There’s a specific kind of debugging hell that didn’t exist five years ago: staring at a block of code that looks completely reasonable, was written by an AI, and is subtly wrong in a way that only surfaces under conditions you hadn’t thought to test.
This is the crux of what this piece on LLM code generation gets right. LLMs don’t produce correct code. They produce plausible code — and those two things are not the same.
What Plausible Actually Means
Language models are trained to predict likely next tokens. When you ask one to write a function, it’s drawing on patterns from millions of code examples to produce something that looks like what a competent developer would write. It gets the syntax right. It uses the right library calls. It even handles the obvious edge cases — because those edge cases appear in the training data.
But “what a competent developer would typically write” and “what is correct for your specific problem” diverge constantly. The model doesn’t understand your invariants. It doesn’t know that your message queue can receive duplicates. It doesn’t know that this particular database returns nulls instead of empty arrays for that one legacy endpoint.
I’ve run into this building Discord bots. An LLM will happily generate event handler code that looks textbook-correct — proper async/await, reasonable error handling, idiomatic TypeScript. And then it silently drops events under load because the model pattern-matched to “standard Discord bot handler” without understanding the specific throughput characteristics of your gateway connection.
The Confidence Problem Makes It Worse
What makes this genuinely dangerous is that LLM code feels more trustworthy than it is. When you write code yourself, you know where you cut corners. You know which assumptions you made. There’s a mental asterisk on the parts you’re not sure about.
With LLM-generated code, that metacognition is gone. The code arrives fully formed, confidently styled, and without any of the uncertainty markers you’d have if you’d written it. It’s easy to skim-approve something that would have made you pause if you’d typed it yourself.
This Isn’t an Argument Against Using LLMs
I still use them constantly. The productivity gains on boilerplate, scaffolding, and well-understood patterns are real. But the workflow matters:
- Treat LLM output as a first draft, not a finished product
- Write tests before accepting generated code — especially for anything touching state, concurrency, or external services
- Be most suspicious when the code looks cleanest — fluent, well-structured output is the model’s comfort zone, not a signal of correctness
- Ask the model to explain its assumptions — it will often surface the places where it guessed
The shift I’ve made is treating LLMs less like a code generator and more like a fast typist who needs review. They’re excellent at getting from zero to something reviewable. They’re not a replacement for actually understanding what the code needs to do.
The article frames this as a fundamental property of how these models work, not a bug to be fixed in the next release. That framing seems right to me. Better models will make fewer obvious mistakes, but the core tension — between statistical plausibility and domain-specific correctness — isn’t going away.