LLMs write plausible code not correct code, and what that distinction means for engineers in production
Plausible Is Not Correct, and That Gap Is Where Production Dies
There is a framing going around right now that I think every engineer working with AI-generated code needs to internalize before they ship another feature. It comes from a post by @KatanaLarp, and the core argument is this: LLMs do not write correct code. They write plausible code.
Read that again. Because the difference is not academic.
🔍 What Plausible Actually Means
Plausible code compiles. It runs. On the happy path, with clean inputs and nominal traffic, it produces output that looks exactly right. A junior engineer reviewing it in a PR might wave it through. Your CI pipeline will almost certainly wave it through.
But plausible is not a semantic guarantee. It is a statistical one. The model predicted the next token based on patterns in training data. It did not reason about your specific schema, your transaction isolation level, your concurrent write patterns, or what happens when two users hit the same endpoint at 2am on a Tuesday during a sale event.
@KatanaLarp used SQL as the concrete example, and it is a good one precisely because SQL failures are so quiet. A query that returns wrong results is much harder to catch than a query that throws an exception. The model has seen thousands of SQL examples. It knows what a JOIN looks like. It will produce a JOIN that looks completely reasonable, that might even be reasonable in most contexts, and that silently returns incorrect row counts under specific load conditions.
That is not a hallucination problem. That is a plausibility problem, and they require different solutions.
Why Engineers Keep Getting Fooled
The demos are curated. Every time you see an LLM write a working web server in thirty seconds, someone chose a problem that fits neatly into the model’s training distribution. Nobody demos the query that works perfectly until your table hits 10 million rows and a certain index stops being used by the planner. Nobody demos the race condition that only appears under concurrent writes.
The deeper issue is that LLMs are trained on code that was written by humans, reviewed by humans, and committed to repositories. That code is not uniformly correct. It reflects the full spectrum of human competence, including the bugs that shipped, the workarounds that became permanent, and the “good enough for now” solutions that are still running in production somewhere.
When a model learns from that corpus, it learns to reproduce patterns that look like the code humans write. Most human-written code is fine most of the time. But production systems do not fail most of the time. They fail at the edges.
⚙️ What This Means for Your Workflow Right Now
The instinct after hearing this is often to distrust AI coding tools entirely, which is the wrong move. The instinct to trust them unconditionally is obviously worse.
The useful frame is this: treat LLM-generated code the way you would treat code from a very fast, very confident intern who has read every Stack Overflow answer ever posted but has never been paged at 3am.
Practically, that means a few things.
Your review process cannot just check for syntax and logic structure. It has to check for correctness against your specific system’s invariants. What are the concurrency assumptions? What happens at the boundary conditions? What does “wrong but silent” look like for this particular piece of code?
The CLAUDE.md approach that @BharukaShraddha wrote about points at part of the answer. If you structure your repository so the model has explicit access to your constraints, your architectural decisions, and your known sharp edges, you get better outputs. Local CLAUDE.md files near risky modules like auth or billing or migrations are a concrete way to push constraint information into the model’s context at exactly the moment it needs it. That does not solve the plausibility problem, but it narrows the gap.
Testing has to be adversarial, not confirmatory. Most test suites test the happy path. They confirm that the code does what it is supposed to do when everything goes right. What you need, especially for LLM-generated code, is tests that probe the conditions under which plausible-but-wrong code breaks.
The Skill That Actually Gets More Valuable
Here is my actual take on where this lands for engineers.
The people who treat AI coding tools as autocomplete on steroids are going to accumulate hidden debt. Fast output, low correctness verification, subtle bugs in production. That is the failure mode nobody in the demo videos is showing you.
The people who develop a sharp instinct for where plausible diverges from correct, who build test infrastructure that catches silent failures, who understand their systems deeply enough to know which pieces are safe to delegate and which are not, those engineers are not being replaced. They are becoming the last line of defense against a class of bugs that is genuinely new.
The skill is not “knowing how to prompt.” The skill is knowing what questions to ask of the output once you have it.
OpenAI’s Codex Security release this week, which focuses on finding and validating vulnerabilities in AI-generated codebases, is a signal that even the model providers know this problem is real and growing. You do not build a security agent for correct code. You build it for plausible code that shipped.
Get comfortable with that distinction. Your production systems will thank you.
Sources
#AIEngineering #SoftwareEngineering #LLMs #ProductionSystems #CodeReview
