The single sharpest fact in the world of artificial intelligence is that it can't always be trusted. A staggering 94% of top AI models have been found to hallucinate, or produce completely false information, according to the 2026 Stanford HAI AI Index. This is a major concern, especially when it comes to high-impact AI queries, such as reviewing academic research, medical diagnoses, and financial data. It's a concern because these queries require accurate information to make informed decisions.

When you ask an AI model a question, it generates text by predicting statistically probable word sequences based on patterns learned during training. Pragati Awasthi, an assistant teaching professor at Drexel University, explains that this means an AI can produce a response that sounds authoritative, reads fluently, and is completely wrong all at once. The accuracy rate for AI answers generated is an open question, subject to many variables. These variables can affect the accuracy of the answers, and they're not always easy to identify.

At least 45% of all AI answers in a study conducted by the BBC and the European Broadcasting Union had at least one significant issue. The research, based on input from 22 media organizations, also found 31% had sourcing problems — missing, misleading, or incorrect attributions. Another 20% of AI answers contained major accuracy issues, including hallucinated details and outdated information. These issues can be serious, and they're not limited to just a few cases.

So, how can you fact-check AI output? One technique is to push back on AI responses and double-check everything produced against other sources. Re-affirming the timeliness of output and repeating prompts can also help. Being as explicit as possible in prompts can reduce the risk of misinformation. It's also important to remember that AI models aren't perfect, and they can't always provide accurate information.

"Addressing inaccuracies in AI involves applying many of the same techniques we have practiced since middle school," said Jan Liphardt, an associate professor at Stanford University and CEO of OpenMind. "This includes checking sources, verifying claims through direct observation when possible, and consulting multiple people or multiple AI systems to see whether there is general agreement about a fact or assertion." This approach can help identify errors and improve the accuracy of AI output.

Common AI mistakes and errors may include misinformation, outdated information, hallucinations, duplication, omitted information, false citations, and a mixture of true and false information. AI models are dependent on training data, which makes their output vulnerable. The data involved may not have been refreshed, so information beyond a certain point in time won't be available. This can be a problem, especially when dealing with time-sensitive information.

One estimate for error rates on a general-purpose AI on complex professional queries possibly falls into the 20% to 40% range, estimated Dr. Fara Kamangar, founder of DermGPT, the dermatology industry's first AI tool. Even at the low end of this estimate, 20%, it's something to be concerned about, according to Aleshia Hayes, a clinical associate professor at Southern Methodist University, based on her own experience with her queries and prompts. She notes that this error rate can have serious consequences, especially in fields like medicine.

To verify the timeliness of the information you're receiving, you can ask the model for any changes since its last round of training data. This is particularly critical if one is seeking data based on numbers, statistics, or recent news events. You can also ask the model to cite its sources, then actually check those citations. This can help identify any errors or discrepancies in the information.

Lauri Kien Kotcher, CEO at Different Day, advises asking AI to argue the opposite position or identify weaknesses in its own answer. This would be the same as asking a human to consider and discuss all sides of an issue to confirm their conclusion. You can even flip the script on the AI model and run the same query against different models to test the foundation of the information being delivered. This approach can help identify any biases or errors in the AI's responses.

Pose the question two, three, or more times to different models. Any discrepancies between the models' results will signal errors in the insights delivered. Shruti Tiwari, AI product leader at Dell Technologies, notes that ChatGPT, Claude, and Gemini are built differently and trained on different data. This means that they may produce different results, and it's up to the user to verify the accuracy of the information.

The good news is that it's possible to detect when AI is producing erroneous output, but one needs to pay close attention and take the time to carefully review the results. Obvious signs of erroneous output include vague or unnamed references, outdated dates, and differing answers if a question is asked again. Human intuition, the feeling that a response looks off, isn't a recommended form of fact-checking by itself, but it can help as a valuable motivator to do further checking.

AI is a powerful tool that puts the world's knowledge at one's fingertips, through widely available and often no-cost services. However, as with many things, mindfulness and rigor are required to successfully navigate the questions that we face every day. It's not enough to just rely on AI; we need to verify the accuracy of the information it provides.

  • 94% of top AI models hallucinate, according to the 2026 Stanford HAI AI Index
  • 45% of all AI answers have at least one significant issue
  • 31% of AI answers have sourcing problems
  • 20% of AI answers contain major accuracy issues

The consequences of basing key decisions on unvetted AI information may cost users their jobs or revenue losses for a company. AI has a credibility problem, and it's well deserved. Thanks to the ubiquity of powerful and inexpensive tools, AI adoption keeps rising among both consumers and business professionals. This trend won't change anytime soon, so it's essential to address the credibility issue.

While erroneous output for low-impact queries may merely result in minor annoyances, there may be more severe consequences with high-impact queries. The danger isn't that AI gets things wrong, but that it gets things wrong in ways that look right, and people act on them before anyone checks. This can lead to serious problems, and it's up to users to be aware of the risks.

Fortunately, there are some relatively easy ways to verify the authenticity of AI output; it only requires increased vigilance on the part of users. By fact-checking AI output and being mindful of potential errors, we can harness the power of AI while minimizing its risks. It's a matter of being proactive and taking the time to verify the accuracy of the information provided by AI models.