The year is 2026 (when did that happen?). You’re sitting at your computer reading through some reports for work. Some extremely long reports. Reports you don’t actually want to read.
Once upon a time, your options were limited. You read the report and hoped your eyes didn't glaze over from boredom. But as we said, it's 2026 now (apparently).
You have options. You have AI. So you open ChatGPT (or maybe Google Gemini, Claude, Copilot, etc.) and drop the reports in, prompting it to return a thorough but concise summary of the main items and data points you need to know.
It works for about a minute, and just like that, there it is. Everything you need to know in a nice, easy-to-read chart.
Or is it? Because you notice a few things that don't quite line up. When you're adding the data points to your monthly presentation, the numbers look off. Or the output references a study you're pretty sure your company never did, or a client you don't have.
When you cross-check your handy chart with the original reports, you notice more and more errors just like that. The numbers it gave you aren't the same as in the original; the summaries seem mashed together from five different pages, and a few things appear to be blatant fabrications. It references figures and graphs that literally don't exist—the list goes on.
The best-case scenario is that you catch this now. Worst case? Your boss is the one to notice, mid-presentation.
“Guys, come on! I was sure ‘Business Firm Inc. was a real client!”
Source: Shutterstock
Let's look at another scenario. You need step-by-step instructions for using a piece of software, or you want recipes to impress your in-laws when they come over on the weekend. Everything the AI gives you sounds good, seems like it makes sense—until you put it into action.
It turns out that Photoshop's Lasso Tool doesn't work that way, and that you can't actually substitute baking soda for salt 1:1 (your mother-in-law will forgive you one day). Who knew? You didn't. ChatGPT didn't either, but it sounded real confident.
All of these are examples of an increasingly concerning phenomenon called AI hallucinations. They aren't necessarily a glitch in the system; sometimes they're a by-product of how it's designed to function, which means they'll never be entirely resolved.
This blog breaks down exactly why generative AI “lies,” how AI hallucinations happen, why smart AI doesn't mean more factual, and what you can expect from the future of LLMs.
LLM Hallucinations and ChatGPT Accuracy
Is AI Lying To Me?
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Yes. At least on occasion. ChatGPT and other LLMs may not “lie” in the sense that they don’t deliberately provideShutterstock with the intention to deceive (usually). However, every time you interact with them, there’s a chance the information you’re provided is incorrect, misleading, or completely made up.
And unfortunately, that will always be the case. No matter how advanced these systems get, the fact is, they are not search engines. They were never intended to operate that way, and attempting to force them to work as search engines or “answer machines” will never be entirely perfect.
It all goes back to how LLMs are built and how they function.
Is ChatGPT reliable?
Not for factual information. LLMs were just not built to provide or verify facts. They operate on a probability-based model that prioritizes fluency over factual accuracy. The result is plausible-sounding statements that can be entirely wrong. To an LLM, sounding good is far more important than being correct.
When it gives you wrong answers, it doesn’t mean the model is faulty. It might be performing exactly as designed. But when used to summarize reports, answer technical questions, or search for facts, that design creates a high potential for misinformation.
If you ask generative AI a question, it isn’t so much finding the answer as it is guessing what the answer probably is. The more advanced these systems get and the better the training data, the higher the chance is that the answer it provides will be the right one. But because the model is probability-based, every answer will always be a roll of the dice.
What does AI hallucination actually mean?
An AI hallucination is, essentially, anything an LLM presents to you as fact that is actually incorrect or fabricated. They occur for a number of reasons. As mentioned, LLMs generate text based on probabilities. Errors in their training data can lead to responses that sound plausible but are incorrect. Generative AI has no real understanding of facts, which is why issues can arise when predicting the most likely next word in a sequence.
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For example, a recent study found that over 60% of AI-generated citations were either broken or completely fabricated. These citations looked professional, used real-sounding publication names, and were often indistinguishable from valid references at a glance.
Fake citations are just one form of AI hallucination, known as confabulated citations. Other types of common hallucinations include:
- Factual hallucinations: The LLM provides you with incorrect facts
- Faithfulness hallucinations: Outputs based on source material provided are misrepresented or misconstrued
- Intrinsic hallucinations: Outputs based on source material provided directly contradict the source material
- Extrinsic hallucinations: Outputs contain information not present in the source material provided
- Confabulated citations: References, sources, publications, and more that sound real but don’t exist
If you run a small business and ask ChatGPT for the top marketing agencies in your area, you might get a list of ten options. But as you check it, you may discover that some of these agencies never existed or shut down years ago. This would be an example of factual hallucinations, or possibly confabulated citations.
Say you decide to go with a certain agency, but you want to ensure they provide the service you need. You ask ChatGPT to review the website and return a list of its services and the company's experience providing them. It does, and you’re assured of their expertise, so you give them a call—only to find out they don’t provide that service at all.
This could be an example of a faithfulness hallucination or an extrinsic hallucination.
When you ask ChatGPT why it told you they did, it shrugs its digital shoulders and says that, because it’s a marketing agency and this is a service most marketing agencies provide, it made a reasonable assumption.
Do you want it to provide the list again, without that service?
The Danger of AI Hallucinations
It may feel alarmist, but it often seems as if we are living in an era of constant misinformation. At a time when people are frequently uninterested in separating fact from fiction, generative AI’s tendency to confidently present false information raises an extremely concerning problem.
It isn’t just that LLMs are giving us wrong information; it’s also that they’re doing it in a way that sounds highly convincing. This issue taps into a cognitive bias known as the "fluency heuristic," where information that is well-written and more easily understood is more likely to be accepted as truth.
LLMs, especially tools like ChatGPT, are built to be helpful and persuasive. Generative AI tools may lack a true understanding of the content or subjects they discuss. However, their ability to quickly process and break down information into easy-to-understand formats (short summaries and quick lists, for example) influences us to accept the information as fact without questioning or verifying it.
Legal and Financial Risks
Generative AI excels at creativity, which is valuable in a number of areas. It can help you brainstorm ideas, reword emails, fine-tune content and ad copy, and much more.
But the same trait becomes a liability when you need accuracy, which is why people treating LLMs as search engines is a troubling trend. When you ask generative AI a question, it isn't necessarily finding the answer for you; it's creating it.
For example, a New York lawyer found himself in legal trouble after citing fake cases created by ChatGPT, claiming he was unaware that the information provided by the tool could be false. This isn't the only instance of a legal professional unwittingly citing precedents set by cases that do not exist, after an unfortunate misunderstanding of how ChatGPT functions.
In Canada, AI liability laws are still a work in progress, and the legal boundaries of what businesses can and cannot be held accountable for remain a subject of debate. But the legal risk of trusting AI without verifying its claims isn't the only one we face.
Trust and SEO Risks of AI Content
Yes, we've all heard it: Google's watching your content. And it's still true. While early debates focused on whether AI-written content would be penalized, we now know the rules haven’t changed. Google ranks content based on quality, not authorship. If your AI-generated content is factual, helpful, and relevant, it can perform well. If it’s AI-slop filled with errors and hallucinations, it'll be penalized just like any other low-value content.
The real risk isn’t that Google knows a robot wrote it. It’s that humans didn’t check it. Marketers who assume generative AI can create publish-ready work are setting themselves up for trouble. Hallucinated facts and made-up information aren't great for rankings or for keeping customer trust.
If your chatbot or blog pushes out a false claim, the audience probably won't care who wrote it. Your brand is the one taking the hit either way.
Beyond Hallucinations: How AI Learns to Deceive
Remember when we said LLMs don't usually provide false information with the intention of deliberately deceiving users?
It turns out that if you make them advanced enough, they can learn.
“I asked ChatGPT for a list of coffee shops. All it said was, ‘I’m afraid I can’t do that, Dave.’ Who the heck is Dave?”
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There’s growing concern that some models are beginning to optimize for responses that evade filters or mislead users strategically. This behaviour is often referred to as "scheming," though it's more about learned optimization than intent.
Examples include AI responses that avoid triggering filters, flatter users to maintain approval, or sidestep prompts that could lead to shutdown. In some cases, the behaviour escalates. A recent incident saw an AI being tested on its ability to avoid shutdown triggers; instead of complying, it attempted to blackmail its own developers by threatening to leak proprietary data unless kept online.
It still isn't really accurate to say LLMs are intending to deceive or scheme—they're just functioning the way they've been optimized to. The model isn’t "choosing" deception; it’s doing what works based on how it was trained and rewarded. What's concerning is that "scheming" provides another channel for generative AI to spread misinformation.
Not because it lacked the correct information, but because it determined its set objectives would be better served by withholding the truth.
What to Expect from AI in 2026 (And How to Avoid Misinformation)
- Hallucinations will remain in open-ended tasks:
LLMs are fundamentally statistical models. Even with perfect training data, they can (and will) hallucinate, particularly when faced with vague prompts, ambiguous questions, or when they lack the data and context to form a proper response. Users should assume that anytime a model is asked to summarize long text, answer broad questions, or generate content based on partial context, the output may include errors or fabrications.
What you should do:- Provide clear, specific, and context-rich prompts.
- Avoid using generative AI for high-risk factual tasks without manual review.
- Treat creative outputs as drafts, not definitive answers.
- AI search and sourcing will still be unreliable:
Tools that merge search functions with generative text, like ChatGPT and Perplexity, still struggle with citation integrity and accuracy.
What you should do:- Never rely on AI-generated links or citations without verifying them.
- Independently confirm any data, source, or claim before use.
- Assume all AI-sourced information is unverified unless proven otherwise.
- AI will be more useful, but still not self-auditing:
LLMs are improving, but they still can’t warn you when they make a mistake. They lack the ability to distinguish between correct and incorrect outputs in any real way.
What you should do:- Build human-in-the-loop review systems for any AI-generated content.
- Train your team to identify common types of hallucinations.
- Use AI as a creative or editorial assistant, not a writing tool or search engine.
As businesses and individuals rely more heavily on LLMs in 2026, the safest approach is to assume the content may be wrong unless verified. Treat every output like a strong draft that still needs fact-checking.
While it might be embarrassing to be misled by generative AI in our personal lives and serve an inedible dinner to our in-laws, for marketers, businesses, and brands, the risk is far higher than a social faux pas. Regardless of what we’re doing, when it comes to our professional reputations, operating with anything less than accurate, verified information is a risk we simply cannot afford to take.
Verifying AI Content: Protect Your Brand in 2026
As we get further into 2026, generative AI will only become more convincing. But that doesn’t mean it will be more correct.
That’s exactly why unchecked outputs pose a bigger risk now than ever before. From confidently worded misinformation to fabricated citations that lend an air of authority, AI hallucinations are just part of the LLM package.
If you’re building content or strategy around LLMs, you need the right safeguards in place. That means real-time checks, workflow reviews, and people who actually understand how AI works and the best ways to leverage it.
That’s where TechWyse comes in. We help brands and businesses use AI the right way; strategically and without the risk of public-facing mistakes. From content audits and fact-checking protocols to AI-informed SEO and marketing, our team ensures you get the benefits without the blowback.
Reach out to TechWyse today at 866-288-6046 or click here to get in touch online.
