Nobody said anything about "correctness". Hallucinations aren't bugs. Everybody writes bugs. People writing code don't hallucinate.
It's a pretty obvious rhetorical tactic: everybody associates "hallucination" with something distinctively weird and bad that LLMs do. Fair enough! But then they smuggle more meaning into the word, so that any time an LLM produces anything imperfect, it has "hallucinated". No. "Hallucination" means that an LLM has produced code that calls into nonexistent APIs. Compilers can and do in fact foreclose on that problem.
Speaking of rhetorical tactics, that's an awfully narrow definition of LLM hallucination designed to evade the argument that they hallucinate.
If, according to you, LLMs are so good at avoiding hallucinations these days, then maybe we should ask an LLM what hallucinations are. Claude, "in the context of generative AI, what is a hallucination?"
Claude responds with a much broader definition of the term than you have imagined -- one that matches my experiences with the term. (It also seemingly matches many other people's experiences; even you admit that "everybody" associates hallucination with imperfection or inaccuracy.)
Claude's full response:
"In generative AI, a hallucination refers to when an AI model generates information that appears plausible and confident but is actually incorrect, fabricated, or not grounded in its training data or the provided context.
"There are several types of hallucinations:
"Factual hallucinations - The model states false information as if it were true, such as claiming a historical event happened on the wrong date or attributing a quote to the wrong person.
"Source hallucinations - The model cites non-existent sources, papers, or references that sound legitimate but don't actually exist.
"Contextual hallucinations - The model generates content that contradicts or ignores information provided in the conversation or prompt.
"Logical hallucinations - The model makes reasoning errors or draws conclusions that don't follow from the premises.
"Hallucinations occur because language models are trained to predict the most likely next words based on patterns in their training data, rather than to verify factual accuracy. They can generate very convincing-sounding text even when "filling in gaps" with invented information.
"This is why it's important to verify information from AI systems, especially for factual claims, citations, or when accuracy is critical. Many AI systems now include warnings about this limitation and encourage users to double-check important information from authoritative sources."
What is this supposed to convince me of? The problem with hallucinations is (was?) that developers were getting handed code that couldn't possibly have worked, because the LLM unknowingly invented entire libraries to call into that don't exist. That doesn't happen with agents and languages with any kind of type checking. You can't compile a Rust program that does this, and agents compile Rust code.
Right across this thread we have the author of the post saying that when they said "hallucinate", they meant that if they watched they could see their async agent getting caught in loops trying to call nonexistent APIs, failing, and trying again. And? The point isn't that foundation models themselves don't hallucinate; it's that agent systems don't hand off code with hallucinations in it, because they compile before they hand the code off.
If I ask an LLM to write me a skip list and it instead writes me a linked list and confidently but erroneously claims it's a skip list, then the LLM hallucinated. It doesn't matter that the code compiled successfully.
