Say it like this
- The model had a hallucination and generated false data.
- We need to reduce hallucinations in our LLM before launch.
- That sounds like a hallucination to me—verify the sources.
Use it at work
Testing AI
- The model had a hallucination: it made up a function that doesn't exist.
- We need to reduce hallucinations in the summaries.
Product
- Show sources so users can check for hallucinations.
- RAG should cut down on hallucinations.
Reporting
- Hallucination rate dropped from 8% to 2%.
- Can you share examples of the hallucinations?
Don't say this
Hallucination is about what the model does (generates false info), not something that breaks systems directly.
Practice
Try it
4 quick exercises
1. The chatbot _______ by providing false citations that sounded completely plausible.
2. You notice your AI model is generating false information. How do you describe this to your manager?
3. Which sentence uses hallucination correctly?
4. Rewrite this using hallucination: The AI model sometimes makes up false information that sounds true but is actually wrong.
Say it out loud
Phrase 1 of 3
"The model had a hallucination and generated false data."
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More about hallucination
Real examples(3)
- in a standup
"Yesterday I discovered that our chatbot was producing hallucinations when answering questions outside its training data."
- in a code review
"We should add validation checks to detect potential hallucinations before returning responses to users."
- in a team meeting
"The main risk with deploying this model is hallucination—it confidently provides incorrect information sometimes."
More mistakes(2)
Handle with care. Do not use to mean a general mistake or bug. Hallucination is specifically when an AI model generates false but plausible information as if it were true.
- ✕We fixed all the hallucinations in the code.✓We implemented guardrails to reduce hallucinations in the AI model.
Hallucinations are AI behavior, not code bugs. You do not fix them the same way as traditional bugs.
- ✕The system had a hallucination error.✓The system experienced a hallucination when processing that query.
Hallucination is already a type of error; saying hallucination error is redundant in tech.
Other forms(3)
- hallucination noun
"The model produced a hallucination when asked about recent events."
- hallucinate verb
"The LLM tends to hallucinate when it lacks training data on a topic."
- hallucinatory adjective
"We noticed hallucinatory outputs in the model response."
Often used with(5)
- reduce hallucinations · Used when implementing strategies to minimize false outputs from AI models.
- detect hallucinations · Identifying when an AI model has generated false information.
- prone to hallucinations · Used to describe AI models that frequently generate false information.
- hallucination problem · The challenge of managing false outputs in AI systems.
- minor hallucination · A small factual error or fabrication from an AI model.
Similar words(3)
- fabrication · More general term; hallucination is specific to AI model behavior.
- false output · Generic description; hallucination is the specific tech term for AI-generated falsehoods.
- confabulation · Academic term with similar meaning; hallucination is more common in tech.
Opposites(3)
- accuracy
- factual correctness
- verified output
Questions(3)
- What is hallucination in AI and machine learning?
- A hallucination is when an AI model, particularly a large language model or generative AI system, generates false, fabricated, or inaccurate information while presenting it with confidence as if it were true. The model does not intentionally lie; rather, it generates plausible-sounding text based on patterns in its training data without any mechanism to verify factual accuracy. This is one of the most significant challenges in deploying AI systems safely.
- How can we reduce hallucinations in AI systems?
- Several strategies help reduce hallucinations: retrieval-augmented generation (RAG) grounds the model in factual sources, using temperature settings can make outputs more deterministic, fine-tuning on high-quality data improves accuracy, and adding explicit uncertainty tokens allows models to express doubt. Additionally, post-generation verification, fact-checking mechanisms, and clear user communication about AI limitations all help mitigate the impact of hallucinations.
- What is the difference between a hallucination and a regular bug?
- A regular bug is a flaw in code logic that produces unintended behavior. A hallucination is fundamentally different: it is the model generating plausible but false information as part of its normal operation. Traditional debugging approaches do not fix hallucinations because the model is not broken—it is behaving as designed. Addressing hallucinations requires different strategies like training data quality, retrieval augmentation, or explicit constraints.
