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Shaw Sha
Shaw Sha

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I Spent 10x Longer Debugging AI Code Than Writing It — Here's What Changed

Everyone talks about AI speeding up coding. Nobody talks about debugging AI-generated code. Last month, I spent three hours hunting down a bug in a 20-line function that an LLM wrote in thirty seconds. That's not a productivity gain—that's a productivity swap. You trade typing speed for debugging speed, and most of the time the trade is terrible.

I've been using AI assistants for about a year now, mostly Claude and GPT-4, and I've noticed a pattern. The first version of any moderately complex piece of code always has at least one subtle mistake. Not syntax errors—those are easy. I'm talking about logical off-by-ones, missing edge cases, or completely hallucinated API calls. And the worst part? The AI writes the code with such confidence that you assume it's correct. You run it, it crashes, and you spend ten minutes thinking you misused the function before you finally look at the generated code with a suspicious eye.

Let me show you a concrete example. I was building a small Node.js service that fetches data from a paginated REST API and merges the results. I asked the AI to write a function that handles pagination with a while loop and an offset parameter. Here's what it gave me:

async function fetchAllPages(baseUrl, limit = 100) {
  let offset = 0;
  let allData = [];
  let hasMore = true;

  while (hasMore) {
    const response = await fetch(`${baseUrl}?limit=${limit}&offset=${offset}`);
    const data = await response.json();
    allData = allData.concat(data.results);
    hasMore = data.results.length === limit;
    offset += limit;
  }

  return allData;
}
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Looks clean, right? I pasted it in, ran my test, and got an infinite loop. The server returned a 400 error after a few requests, but the function kept going because response.ok was never checked. The AI assumed every call succeeds. I spent forty-five minutes debugging that—not because the bug was hard, but because I trusted the output. I added a try/catch and a status check, and then I found the real issue: the API's results array could be empty on the last page, but the AI's logic data.results.length === limit meant that if the last page returned fewer than limit items, it would correctly stop. But if the API ever returned exactly limit items on a non-last page and there was a network glitch that caused a duplicate entry, the loop would hang. I ended up rewriting the whole pagination logic.

That one function cost me about two hours of debugging—roughly 10x the time it took to generate. Multiply that by every file in a project, and suddenly AI isn't saving time; it's creating a tax.

Why does this happen? I think there are a few reasons. First, LLMs are trained on average code, and average code is sloppy. They learn to write the most common pattern, not the robust pattern. Second, they have no concept of your specific environment—your API's quirks, your error handling conventions, your testing framework. They generate code that works somewhere but not here. And third, they write with perfect grammar but zero humility. There's no comment saying "this might break if the network is down" or "you should add rate limiting." It's just flat, confident, wrong.

The kicker? The debugging process is worse than debugging your own code because you didn't write it. You don't have the mental model of why each line is there. You have to reverse-engineer the AI's reasoning, which is often nonsensical. I've seen AI-generated code that imports a library, uses it once in a way that doesn't match the docs, and then never references it again. It's like reading a novel where every paragraph was written by a different author who didn't read the previous ones.

So what changed? I had to stop treating AI output as a finished product and start treating it as a first draft—a very fast first draft. I now follow a strict workflow:

  1. Write the test first. Before I even open the AI chat, I write the expected input and output in a test file. I use Jest for JavaScript, but any framework works. Then I ask the AI to generate code that passes that test. If it doesn't pass on the first try, I feed the test error back into the prompt. This cuts debugging time by about 60% because the AI has a concrete target.

  2. Ask for the minimum, then iterate. Instead of "write a pagination function," I say "write a function that fetches one page given offset and limit." Then I test it. Then I ask for the loop. This prevents the AI from inventing complex logic I didn't ask for.

  3. Verify external dependencies manually. AI loves to hallucinate library APIs. I now keep a browser tab open with the actual documentation. If the AI writes client.query("SELECT * FROM users"), I check if the library even has a query method. Last week it suggested fs.readFileSync with a callback—just wrong.

  4. Use consistent AI models via reliable endpoints. This was the biggest hidden problem. When I used free tiers or rotating API keys, the model behavior changed day to day. One day it generated clean async/await, the next it used .then() chaining with missing error handlers. I needed a stable backend that didn't throttle me or swap models mid-conversation. That's when I started using a pay-as-you-go proxy that gives me consistent access to the same model version. For example, tai.shadie-oneapi.com offers a straightforward API compatible with OpenAI's format, no quotas, no surprises. I pay per request, and the model output is reproducible enough that I can debug against a known baseline. It's not a silver bullet, but it removes the "why did the model change its mind?" frustration from the debugging loop.

After adopting these practices, my effective debugging time dropped from 10x to maybe 2x or 3x. I still spend more time fixing AI code than I would writing the same code from scratch—but the overall time is lower because generation is so fast. The key insight is that AI accelerates the writing part, which was never the bottleneck for experienced developers. The bottleneck is understanding the problem and verifying the solution. AI doesn't help with that unless you structure your workflow around validation.

I still use AI every day. I just don't trust it. And that skepticism, ironically, is what makes me faster. Because now when it gives me a function, I already have a test suite ready, I have the docs open, and I have a stable API endpoint that won't surprise me. The code still has bugs, but I find them in minutes, not hours.

If you're feeling the same pain—spending more time debugging AI code than writing it—try flipping the order. Test first, prompt small, and lock down your model endpoint. You might find that AI becomes the productivity booster everyone promised, instead of the productivity sink it often is.

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