Key takeaways
- An AI hallucination is a fluent, confident output that is false or unsupported by the source or reality.
- It happens because models predict plausible text, and training and evaluation often reward guessing over admitting uncertainty.
- Famous cases include fake legal citations filed in court and a chatbot inventing an airline refund policy.
- Grounding with RAG, citations, refusal rules and verification reduce hallucinations but do not eliminate them.
- In medicine, hallucinations are perceptions without a real stimulus, a symptom that needs professional care.
AI hallucination is when a generative AI model produces output that sounds confident and fluent but is false, invented or unsupported, such as a fake citation, a non-existent policy or a wrong date stated as fact. It is one of the main risks of using language models for real work. Reducing it is a core design goal of RAG.NextUpgrad, which grounds answers in retrieved sources and declines when evidence is weak. This page explains the term, why it happens, real examples, and the unrelated medical meaning.
What is hallucination in generative AI?
In generative AI, a hallucination is content that is not grounded in the input, the provided sources or reality, yet is presented as true. It includes invented facts, fake references, wrong numbers and confident answers to questions the model cannot actually answer.
Also asked as: what is hallucination in gen ai
Researchers distinguish intrinsic hallucinations, which contradict the source material, from extrinsic ones, which add claims that cannot be verified from it. Image models can hallucinate too, adding objects or text that were not requested. Hallucinations matter most in high-stakes areas such as law, medicine, finance and education, where a plausible error can cause real harm.
What does AI hallucination refer to?
It refers to an AI system generating information that seems plausible but is incorrect or fabricated. The key feature is confidence without grounding: the output reads as authoritative, so users may not notice the error.
Examples range from small, like a slightly wrong year, to serious, like an invented legal case or medical dosage. It is different from a model refusing or saying it is unsure, which is the desired behaviour when information is missing. It also differs from bias, though both are reliability problems. Good systems are designed so that hallucinations are rare, visible and easy to verify.
Why is it called AI hallucination?
The term borrows from psychology, where a hallucination is a perception of something that is not there. Applied to AI, it describes a model "seeing" facts that do not exist. It was used in computer vision and machine translation research before chatbots made it widely known in 2022 and 2023.
Some researchers criticise the term for making models sound human, and suggest "confabulation" or simply "fabrication", since models do not perceive anything. The name stuck in public use anyway. Whatever you call it, the practical meaning is the same: generated content that is not supported by facts or sources.
Why does AI hallucination occur?
Language models generate the most plausible next words based on patterns in training data, not by checking facts. When they lack information, they still produce fluent text. Training and benchmarks that reward correct answers but not honest uncertainty encourage guessing.
Contributing factors:
- Gaps or errors in training data, and knowledge cut-off dates.
- Questions about rare facts, such as specific citations or numbers.
- Prompts that pressure the model to answer.
- Long contexts where details get mixed up.
- Retrieval that returns irrelevant passages in RAG systems.
Mitigations include grounding answers in retrieved documents, requiring citations, allowing and rewarding "I don't know", and verifying outputs against sources.
What are examples of AI hallucinations?
Common examples include invented academic references with real-looking authors and journals, fake legal cases, made-up quotes attributed to real people, wrong statistics, non-existent product features, and confident biographies containing false details.
In coding, models sometimes suggest functions or packages that do not exist, which attackers have exploited by publishing malicious packages with those hallucinated names. In images, models add extra fingers or garbled text. In summaries, models may add conclusions not in the original document. Each example shares the pattern: plausible form, false content, delivered without hesitation.
What are examples of ChatGPT hallucinations?
Well-known ChatGPT hallucinations include fabricated legal precedents submitted by lawyers to a US court in 2023, invented academic citations in student essays, and false biographical claims about real people, some of which led to complaints and legal challenges.
Also asked as: hallucination examples chatgpt
Users also report ChatGPT confidently giving wrong arithmetic in long calculations, inventing book chapters or page numbers, and describing software settings that do not exist. Newer models with web search and citations hallucinate less on factual questions, but errors still occur, especially without search. Always check citations by opening them, and verify any fact you plan to rely on.
What are examples of LLM hallucinations in general?
Across language models, typical hallucinations include fake citations, invented API functions, incorrect historical dates, misattributed quotes, and summaries that include claims absent from the source. They appear in every major model to some degree.
Also asked as: hallucination examples llm
A notable early case: in Google's Bard launch demo in February 2023, the chatbot wrongly claimed the James Webb Space Telescope took the first picture of a planet outside our solar system. In RAG systems, a common failure is answering from general knowledge when retrieval misses, which is why RAG.NextUpgrad uses a confidence threshold to refuse rather than guess.
What are AI hallucination examples in the news?
High-profile cases include the 2023 Mata v. Avianca case, where lawyers were sanctioned for filing a brief with ChatGPT-invented cases; Air Canada being held liable in 2024 for its chatbot inventing a refund policy; and Google's AI Overviews suggesting glue on pizza in 2024.
Also asked as: hallucination examples ai · ai hallucination examples news · ai hallucination examples reddit
These stories share lessons: organisations are responsible for what their AI says, professionals must verify AI-generated content, and AI features should cite sources and be tested on edge cases before launch. Courts in several countries now require lawyers to disclose or verify AI-assisted filings, and companies add review steps before AI outputs reach customers.
What AI hallucination examples do users share online?
Users on forums such as Reddit frequently share hallucinations like chatbots inventing song lyrics, giving fake recipes, misquoting book plots, creating non-existent research papers, and insisting on wrong answers when challenged.
Popular threads also show models apologising and then repeating the same mistake, or inventing details about the user's own life. These examples are useful reminders, but they are anecdotes, not measurements; model versions and settings differ. For a fair picture of reliability, look at systematic evaluations, and test the specific tool you use on questions from your own domain where you know the correct answers.
What are examples of hallucinations in schizophrenia?
In schizophrenia, hallucinations are perceptions without a real external stimulus, most commonly hearing voices that others do not hear, such as voices commenting on the person or giving commands. Visual, touch, smell and taste hallucinations can also occur.
Also asked as: hallucination examples schizophrenia
This medical meaning is unrelated to AI hallucinations, beyond the borrowed word. Hallucinations can be distressing and are treatable; schizophrenia is managed with medication, therapy and support. If you or someone you know experiences hallucinations, seek help from a doctor or mental health professional, and in a crisis contact local emergency services. Using clear, respectful language matters, since the condition is often stigmatised.
Sources
- Ji et al., Survey of Hallucination in Natural Language Generation (2022)arxiv.org
- OpenAI, Why language models hallucinateopenai.com
- Wikipedia, Hallucination (artificial intelligence)en.wikipedia.org
- Wikipedia, Mata v. Avianca, Inc.en.wikipedia.org
- National Institute of Mental Health, Schizophrenianimh.nih.gov
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
