AI chatbots hallucinate when they generate information that sounds confident and fluent but is partially or entirely fabricated. This happens routinely, even with the most advanced models available today. GPT-4 Turbo, one of the best-performing models on hallucination benchmarks, still produces false or unsupported claims roughly 19% of the time when faced with challenging real-world queries. Less capable models hallucinate significantly more often. Understanding why this happens, and what it looks like in practice, helps you use these tools without getting burned.
Why Chatbots Make Things Up
AI chatbots don’t retrieve facts from a database the way a search engine pulls up a webpage. Instead, they predict the next word in a sequence based on statistical patterns learned during training. Every response is essentially a probability game: the model picks the most likely next token, then the next, then the next, building sentences that read naturally but aren’t anchored to any source of truth. The model has no internal fact-checker. It doesn’t “know” anything in the way a person knows their own address. It has learned what plausible-sounding text looks like, and it produces more of it.
This architecture means hallucinations aren’t bugs in the traditional sense. They’re a predictable consequence of how these systems work. When the model encounters a question it doesn’t have strong training signal for, it doesn’t say “I’m not sure.” It fills the gap with whatever continuation scores highest on its internal probability distribution. The result can be a fabricated statistic, a nonexistent research paper, or a confident but wrong explanation of how something works.
Training Data Sets the Ceiling
These models are trained on massive amounts of internet text, which contains both accurate and inaccurate information, along with cultural and societal biases. The model learns patterns from all of it without distinguishing fact from fiction. If a false claim appears frequently enough in training data, the model may reproduce it with the same confidence as a well-established fact. Gaps in the training data create a different problem: when the model has limited exposure to a topic, it interpolates from whatever loosely related patterns it has, often producing plausible-sounding nonsense.
Contradictions in training data compound the issue. If the model has seen conflicting claims about a medical treatment or historical event, it has no reliable way to adjudicate between them. It may confidently assert one version in one conversation and the opposite version in another, depending on how the question is phrased and which patterns get activated.
Settings That Increase the Risk
Behind every chatbot response is a parameter called “temperature” that controls how creative or conservative the model’s word choices are. A lower temperature makes the model stick closely to its most probable predictions, producing more predictable (and generally more accurate) output. A higher temperature introduces more randomness, letting the model explore less likely word choices. This can make responses more creative and varied, but it also increases the chance of the model wandering into fabrication. Research published in Nature found that sampling at a very low temperature (0.1) produces the model’s “best generation,” meaning its most reliable output, while higher settings introduce more variability.
Other sampling parameters work similarly. Techniques like nucleus sampling, which limits the pool of candidate words to those covering the top 90% of probability, help constrain the model’s choices but don’t eliminate hallucination. The fundamental issue remains: even the highest-probability word sequence can be factually wrong.
What Hallucinations Look Like in Practice
Hallucinations aren’t always obvious. They range from subtle distortions (slightly wrong dates, misattributed quotes, inflated statistics) to wholesale fabrication (invented research studies, fake legal citations, fictional people presented as real). The most dangerous hallucinations are the ones embedded in otherwise accurate text, where one false detail hides among several true ones.
The legal profession has provided some of the most visible cautionary tales. In a 2025 case before the U.S. Fifth Circuit Court of Appeals, a judge identified 21 instances of fabricated quotations or serious misrepresentations of law in a single legal brief. The lawyer, who initially denied using AI and pointed to legal databases as the source of errors, was ordered to pay $2,500 in sanctions after the court called her explanations “not credible” and “misleading.” The judge noted that lesser sanctions would have been imposed had the lawyer simply accepted responsibility. As of early 2025, a database maintained by a legal researcher had catalogued 239 separate cases of AI-generated hallucinations appearing in legal filings submitted by lawyers in the United States.
These aren’t isolated incidents from early, primitive models. They keep happening because the underlying technology hasn’t solved the core problem, and professionals under time pressure don’t always verify what the chatbot produces.
Some Topics Trigger More Hallucinations
Hallucination rates aren’t uniform across all types of questions. Researchers behind the HaluEval-Wild benchmark, the first major evaluation designed around real-world user queries rather than narrow academic tasks, sorted 500 challenging questions into five categories and tested multiple models against them. The benchmark was specifically designed to capture the kinds of questions people actually ask chatbots, not the clean, well-defined tasks used in earlier evaluations focused on translation or text summarization.
Questions that push the boundaries of a model’s knowledge and reasoning are where hallucinations spike. Highly specific factual queries (names, dates, niche statistics), questions requiring multi-step reasoning, and prompts about recent events the model wasn’t trained on all carry elevated risk. Broad, well-documented topics like “how does photosynthesis work” are relatively safe. “What were the specific budget allocations in a small city’s 2023 infrastructure plan” is where things fall apart.
How Companies Are Reducing Hallucinations
The most promising technical approach is called retrieval-augmented generation, or RAG. Instead of relying solely on what the model learned during training, RAG systems fetch relevant documents from external sources at the time of your query and feed that information to the model as context. This grounds the response in actual source material rather than pattern-matching alone.
Standard RAG helps but doesn’t solve the problem completely. It reduces outright fabrication, but models using basic RAG still sometimes present speculative or poorly supported claims. The retrieval step can miss relevant documents or pull in loosely related ones that lead the model astray. More advanced implementations address this with multiple retrieval passes, re-ranking of sources by relevance, and scoring systems that check whether the generated answer is actually supported by the retrieved evidence. One such system, described in a 2025 study in Frontiers in Public Health, reduced hallucination rates by over 40% compared to standard approaches by layering these verification steps together.
The tradeoff is speed and cost. Each additional verification step requires more computation, which means slower responses and higher operating expenses. Companies are constantly balancing accuracy against the seamless, instant experience users expect.
How to Spot Hallucinations Yourself
Since no model is hallucination-free, the practical skill is learning to catch them. A few patterns are worth watching for.
- Overly specific details on obscure topics. If a chatbot gives you a precise date, a direct quote, or a specific statistic for something you can’t easily verify, treat it as suspect until you confirm it independently. Models are most likely to fabricate exactly the kind of granular detail that makes a claim feel authoritative.
- Citations and references. Chatbots frequently invent academic papers, complete with realistic-sounding titles, author names, and journal names. If a response includes a citation, search for it directly. A significant percentage of AI-generated citations don’t correspond to real publications.
- Confident answers to ambiguous questions. When you ask something that should reasonably have a hedged or nuanced answer and the model responds with absolute certainty, that confidence may be masking uncertainty the model can’t express.
- Ask the same question differently. If you rephrase your question and get a meaningfully different answer, the model likely doesn’t have strong grounding on the topic. Consistent answers across phrasings suggest (but don’t guarantee) more reliability.
The single most effective habit is simple: verify any claim that matters before you act on it. Chatbots are useful for brainstorming, drafting, summarizing, and exploring ideas. They are unreliable as sole sources of factual information, and treating them otherwise is how fabricated legal citations end up in court filings.