AI, decoded

Are AI hallucinations always bad?

No. A hallucination is the model generating something not grounded in fact, and whether that is bad depends entirely on the use. It is a feature for creative work and dangerous for anything factual, with the worst case being an answer that looks right but is wrong in context.

· Chain of Thought

AI Evaluation & ReliabilityRAG & RetrievalAI Engineering

A chat exchange: “What is CTL?” answered by a confident, plausible reply stamped with a check mark that is crossed out, beside the words: Looks right. Isn’t. The hallucination that hurts.

1. 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. Chip Huyen is blunt about it: hallucinations are going to be really great for creative use cases. There, the invention is the goal.

2. For factual work, it is 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 is factually inconsistent with what is supposed to be true. Most enterprise use cases live here.

3. The dangerous one: looks right, isn’t

Vivienne Zhang’s example, which she credits to Jensen Huang at GTC, sticks. Ask an NVIDIA copilot “what is CTL” and it returns a generic, textbook expansion of the acronym. But for a chip designer inside NVIDIA the right answer is the internal one, “Compute Trace Library.” The response is confident, plausible, and wrong in context. For a new employee, that is just as insidious as a completely wrong answer, because it looks right.

Why it matters

“Stop the model from hallucinating” is the wrong goal. The right goal is to know which mode you are in. If you are doing creative work, let it run. If you are doing factual work, your real job is grounding it and catching the answers that look right but are not.

Are AI hallucinations always bad? Same behavior, two jobs One behavior, the model generating something not grounded in fact, splits two ways. For creative work, such as a poem, a name or a fresh angle, invention is the point: a feature. For factual work it is bad when the answer is factually inconsistent with what is supposed to be true. The dangerous case is an answer that looks right but is wrong in context: asked what CTL means, an NVIDIA copilot gives a generic, textbook expansion, confident and plausible, while for a chip designer inside NVIDIA the right answer is the internal one, Compute Trace Library. The goal is not to stop hallucination but to know which mode you are in: let creative work run; ground factual work and catch the answers that look right but are not. Same behavior, two jobs Whether a hallucination is bad depends entirely on what you are using the model for. the model generates something not grounded in fact CREATIVE WORK · A FEATURE a poem, a name, a fresh angle: inventing something is exactly what you came for. Let it run. FACTUAL WORK · THE RISK bad when it’s factually inconsistent with what is supposed to be true THE DANGEROUS ONE · LOOKS RIGHT, ISN’T “What is CTL?” the copilot answers a generic, textbook expansion confident · plausible what a chip designer inside NVIDIA needs “Compute Trace Library” the internal answer Wrong in context, and just as insidious. Don’t aim to stop hallucination. Know which mode you’re in. Creative work: let it run. Factual work: ground it, and catch the answers that look right but aren’t. Chip Huyen and NVIDIA’s Vivienne Zhang, ep 5. The CTL example is one Vivienne credits to Jensen Huang at GTC.
Know which mode you are in: let creative work run; ground factual work and catch the answers that look right. Vivienne Zhang tells the CTL example in episode 5 and credits it to Jensen Huang at GTC. Download the image

Hear it from the guest

“So I think hallucinations is going to be really, really great things for creative use cases. It's a bad thing for like use cases that depend on factual consistency.”
“The application gives an answer that looks correct, but it's not what the user is looking for, and I will consider that a hallucination.”

Quotes lightly edited to remove filler words.

From the conversation

This explainer is drawn from these episodes — each carries its full transcript.

Concepts in this explainer

AI Hallucination