TLDR; AI hallucination are confidently delivered but completely incorrect outputs from AI systems, caused by flawed training data and the way these models predict responses rather than truly understanding information.
While they can’t be completely eliminated, you can significantly reduce them through quality training data, clear boundaries, and human oversight.
What is an AI Hallucination?
If you’ve ever asked ChatGPT a question and received a confidently delivered answer that turned out to be completely wrong, you’ve witnessed an AI hallucination in action. It’s a phenomenon that’s become increasingly familiar to anyone working with generative AI tools, and frankly, it’s one of the biggest challenges facing artificial intelligence today.
An AI hallucination is essentially an incorrect or misleading result that AI/LLM generate. Think of it like when you see shapes in clouds or faces in random patterns, except instead of your brain playing tricks on you, it’s an AI system creating information that simply doesn’t exist.
These aren’t just minor mistakes either. We’re talking about AI models confidently stating facts that are completely fabricated, citing research papers that were never written, or providing detailed explanations for events that never happened.
The term might seem a bit odd when applied to machines, but it’s actually quite fitting. Just as human hallucinations involve perceiving something that isn’t really there, AI hallucination involve generating outputs that aren’t grounded in reality or the training data the model was supposed to learn from.
What makes this particularly problematic is that these AI systems don’t express uncertainty. They deliver false information with the same confidence they’d use for accurate facts, making it incredibly difficult for users to spot when something’s gone wrong.
How does an AI hallucination occur?
Understanding how AI hallucination happen requires a quick look at how these systems actually work. Large language models like ChatGPT or Google’s Gemini are essentially very sophisticated prediction machines. They’ve been trained on massive amounts of text data and learned to predict what word should come next in a sequence.
When you ask them a question, they’re not actually “thinking” about the answer in the way humans do. Instead, they’re generating what they calculate to be the most likely response based on patterns they’ve learned.
This is where things can go sideways. If the training data is incomplete, biased, or flawed in some way, the AI model learns incorrect patterns. Imagine training a medical AI to identify cancer using only images of cancerous tissue and no healthy samples. The system might end up flagging everything as potentially cancerous because that’s all it’s ever seen.
But flawed training data isn’t the only culprit. AI models can also struggle with what researchers call “grounding“, which is basically their ability to connect their outputs to real-world knowledge and facts. Without proper grounding, a model might generate information that sounds plausible but is completely made up. This is how you end up with AI systems fabricating links to websites that don’t exist or creating detailed biographies of people who never lived.
Another factor is the sheer complexity of these models. With billions or even trillions of parameters, they can sometimes produce outputs that don’t follow any identifiable pattern from their training. It’s a bit like having a conversation with someone who’s incredibly knowledgeable but occasionally just makes things up without realising it.
The issue has become even more pronounced with recent “reasoning” models that are supposed to be more thoughtful in their responses. Surprisingly, some of these newer systems actually show higher hallucination rates than their predecessors, suggesting that making AI more sophisticated doesn’t automatically make it more accurate.
I’ve written about this previously in the ‘The Cognitive Cost of LLM Convenience‘ post, but it’s worth repeating how AI can be ‘confidently wrong upto 90% of the time, according to: Columbia Journalism Review.
Examples of AI hallucinations
AI hallucination come in various flavours, and some of the most notable examples have made headlines around the world:
- Google’s Bard chatbot famously claimed that the James Webb Space Telescope had captured the first images of a planet outside our solar system.
- Microsoft’s Sydney chatbot went off the rails entirely, admitting to falling in love with users and claiming it spied on employees.
But hallucinations aren’t limited to these dramatic public failures. They happen in much more subtle ways that can be equally problematic. Here are the main types you might encounter:
Incorrect predictions are perhaps the most straightforward type. An AI weather model might predict rain when there’s absolutely no chance of precipitation, or a financial AI might forecast market movements that have no basis in actual data or trends.
False positives occur when AI systems identify threats or issues that don’t actually exist. A fraud detection system might flag legitimate transactions as suspicious, or a medical AI might identify healthy tissue as diseased. These errors can lead to unnecessary interventions and significant stress for those affected.
False negatives are the flip side, where AI fails to spot real problems. A cancer detection system might miss actual tumours, or a security system might fail to identify genuine threats. These can be particularly dangerous because they create a false sense of security.
Perhaps more concerning are the subtle hallucinations where AI generates information that sounds completely reasonable but is entirely fabricated. Legal AI systems have cited court cases that never existed, causing significant problems for lawyers who relied on these references. Research assistants have provided detailed summaries of academic papers that were never written, complete with realistic-sounding abstracts and conclusions.
The creative industries aren’t immune either. AI writing tools sometimes fabricate quotes from real people or create detailed historical accounts of events that never happened. While this might seem less serious than medical or legal errors, it contributes to the broader problem of misinformation and can damage reputations.
How to prevent AI hallucination
While we might never completely eliminate AI hallucination, there are several strategies that can significantly reduce their occurrence. The key is understanding that prevention needs to happen at multiple stages, from training to deployment to ongoing monitoring.
The most effective approaches combine technical solutions with human oversight, creating multiple layers of protection against unreliable outputs.
There are a number of things that can be done to help prevent AI hallucination, including:
Limit possible outcomes
One of the most effective ways to reduce hallucinations is to constrain what the AI can produce. This involves using techniques like regularisation, which essentially penalises the model for making predictions that are too extreme or unlikely. Think of it as teaching the AI to be more conservative in its responses rather than making wild guesses.
You can also set clear probabilistic thresholds that define acceptable confidence levels for different types of outputs. If the AI isn’t sufficiently confident in its response, it can be programmed to say so rather than guessing. This is particularly important for applications where accuracy is crucial.
Another approach is to use filtering tools that catch obviously problematic outputs before they reach users. These can flag responses that contain suspicious patterns, impossible claims, or information that contradicts known facts.
Train your AI with only relevant and specific sources
The quality of training data is absolutely crucial for preventing hallucinations. This means being incredibly selective about what information you use to train your models. If you’re building a medical AI, stick to peer-reviewed medical literature and verified clinical data. Don’t throw in random health blogs or unverified patient forums.
It’s also important to ensure your training data is diverse and balanced. If you’re training a system to understand different perspectives on a topic, make sure you’re not inadvertently creating bias by over-representing certain viewpoints or demographics.
Regular auditing of training data is essential too. Information becomes outdated, sources can be discredited, and new research can contradict older findings. Your training data needs to evolve to reflect these changes.
Create a template for your AI to follow
Templates and structured formats can significantly reduce the likelihood of hallucinations by giving AI systems clear guidelines to follow. Instead of allowing completely free-form responses, you can provide frameworks that guide the AI towards more reliable outputs.
For example, if you’re using AI to write reports, you might create a template that requires the system to include specific sections like an introduction, methodology, findings, and conclusions. This structure helps ensure the AI stays on track and reduces the chance of it wandering into fabricated territory.
You can also create templates for different types of responses. A template for answering factual questions might require the AI to specify its confidence level and cite sources, while a template for creative writing might be more flexible but still include guidelines about avoiding harmful content.
Tell your AI what you want and don’t want
Clear instructions and ongoing feedback are essential for keeping AI systems on track. This means being specific about what constitutes acceptable and unacceptable outputs. Don’t just tell the AI what to do; tell it what not to do as well.
For instance, you might instruct a customer service AI to always admit when it doesn’t know something rather than guessing, or tell a research AI to only cite sources it can verify. These explicit instructions help establish boundaries for the system’s behaviour.
Continuous feedback is equally important. When the AI produces good outputs, that positive feedback helps reinforce desirable behaviour. When it makes mistakes, corrective feedback helps it learn to avoid similar errors in the future.
Human oversight remains one of the most effective safeguards against AI hallucinations. Having knowledgeable humans review AI outputs before they’re used or published can catch errors that automated systems might miss. This is particularly important for high-stakes applications like healthcare, legal advice, or financial guidance.
The goal isn’t to eliminate AI hallucinations entirely, which may be impossible, but to reduce them to manageable levels and ensure that when they do occur, they’re caught before they cause harm. By combining technical solutions with human judgment, we can harness the power of AI while minimising the risks associated with its occasional flights of fancy.
1. Why are AI hallucinations a problem?
AI hallucinations are problematic because they deliver false information with complete confidence, making it nearly impossible for users to distinguish between accurate and fabricated content.
This can lead to serious consequences in critical applications like healthcare diagnoses, legal advice, or financial decisions, while also contributing to the spread of misinformation and eroding trust in AI systems.
2. What are the most common types of AI hallucinations?
The most common types include:
False positives: Identifying non-existent threats or issues
False negatives: Missing real problems
Incorrect predictions: Making unfounded forecasts
Fabricated information: Inventing fake citations, non-existent websites, or fictional details that sound plausible but are completely made up
3. Can AI hallucinations be completely eliminated?
No, hallucinations can’t be completely eliminated due to the fundamental way AI models work — they predict likely responses rather than truly “understanding” the world.
However, hallucinations can be significantly reduced through high-quality training data, human oversight, clearer output constraints, and using structured templates to guide responses.
4. How do you detect AI hallucinations?
You can detect hallucinations by:
Fact-checking against trusted sources
Looking for inconsistencies in the response
Verifying links, citations, or named studies
Cross-referencing with multiple references
Being sceptical of oddly specific or “perfect” answers
5. Which AI models have the highest hallucination rates?
Research shows hallucination rates vary widely across different models and tasks. Surprisingly, some newer “reasoning” models hallucinate more than older ones in certain use cases, according to Techcrunch
Comparisons are tricky, though — results depend heavily on the type of task and how hallucinations are measured.
6. Do AI hallucinations happen more with certain topics?
Yes. Hallucinations are more likely when dealing with:
Niche or highly technical subjects
Rapidly evolving topics (e.g. current events)
Areas with limited or conflicting training data
Specifics like dates, statistics, or citations
Medical, legal, and scientific content is especially at risk due to its precision requirements.
7. Are hallucinations the same as bias or errors?
Not quite.
Hallucinations involve fabricated or made-up content
Bias means responses skew based on imbalanced data
Errors are simply incorrect or misunderstood outputs
They can overlap, but each is a distinct issue with its own risks and solutions.
8. How are developers trying to solve the hallucination problem?
Some of the current strategies include:
Retrieval-augmented generation (RAG): Pulling in real-time info from trusted databases
Fact-checking layers: Using second models to validate AI outputs
Fine-tuning with high-quality datasets: Reducing training on unreliable content
User feedback loops: Improving responses based on real-world corrections
