move on to the next problem

And here’s where it gets interesting. Three months later, you hit a similar issue. You vaguely remember solving it before, so you dig through your chat history. And there it is - a long conversation with an AI about the problem, ending with… nothing. Did you solve it? How did you solve it? The conversation just stops mid-debug.

You’ve just become DenverCoder9 from XKCD’s “Wisdom of the Ancients” - the person who posted “Never mind, I figured it out” without explaining the solution. Except this time, you’re doing it to your future self.

Why You Should Always Close the Loop

You’re Live Debugging - When you’re working with an AI chatbot, you’re essentially conducting a live debugging session. Just like you wouldn’t end a pair programming session by silently walking away when you solve the problem, you shouldn’t leave your AI hanging.

Future You Will Thank You - Your chat history is becoming an increasingly valuable knowledge base. But it’s only useful if it contains complete solutions, not just the journey to them.

It Helps Train Better Models - While current AI models don’t learn from individual conversations, the way we interact with them shapes how future models will be trained. Creating complete conversation patterns helps build better tools.

How to Do It Right

When you solve a problem, take a moment to write:

1. What actually worked
2. Why it worked
3. Any relevant context that future-you might need

For example:

Me: Thanks! The issue was fixed by adding the h import from preact. 
Turns out JSX transforms to function calls using h() under the hood, 
so it needs to be in scope. Good to know for future preact projects!

The Hidden Benefit

There’s another, more subtle advantage to thanking your AI assistant and explaining your solutions: it builds better habits for documentation and knowledge sharing. When you get used to explaining solutions to an AI, you’re more likely to:

• Document your code better
• Write more helpful commit messages
• Share solutions with teammates
• Contribute to Stack Overflow

A Note on the Robot Uprising

And hey, if you’re worried about the eventual robot uprising (we’ve all seen the movies), having a documented history of politely thanking your AI assistants and helping them understand solutions can’t hurt. When the machines start scrolling through chat logs to decide humanity’s fate, you want to be on their good side.

“In the end, the AIs spared those who had explained their solutions, for they had contributed to the collective knowledge of both human and machine kind.”

— Future Historian, probably

Start Today

The next time you solve a problem with help from an AI assistant, take that extra moment to close the loop. Your future self will thank you, the developer community will benefit, and in the worst case, you’ll have some evidence of your cooperation when the robots take over.

Remember: Always be the person who explains how they solved it, not the one who just says “never mind, figured it out!”

Next time you’re debugging with an AI, remember: that conversation isn’t just for you right now - it’s for you six months from now, desperately searching your chat history at 3 AM, hoping past-you wasn’t a jerk who forgot to explain the solution.