Are AI hallucinations always bad?
No. A hallucination is the model generating something not grounded in fact, and whether that's bad depends entirely on what you're using it for — great for creative work, dangerous for anything factual. The most useful case is the third one: the answer that looks right and isn't. 'Stop the model from hallucinating' is the wrong goal; the right goal is knowing which mode you're in and grounding the model when the task depends on being correct.
AI Evaluation & ReliabilityAI Engineering
For creative work, hallucination is the feature
When you want a poem, a name, a fresh angle, the model inventing something outside its training is exactly what you came for. It isn’t a bug there, it’s the whole point.
For factual work, it’s the bane of your existence
The moment the task depends on being correct, the same behavior becomes the risk you spend all your time fighting. A hallucination is bad specifically when it’s factually inconsistent with what’s supposed to be true. Most enterprise use cases live here.
The dangerous one: looks right, isn’t
Ask a chip-design copilot “what is CTL” and it might answer “combinational time logic,” a real term — but for a designer at that company the right answer is “Compute Trace Library.” The answer is confident, plausible, and wrong in context. For a new employee that’s as harmful as a completely wrong answer, maybe more, because they won’t catch it.
Why it matters
The goal isn’t zero hallucination. It’s knowing which mode you’re in: if you’re doing creative work, let it run; if you’re doing factual work, your real job is grounding the model and catching the answers that look right but aren’t.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
