Why Does AI Make Things Up? The Truth About Hallucinations
When your favorite AI gets creative... too creative
"According to an important Stanford University study published in 2024, 78.3% of artificial intelligence users have experienced significant improvements in their work productivity."
Sounds impressive, right? There's just one tiny problem: that study doesn't exist. An AI completely invented it.
This ability to generate information that sounds totally legitimate but is entirely fabricated has a name: hallucinations. And if you use ChatGPT, Claude, or any other AI assistant, I guarantee it's happened to you more times than you realize.
The problem? When a human makes up data, it's usually pretty obvious, but when an AI does it, it can sound so professional and convincing that it's hard to spot the fiction. And that's where the trouble begins...
What are hallucinations?
When we talk about hallucinations in artificial intelligence, we're not referring to AI seeing things that don't exist (although technically...), but to something more specific: it's when an AI model generates information that appears completely real and coherent, but is actually incorrect or simply invented out of thin air.
To understand hallucinations, we first need to understand how these models work. An AI like ChatGPT or Claude has been trained with an immense amount of information (articles, books, websites, conversations, etc). When you ask a question, it constructs a response using the patterns and connections it learned from all that information.
Sometimes, when it doesn't find a direct answer, the AI tries to "fill in the gaps" using patterns it already knows. It's as if it's thinking: "Well, I don't know this exactly, but based on similar things I've seen, it's probably something like this..."
And here's the fascinating part: these generated responses aren't random, nor is it obvious they're false. In fact, they can be incredibly convincing because:
Your assistant states them with complete confidence!
It uses relevant and precise terminology
It follows logic that appears to make perfect sense
It's based (partly) on real information
It includes specific details that make it seem authentic
It's like a student who didn't study for an exam, crafting an answer by mixing everything they vaguely know about the topic with their "creative imagination." It might sound impressive, but that doesn't make it correct.
Types of hallucinations
1. Data and facts
The classic example: your favorite AI confidently cites a study that never existed or mentions completely fabricated statistics. For instance, when it tells you "According to a Harvard University study in 2022..." and it turns out that study was never actually conducted.
2. Errors in logic
This happens when you ask AI to help with a math problem and it delivers a solution that looks correct at first glance. But when you review the calculations, you discover that somehow 2 magically transformed into 5 somewhere in the process.
3. The information remix
My personal favorite: it's like a DJ blending two songs to create something entirely new. The AI takes real information from different sources and combines them into something that sounds convincing but simply doesn't exist. For example, when you ask about a movie and it confidently describes a plot that mixes elements from three completely different films.
How to identify a hallucination?
The bad news is that hallucinations can be incredibly convincing (that's part of the problem). The good news is that there are some clear warning signs to watch for:
When the AI provides unusually specific details you never requested
If it references studies or sources with very recent dates (especially near its training cutoff)
When it gives suspiciously perfect or convenient answers that perfectly match what you hoped to hear
If it provides very precise statistical data (like "78.3%") without citing verifiable sources
Why do models hallucinate?
Now that we understand what hallucinations are, the big question is: why do they happen? Wouldn't it be easier if AI simply said "I don't know" when it's uncertain?
The short answer is: it's complicated.
How does an AI really "think"?
AI models are extraordinarily sophisticated pattern recognition machines. They don't "understand" the world like humans do; what they do (and they do it remarkably well) is identify patterns in the massive datasets they were trained on.
Imagine learning to cook by watching millions of recipe videos, but without being able to smell or taste anything. You might recognize patterns like "after sautéing onions, they always add garlic" or "desserts contain sugar," but you wouldn't truly understand why these patterns work. That's how AI "learns."
Three main reasons why they hallucinate:
1. The pressure to respond
Unlike humans, these models are designed to always provide an answer. It's like that friend we all know who never admits when they don't know something – they always have a response ready, even if they have to invent it on the spot.
2. The problem of incomplete knowledge
AI has a training cutoff date. For example, if you ask an assistant about events from last week, it has to "guess" based on patterns it learned up until its last update. It's like being asked to predict the finale of a series you haven't watched yet – you might make educated guesses based on previous episodes, but you can't truly know what will happen.
3. Creative logic
When answering questions, humans use not only our direct knowledge but also common sense and real-world experience. AI attempt to simulate this by connecting different pieces of information, but sometimes these connections produce... creatively incorrect results.
For example, if the AI knows that:
Dogs are popular pets
Pets need vaccines
A "dog year" equals "seven human years"
Vaccines are given annually
It might tell us something like "Dogs need 7 vaccines every year," combining these separate facts to create a conclusion that sounds perfectly logical but is completely fabricated.
The dilemma of creativity vs precision
Ironically, the very characteristic that makes AI so useful – its ability to make connections and generate creative responses – is also what makes it prone to hallucinations. It's quite similar to the human brain: our capacity for making creative connections enables us to innovate, but it's also what sometimes leads us to see shapes in clouds or faces in electrical outlets.
The critical difference is that we humans know when we're being creative versus factual. AI, on the other hand, present everything with identical confidence – delivering both verified facts and complete fabrications with the same authoritative tone. They have no internal gauge for distinguishing between what they know and what they're inventing.
Why AI will (probably) always hallucinate
And here comes the most fascinating (and perhaps concerning) revelation: according to researchers, this isn't just a problem that will vanish with better models or more training data. Last year, the study Hallucination is Inevitable: An Innate Limitation of Large Language Models was published, suggesting something profound: hallucinations aren't a bug but a fundamental feature hardwired into AI's very DNA.
Yes, you read correctly: "inevitable." That's a bold claim, I know. But before you close this tab thinking I'm being needlessly pessimistic, let me explain why this isn't necessarily bad news, and why understanding this reality will actually help you use AI more effectively.
It's not a bug, it's a feature
A team of researchers set out to determine whether hallucinations are a problem we could eventually solve through technological improvements. Their conclusion? Hallucinations are inevitable in any AI model, regardless of how advanced it becomes.
It's as if they discovered that hallucinations aren't a flaw in the system but an integral part of it. Like in The Matrix, they're woven into the very fabric of AI's structure – we can't simply "fix" them (unless you happen to be Neo 😎).
Why understanding this matters
Before we panic, there are three key points worth considering:
1. Not all hallucinations are bad
Sometimes, what technically qualifies as a "hallucination" can actually be useful. For instance, when AI creates a brilliant metaphor to explain a complex concept, it's technically "hallucinating," but that creative leap helps us understand difficult ideas more clearly.
2. Theory vs reality
In theory, we might believe hallucinations can be completely eliminated, but in practice, we can only reduce them to a manageable level.
"In theory, there is no difference between theory and practice. In practice, there is."
– Yogi Berra
3. Truth is not always singular
Researchers define "hallucination" as any response that deviates from an absolute truth. But in the real world, especially for creative or subjective topics, does a single definitive truth always exist?
What this means for us
AI models will always have weak spots, particularly with complex mathematical problems, questions requiring deep reasoning, very recent information, or situations that demand genuine understanding of human context.
But this doesn't mean AI isn't valuable. In fact, researchers point out that although hallucinations can't be completely eliminated, modern systems already employ effective methods to minimize them - like admitting uncertainty, verifying information against external sources, and incorporating human feedback.
The real challenge
The true challenge isn't eliminating hallucinations entirely (now we know that's impossible), but learning to work productively with this limitation. It's similar to driving a car with blind spots - we can't eliminate them completely, but we can develop habits that help us navigate safely despite them.
And this brings us to the most crucial skill for the AI age: knowing when and how to trust what AI tells you - and when to verify.
Living with a hallucinating AI: a practical guide
Now that we know hallucinations are inevitable, the question isn't how to eliminate them, but how to work effectively despite them. It's like electricity: you don't need to be an electrician to use it safely, but you do need to understand some basics to avoid getting shocked.
Know the danger zones
Hallucinations don't appear randomly; they're more likely in specific situations. For example, when you ask about very recent events, the AI must "improvise" based on older training data. Complex mathematical calculations, precise statistics, and any topic requiring highly specific or cutting-edge specialized knowledge are particularly risky territories.
Trust, but verify
The key is knowing when to trust AI output and when to double-check it. It's similar to working with a brilliantly creative colleague who sometimes gets carried away by their imagination – over time, you learn which tasks they excel at and which require verification.
For creative brainstorming, general explanations, or idea generation, you can be more flexible. But when dealing with statistical data, direct quotes, historical facts, or advice that could have legal, financial, or health implications, always verify the information.
Essential AI usage habits
To work effectively with AI assistants, develop these key habits:
Verify sources: When AI mentions a study, statistic, or specific fact, take a moment to look it up from a reliable source.
Break down complex tasks: Rather than requesting complete answers to complex questions, divide problems into smaller, more easily verifiable components.
Use multiple prompts: Ask the same question in different ways to check if responses remain consistent – inconsistencies often reveal hallucinations.
Request explanations: A simple "Why do you think that?" or "How did you reach that conclusion?" can quickly expose flawed reasoning or fabricated information.
The future of AI interaction
As these systems become increasingly embedded in our daily lives, understanding their limitations becomes ever more crucial. The goal isn't to distrust AI, but to use it in a realistic and productive way – knowing both its strengths and its limitations.
Wrapping up
After two plus years of using AI daily, I've encountered hallucinations of all varieties: from minor date errors to elaborate explanations of research papers that don't actually exist.
Did this make me abandon AI? Not at all. I've simply figured out how to work with it more effectively. Hallucinations are a limitation that will likely always be with us, but with appropriate precautions and a healthy dose of common sense, these tools can be tremendously valuable.
What about you? Has an AI ever surprised you with an especially "creative" response? Share your experience in the comments!
Thanks for reading!
Germán
Hey! I'm Germán, and I write about AI in both English and Spanish. This article was first published in Spanish in my newsletter AprendiendoIA, and I've adapted it for my English-speaking friends at My AI Journey. My mission is simple: helping you understand and leverage AI, regardless of your technical background or preferred language. See you in the next one!