What are AI hallucinations?
When AIs hallucinate, it's not about fantasy – it's about content that's invented but appears authentic. This happens especially with language models like ChatGPT: They create answers that sound linguistically convincing but are factually incorrect.
The reason: Such models function purely statistically. They calculate which word is likely to come next – without checking whether the statement is actually true. That's why answers can emerge that are linguistically convincing but factually wrong. A well-known example: When asked about Australia's capital, the AI says "Sydney". This sounds right but isn't correct. The correct answer would be Canberra.
And it doesn't stop at text. AI-generated images can also hallucinate, such as an elephant with six legs or a clock with too many hands. Looks right at first glance, but is simply incorrect.
Hallucinations are particularly problematic when AI is used as an information source – whether in customer service, research, studies, or everyday life. Because AI hallucinations often appear very convincing, and that's exactly what makes them so treacherous.
Causes of AI Hallucinations
But why do AI models hallucinate at all? The reasons for AI hallucinations are multifaceted. They can arise through an interplay of training data, model architecture, and the way content is generated:
A key reason lies in the data with which a model is trained. If it's faulty, outdated, or incomplete, this is also reflected in the AI's responses. If current knowledge or specialized expertise on certain topics is missing, this can lead to false statements.
Example: Suppose an AI was trained with texts containing information from the 90s but doesn't consider newer data. If you ask it about current trends, it could deliver false or outdated information, such as an answer referring to then-popular music groups but not current charts.
Another problem is biased training data – for instance, when a model predominantly uses content from certain geographical regions. Then cultural topics might be presented one-sidedly or certain viewpoints overemphasized, giving users the impression of a well-founded statement, although it's not universally valid.
Example: If a user asks about a tradition in Asia, an AI model might mistakenly present a practice from another cultural area as typical – a seemingly plausible but factually false statement.
The methods used to train and develop the model also play a role. Some models generalize too much, others adopt too much from training data – both can lead to erroneous results. Technical weaknesses in architecture, such as too strong a focus on individual words instead of overall context, amplify this effect.
Example: A user asks: "What are the best tips for growing tomatoes in a greenhouse?" The AI responds: "Tomatoes should be planted in direct sunlight." This answer is a generalization that ignores the specific context of the greenhouse, where too much direct sunlight can be harmful.
Furthermore, language models, as already mentioned, don't strive for truth but for linguistically plausible answers. They don't evaluate whether something is right or wrong. They merely calculate which word or information is most likely to come next, based on patterns in the training data. This makes them particularly susceptible to content that sounds good but isn't factually correct – like the answer that Sydney is Australia's capital.
Another example: A student has the AI create an academic text – including sources. The AI then names several titles with author, publisher, and year. But upon closer inspection: The sources are completely made up. Although they sound serious, they don't exist. Such hallucinations occur particularly frequently with academic inquiries and can have serious consequences if adopted unchecked.
The type of input can also play a role. When user questions are imprecise, ambiguous, or contradictory, the probability increases that the answer will also be imprecise or completely wrong.
Example: A user asks: "How long does it take to build a house?" The answer could vary greatly since the question is too vague. Depending on specific circumstances – like size, location, and building materials used – the actual construction time could vary greatly. The model might give a standard answer like "one to two years" without considering these variables.
Finally, another point remains: Many language models are essentially black boxes. Even developers often can't exactly trace why a particular statement comes about in a particular way. This makes it difficult to specifically identify error sources and is one reason why hallucinations continue to represent a major challenge. This lack of transparency not only complicates troubleshooting but also poses a risk for responsible AI use.
Why AI Hallucinations Persist Despite Progress
Despite regular updates and technical developments, the hallucination problem persists – in some cases, it has even worsened. Because with each new model generation, not only capabilities increase but also system complexity. This makes it harder to understand how certain statements come about. Research now speaks of the so-called rebound effect: The better a model can handle complex language, the more credible – and thus riskier – its errors appear.
Internal tests by OpenAI confirm this development: Precisely the newer versions of ChatGPT hallucinate more frequently than earlier models. The GPT-o3 model gives a false answer about every third time when asked about well-known people – more than twice as often as its predecessor. The problem shows even more clearly with the smaller o4-mini model, which achieves error rates of up to 80 percent in individual tests on general knowledge questions.
Another factor lies in training data: Its scope is growing rapidly, but quality doesn't always keep pace. Erroneous content can repeat or come from questionable sources. This increases the danger that language models adopt faulty patterns or weight facts incorrectly. This becomes particularly problematic when AIs are used in sensitive areas like education, science, justice, or medicine. Cases have already occurred where an AI recommended a non-existent chemical as a potential medicine or invented fictitious court decisions complete with case numbers and arguments.
The so-called fine-tuning – subsequent adjustment of a model – also offers no guarantee so far. Often the corrections only work on the surface, while fundamental error structures remain in the system.
All this shows: Technical progress alone isn't enough to reliably exclude hallucinations.
Risks of AI Hallucinations
AI hallucinations might seem like a technical problem at first glance, but they can have serious impacts:
Misinformation spreads quickly
AI-generated false information spreads rapidly – especially because it's often formulated so credibly that many consider it correct and pass it on without hesitation. Combined with the dynamics of social media and digital platforms, a dangerous acceleration of misinformation spread emerges.
Trust in AI diminishes
When an AI system is repeatedly experienced as unreliable, e.g., through contradictory or obviously false statements, users lose trust in the technology itself. This is particularly critical with chatbots serving as the first point of contact in customer service. Those who have bad experiences here turn away from the channel (and sometimes the company). To counteract this, AI systems must communicate reliably, consistently, and comprehensibly.
Real consequences for everyday life
Wrong AI answers don't remain without consequences in real life either. In healthcare, an AI might falsely classify a benign skin change as dangerous if it relies on unreliable information. This can lead to unnecessary examinations and unsettle patients. In the financial sector too, hallucinated data or connections could lead to false credit assessments – with potentially serious consequences for those affected or companies.
Distinguishing true from false becomes more difficult
AI-generated content often appears so realistic that it becomes difficult to distinguish from real information – especially for those not familiar with a particular field. A simple example: An AI could falsely claim that a famous historical event took place in a different location. Many would believe it because the answer sounds so convincing. This problem intensifies when not only text but also images and videos are used, making the distinction between reality and falsification even more difficult.
Ethical and legal questions
AI hallucinations also lead to problems that go beyond technology. When an AI delivers biased or discriminatory answers, for example, this represents not only a technical but also a societal problem. Additionally, legal questions about liability arise when an AI gives erroneous advice that leads to damage. Who bears responsibility when an AI makes false medical diagnoses or gives dangerous recommendations?
Risks to company reputation
Companies using AI in customer service risk reputational damage when erroneous information gets out – such as through a chatbot that makes false statements about prices, availability, or terms and conditions. Such errors can quickly become public, such as in social media or through reviews, and thus significantly damage brand image. Therefore, not only prevention is crucial, but also transparent communication in dealing with errors to maintain trust long-term.
→ What typical chatbot errors look like – and how to avoid them – is shown in the article: "The 6 Biggest Chatbot Fails and Tips on How to Avoid Them".
Solutions: Preventing AI Hallucinations
Although hallucinations in AI models currently can't be completely avoided, there are many ways to at least reduce them. This depends on both developers and users:
6 Mechanisms That Reduce Hallucinations
1. Train with high-quality data
The better and more diverse the data material, the more precise the model becomes. Erroneous, biased, or duplicate data should be specifically avoided.
2. Improve model architecture
Modern techniques like Thinking in Steps (step-by-step thinking) and targeted human feedback help make model responses more structured, comprehensible, and accurate. This can reduce errors and hallucinations.
3. Limit topics and area of application
To ensure the AI only gives well-founded answers and avoids speculation, it should be clearly defined which topic areas it's responsible for. Through targeted filters and limitations, it can be prevented that the AI responds to uncertain or irrelevant questions. This effectively prevents hallucinations.
4. Test thoroughly – before and after going live
Before an AI model goes live, it should be thoroughly tested – preferably not just once, but regularly. This allows errors to be identified early and improved. Tools like SimpleQA help, which specifically check questions and answers and evaluate model quality. And sometimes an outside perspective helps: External checks often bring entirely new insights.
5. Analyze errors systematically
Research is currently working intensively on better understanding the "internal processes" of language models – that is, to look somewhat into the already mentioned black box. Because so far it's often unclear how exactly a model arrives at a particular answer. The goal is to make this process more transparent and intervene correctively when errors occur.
6. Build in protective mechanisms
Technical protective mechanisms like so-called guardrails monitor AI outputs in real-time. They recognize possible hallucinations or implausible answers early and specifically steer responses in the right direction. These reactive tools act as a control instance that accompanies the model during operation and directly corrects or prevents misconduct.
6 Concrete Tips for Users:
1. Question answers
AI doesn't think like humans. Therefore, it's worth approaching AI-generated answers with common sense and always questioning results.
2. Fact-check
Especially with sensitive topics: Better to double-check once more and preferably cross-check with trustworthy sources.
3. Ask clearly
The clearer the question, the clearer the answer. Clear prompts – preferably with examples or additional info – help the AI respond better.
4. Think in steps
Chain-of-thought prompts guide the AI step by step to the answer – and provide more structure in the result.
5. Use settings
Those with access to parameters can, for example, lower randomness (temperature) and thus specifically influence answer quality.
6. Give feedback
Whether like or correction: Every feedback helps make systems better. Many models learn directly from it.
It's important to understand that humans remain responsible. No matter how good a system is, a final check by humans is sensible and necessary.
How moinAI Does It
moinAI specifically employs various measures to avoid AI hallucinations and ensure reliable answers in customer dialogue. At the center is the so-called Retrieval-Augmented Generation (RAG) method. This combines the performance of large language models with a verified Knowledge Base – a centrally maintained knowledge archive that contains exclusively company-relevant content.
Instead of freely hallucinating, the AI chatbot specifically accesses this Knowledge Base. This way it delivers contextual and precise answers – even for complex inquiries. The "world knowledge" that one knows from ChatGPT, for example, doesn't flow into the answers that the chatbot or AI agents give.
Another safety net is the integrated knowledge verification system: When no verified information is available for a question, the chatbot communicates this openly instead of speculating.
Additionally, technical guardrails – safety mechanisms – protect against the chatbot responding outside defined boundaries or generating unreliable content.
Through this combination, customer communication with moinAI remains reliable and creates trust in AI-supported dialogues.
Conclusion: AI – Not Every Sentence a Hit
AI is powerful and impressive – but not infallible. And hallucinations are a symptom of these limitations: seemingly plausible statements that, upon closer examination, lead nowhere. Especially where reliability is crucial, this becomes a challenge.
But there are solutions. Technical advances like RAG, guardrails, or verified knowledge sources show: With a clear concept and responsibility on all sides, the risk can be reduced. This makes AI use increasingly safer and more reliable.
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