Why Does AI Hallucinate? A Peek Under the Hood of Language Models

Artificial intelligence today handles tasks that not long ago seemed like pure sci-fi. It writes texts, designs programming code, and helps us with a multitude of complex tasks. The more smoothly and convincingly its answers sound, the easier we forget one big risk: AI can create a completely fabricated response that, at first glance, appears 100% credible.

This phenomenon has been dubbed 'AI hallucination.' It's not a random software bug or a deliberately overlooked function that developers just haven't turned off. It's a natural and deeply ingrained consequence of how today's large language models (LLM) are designed and trained. Let's take a look under the hood at how these systems work to understand why they so confidently 'talk nonsense.'

What Are AI Hallucinations?

In generative artificial intelligence, a hallucination refers to a situation where the model creates a response that, while grammatically and stylistically perfect and credible, has no real-world backing.

Models can hallucinate in truly surprising ways. They might invent non-existent law paragraphs, make mistakes in mathematical calculations, or generate realistically sounding scientific studies, including specific author names and web addresses that never existed. It even happens with completely trivial inquiries, like when you want to verify someone's birth date or look up official historical figures from an archive.

What Happens Inside a Language Model

To understand where these mistakes come from, we need to clarify one thing: large chatbots like ChatGPT or Gemini do not function like internet search engines, nor are they huge fact databases. They have no real knowledge of our world and no idea what is true and false.

A language model is essentially a large neural network, which is essentially a complex set of 'trained instincts.' These instincts were obtained by analyzing billions of words from the internet, books, and discussions. The goal of this financially and technologically extraordinary training is not to teach the machine to understand the meaning of the text, but simply to learn to convincingly imitate human speech.

How AI Works with Tokens

When you type a question into a chatbot, the neural network does not work directly with your words. The text is first broken down into so-called tokens. Tokens are pre-prepared words or their parts that correspond to specific numbers. The mathematical network inside a computer can crunch only numbers.

The model takes these numbers (your query along with the entire previous context of the conversation) and runs them through its layers. But the result isn't that it looks into some drawer for the 'correct answer.' The output is a purely mathematical prediction: the model calculates which next token is most likely to follow the text that has already been written.

Why the Model Predicts Instead of Verifying

The entire principle of how artificial intelligence works relies on estimating probabilities, not verifying truth. The model assembles text word by word (token by token). Each newly generated word is immediately incorporated into the input, and based on this fresh context, the probability for the next word is calculated.

To prevent the outputs from becoming too dull and dry, a hidden parameter called 'temperature' is used during generation. When the temperature is zero, the model will always choose the most predictable token, which leads to bland and repetitive answers. A higher temperature allows the model to also pick less likely words. This unlocks creativity and the ability to write interesting stories, but at the same time drastically increases the chance that the model will resort to absolute fiction.

How One Answer Is Formed Step by Step

The entire process from query input to the final hallucination looks like this in practice:

1. Analysis of the input: Your question is converted into numbers (tokens).
2. Probability calculation: The model weighs the context and chooses words from hundreds of possible candidates that statistically fit best.
3. Creating the narrative: If the model encounters a topic where it lacks solid data, it doesn't admit ignorance. It uses fluent language to construct text that sounds completely logical but simply fills in the details according to statistics.
4. Looping context: Once the model generates an incorrect detail halfway through a sentence (like a wrong year or fictional name), this detail becomes a solid part of the context. In subsequent steps, the model builds on its own lie and adds more plausible-looking justifications to it.

Some Questions Lead to Hallucinations More Often

During training (known as pre-training), the model sees only correct, fluent texts. But there are different types of information in the data. Spelling rules or properly closing brackets appear repeatedly in texts, so the AI learns them flawlessly.

The problem arises with specific facts that appear rarely in the data and do not follow any repeatable pattern. Typical examples are exact numbers, years, birth dates, or little-known historical events. Because these facts appear virtually randomly in the data, the model cannot reliably infer them. Therefore, if you ask artificial intelligence for a specific numerical figure or details that a person must precisely remember from an archive, the risk of hallucination skyrockets.

How Are AI Models Trying to Limit Hallucinations?

Currently, developers are using several main strategies to tame the models' wild imagination:

• External tools and search. Just as models have been given calculators for mathematical calculations, they can now search the web before responding. They read relevant pages and then cite them. This significantly reduces the chance of fabricating a response, although even with citations hallucinations can still occur.
• Reasoning models. Advanced systems (e.g., OpenAI's o3 or o5 series) conduct an internal monologue before generating a response. They first think through the process, check their sources, and only then release text to the user. There are significantly fewer hallucinations, but when they occur, they are more refined and harder to detect.
• RAG (Retrieval-Augmented Generation) architecture. Primarily used in companies. The model disconnects from free guessing and is forced to work exclusively with a provided database of internal documents. The probability of error is thus minimized.

The main problem lies in how models are evaluated in tests. Most metrics measure pure success (accuracy) on a scale of 1 (correct) or 0 (incorrect). Cautious admission of 'I don't know' always ends up with a zero, whereas risky guessing can occasionally earn the model a point. The system literally motivates artificial intelligence to confidently guess rather than admit uncertainty. OpenAI is therefore advocating for an adjustment in evaluation methodology that would penalize models for fabricating information and instead reward them with points for openly acknowledging uncertainty.

How to Get More Reliable Answers from AI

Until evaluation systems change, we as users must approach chatbot outputs critically. Here are some tips to mitigate risks:

• Provide data directly in the prompt. If you want AI to analyze a text or law, insert it directly into the query and explicitly forbid adding anything that isn't there. This minimizes the chance for fabrications.
• Change the tone of the question. Don't corner the model with questions about things it can't logically deduce (exact statistical figures, biographical details).
• Verify everything important. Especially in sensitive areas like law, medicine, or finance, consider AI outputs as rough drafts that you need to verify in independent, reliable sources.

Frequently Asked Questions

Why does AI sometimes promise to send me an email or complete a task later, even though it can't do so?

The model has no awareness of its real technical capabilities or the current time. It only complements text based on statistical probability. When you assign it a task, it evaluates the most natural continuation of the sentence as a polite confirmation that it's already working on it, even if no background process is actually running.

Can a chatbot completely invent a book or scientific article that looks genuine?

Yes, this happens very often. AI has learned the structure of scholarly citations from texts, so it can generate titles that sound very real and scholarly. Information in such citations may be entirely fabricated, and the only way to verify this is to attempt to independently locate the referenced work.

Will it help against hallucinations if I ask the same thing again in a new chat?

Yes, starting a new chat is a very effective help. This wipes the entire previous conversation context. If the AI made a mistake in previous sentences, in the original chat it would build on that mistake due to its nature, while in a clean window, it starts calculating probabilities from scratch.

If I enable internet search, can I be sure the response will be error-free?

Unfortunately, there is no guarantee. The search engine does supply the model with correct data, but AI still assembles text by estimating the next word. Tests show that even with web access, chatbots can misunderstand information, confuse connections, and create an error that they further intertwine with real references.

Is it even worth using artificial intelligence knowing it makes such mistakes?

Definitely, it's just necessary to adjust expectations. For brainstorming, writing emails, coming up with creative concepts, or finding unconventional connections, this model's freedom is a huge advantage. However, you must act as an editor, let AI write, and always verify factual details yourself.