this post was submitted on 15 Aug 2025
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To those who have played around with LLM code generation more than me, how are they at debugging?
I'm thinking of Kernighan's Law: "Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it." If vibe coding reduces the complexity of writing code by 10x, but debugging remains just as difficult as before, then Kernighan's Law needs to be updated to say debugging is 20x as hard as vibe coding. Vibe coders have no hope of bridging that gap.
The company I work for has recently mandated that we must start using AI tools in our workflow and is tracking our usage, so I've been experimenting with it a lot lately.
In my experience, it's worse than useless when it comes to debugging code. The class of errors that it can solve is generally simple stuff like typos and syntax errors — the sort of thing that a human would solve in 30 seconds by looking at a stack trace. The much more important class of problem, errors in the business logic, it really really sucks at solving.
For those problems, it very confidently identifies the wrong answer about 95% of the time. And if you're a dev who's desperate enough to ask AI for help debugging something, you probably don't know what's wrong either, so it won't be immediately clear if the AI just gave you garbage or if its suggestion has any real merit. So you go check and manually confirm that the LLM is full of shit which costs you time... then you go back to the LLM with more context and ask it to try again. It's second suggestion will sound even more confident than the first, ("Aha! I see the real cause of the issue now!") but it will still be nonsense. You go waste more time to rule out the second suggestion, then go back to the AI to scold it for being wrong again.
Rinse and repeat this cycle enough times until your manager is happy you've hit the desired usage metrics, then go open your debugging tool of choice and do the actual work.
Reads to me as "Please help us justify the very expensive license we just purchased and all the talented engineers we just laid off."
I know the pain. Leadership's desperation is so thick you can smell it. They got FOMO'd, now they're humiliated, so they start lashing out.
As it seems to be the case in all of these situations, AI fails hard at tasks when compared to tools specifically designed for that task. I use Ruff in all my Python projects because it formats my code and finds (and often fixes) the kind of low complexity/high probability problems that are likely to pop up as a result of human imperfection. It does it with great accuracy, incredible speed, using very little computing resources, and provides levels of safety in automating fixes. I can run it as an automation step when someone proposes code changes, adding all of 3 or 4 seconds to the runtime. I can run it on my local machine to instantly resolve my ID10T errors. If AI can't solve these problems as quickly, and if it can't solve anything more complicated reliably, I don't understand why it would be a tool I would use.
maybe its just me but I find typos to be the most difficult because my brain and easily see it as correct so the whole code looks correct. Its like the way you can take the vowels out of sentences and people can still immediately read it.