What Is AI Hallucination? 8 Steps To Avoid AI Hallucinations

AI hallucination is when a generative AI model produces information that is false, fabricated, or unsupported by its sources, and presents it as confidently as it presents accurate answers. It happens because these models predict the most plausible next words rather than retrieve verified facts.

Generative AI has become more reliable than it was a few years ago, and the best models now hold hallucination rates on grounded summarization tasks to low single digits. But the problem has not been solved, and researchers argue it cannot be eliminated. If you leave AI models with too much freedom, they can still return inaccurate or contradicting information. This guide explains why that happens and how to keep your AI output trustworthy.

What is AI hallucination?

AI hallucination is a phenomenon that makes LLMs (Large Language Models) generate inaccurate information and responses. These inaccuracies can range from mild deviations from facts to completely false or made-up information, including invented statistics, misattributed quotes, and citations to sources that do not exist. The cause is structural rather than occasional: a language model predicts the most plausible next word in a response, so it optimizes for text that reads correctly rather than text that is verifiably correct. Because the output stays fluent and confident either way, a hallucinated answer usually looks exactly like an accurate one, which is why hallucinations get published.

How often it happens depends heavily on the task. On grounded summarization, where the model is handed a document and asked to summarize only what that document contains, the top model on Vectara’s hallucination leaderboard sits at 1.8%, with most listed models falling between roughly 3% and 15% and a tail running past 20% (checked 2026-08-12; the leaderboard is a living document, revised as models are added and updated). Open-ended generation is harder, because there is no source document to stay faithful to, and Vectara notes its benchmark deliberately does not try to measure every way a model can hallucinate. ChatGPT, the best-known generative AI system, still carries a disclaimer warning users about “inaccurate information about people, places, or facts.”

AI models like ChatGPT are trained to predict the most plausible next word of a response based on the user's query (also known as a prompt). As the model isn't capable of independent reasoning, these predictions aren't always accurate.

That's why, by the end of the generation process, you may end up with a response that steers away from facts.

These deviations aren't always obvious, mainly because a language model can produce highly fluent and coherent text that makes it seem like you shouldn't doubt the response.

This is why fact-checking content from AI copywriting tools is crucial to ensuring your content doesn't contain false information.

What causes AI hallucination?

AI hallucination is caused by gaps between what a model learned and what it is being asked to do. Improper, low-quality training data is the most common source: the output of a generative AI model directly reflects the datasets it was trained on, so if there are any gaps that leave room for the so-called “edge cases,” the model might not give an accurate response. Overfitting to a narrow dataset and language that has moved on since training push a model the same way, toward filling gaps with plausible invention. Prompting matters too, because a vague request gives the model more room to guess.

Training incentives play a part as well. The 2025 paper Why Language Models Hallucinate argues that standard training and benchmark scoring reward a confident guess over an admission of uncertainty, so models learn to answer rather than abstain. That helps explain why hallucinations persist even in state-of-the-art systems, and why prompts that explicitly allow the model to say it does not know tend to help.

A good example of such an issue is overfitting, which happens when an AI model is too accustomed to a dataset used for its training.

When this happens, the model is inapplicable to other datasets, so forcing it to create a response based on newly introduced data can lead to false information.

If this concept sounds too complex, here's a simplified example that clarifies it:

Let's say you asked an AI tool to draft a commercial real estate purchase agreement. If the tool was trained on residential real estate data and overfitted, it may not have had enough exposure to commercial agreements to understand the differences between them.

It would still generate a draft because you prompted it, but it may leave out important sections specific to commercial agreements or even make them up.

Language-related challenges can also contribute to hallucinations. AI must stay up-to-date on the constant evolution of language to avoid misinterpretations caused by new terminology, slang expressions, and idioms.

For best results, it's always best to use clear, plain language when prompting AI content generators.

Why is AI hallucination a problem?

AI hallucination isn’t merely an error in computer code: it has real-life implications that can expose your brand to significant dangers. The main consequence you might suffer is the deterioration of consumer trust as a result of putting out false information. Your reputation might take a hit, which may require a lot of time to fix. Hallucinations cost you time in the other direction too: if your AI tool keeps responding with inaccurate information, you can’t confidently publish a piece before fact-checking everything, and in some cases that takes longer than it would to do your own research manually.

The dangers of AI hallucinations are particularly visible in YMYL (Your Money, Your Life) topics.

Google looks for the highest possible degree of E-E-A-T (Experience, Expertise, Authoritativeness, and Trust) in order to rank such pieces high in search results, so any inaccuracies can damage your SEO standing.

Worse yet, hallucinations may lead to your AI tool generating content that negatively impacts the reader's well-being.

All of this doesn't mean you should steer away from AI when creating content—all you need to do is mitigate hallucinations to ensure your AI tool provides accurate, reliable information.

8 ways to prevent AI hallucinations

While you may not have complete control over your AI tool’s output, there are many ways to minimize the risk of it making up information. Each step below works by removing ambiguity: give the model the context and specific data it needs, narrow the question so it has fewer ways to guess, name the sources it should use, assign it an expert role, and state explicitly what you don’t want in the response. Lower the temperature setting to make the output less random, apply extra scrutiny to YMYL topics, and fact-check the finished draft before it goes live.

Here are some of the most effective steps to prevent AI hallucinations:

1. Provide relevant information

AI models require proper context to yield accurate results. Without it, the output is quite unpredictable and most likely won't meet your specific expectations. You need to explain to AI what you're looking for and give it a bigger picture of your content.

It's also a good idea to direct your prompt with specific data and sources.

This way, your AI model will know exactly where to pull its information from, which reduces the risk of hallucinations.

So what does this look like in practice?

It all comes down to avoiding vague prompts and giving AI as many specifics as possible.

For example, instead of saying,

"Write an introduction to an article about the digital marketing industry," your prompt can be something like:

"Write a 150–200-word introduction to an article about the state of the digital marketing industry. The article will be published on an SEO blog, and the tone should be friendly and authoritative. Use official .gov sources to provide relevant statistics about the industry's current state and predictions."

Surfer AI, for example allows you to add specific information using its custom knowledge feature. Let's say you are writing an article on fighting climate change.

You can instruct Surfer to include information that details the impact of climate change in the polar region.

2. Limit possible mistakes

Besides giving AI a clear direction, you should set some boundaries within which you want the response to be.

Ambiguous questions may be misunderstood and increase the chance of hallucinations.

One way to help your AI tool provide a correct answer is to ask limited-choice questions instead of open-ended ones.

Here are some examples that clarify this difference:

Open-ended: How has unemployment changed in recent times?

Limited-choice: What were the unemployment rates in 2021 and 2022 according to government data?

Open-ended: How much content should my website have?

Limited-choice: How many blog posts does an average business publish per month?

The point is to ensure AI looks for specific data instead of having the liberty to come up with the answer on its own.

It's also important to instruct AI to admit when it can't find reputable sources to back up its claims.

3. Include data sources

If you don't want your AI model to steer away from facts, you can tell it where to look for information.

Some of the example prompts you saw above do this by instructing AI to look for reputable research, but you can take it a step further and give your platform the specific websites you want it to use.

For instance, in the above unemployment rate example, you can tell AI to only use data from the U.S. Bureau of Labor Statistics.

4. Assign a role

Role designation is a useful prompting technique that gives AI more context behind the prompt and influences the style of the response. It also improves factual accuracy because the model essentially puts itself in the shoes of an expert.

5. Tell AI what you don't want

Seeing as AI hallucinations occur largely due to unrestricted creativity paired with faulty training data, an effective way to reduce them is to preemptively guide the response through so-called "negative prompting."

6. Fact check YMYL topics

As mentioned, AI hallucination can do significant damage when you're covering YMYL topics, which mainly boil down to financial and medical advice.

7. Adjust the temperature

Temperature setting is a useful feature of AI tools most users don't know about. It lets you directly impact the randomness of the model's response, helping you reduce the risk of hallucinations.

8. Fact-check AI content

Regardless of how useful AI is, you shouldn't copy and paste the content it produces. Make sure to verify everything before publishing to avoid copyright issues with AI content.

Examples of AI hallucinations

AI hallucinations range from mildly entertaining to full-on dangerous. There have been several notable cases of AI chatbots spreading false information about historical events, public figures, and well-known facts. The best-documented examples follow a single pattern: the model states something specific and checkable, states it fluently, and gets it wrong in a way nobody notices until someone verifies it.