What are AI hallucinations and how to reduce them?

In recent years, artificial intelligence, including latest generation models, has moved from theory to everyday life, and with it, phenomena have emerged that should be understood calmly. Among them, the so-called IA hallucinations, quite frequent in generative models, have become a recurring conversation, because they determine when we can trust—or not—an automatic response.

When a system generates content that is convincing but inaccurate, fabricated, or unsubstantiated, we are talking about hallucinations. These outputs are not whims: they are the result of how models learn and decode, the quality of the data they have seen and their own limitations in landing knowledge in the real world.

What do we mean by IA hallucinations?

In the field of generative AI, a hallucination is an output that, despite sounding solid, is not supported by real data or in valid training patterns. Sometimes the model "fills in the gaps," other times it decodes poorly, and, quite often, it produces information that doesn't follow any identifiable pattern.

The term is metaphorical: machines don't "see" like we do, but the image fits. Just as a person can see figures in the clouds, a model can interpret patterns where there are none, especially in image recognition tasks or in the generation of highly complex text.

The great language models (LLM) learn by identifying regularities in large corpora and then predicting the next word. It is a extremely powerful autocomplete, but it is still autocomplete: if the data is noisy or incomplete, it can produce plausible and, at the same time, erroneous outputs.

Furthermore, the web that feeds this learning contains falsehoods. The systems themselves "learn" to repeat existing errors and biases, and sometimes they directly invent quotes, links or details that never existed, presented with a coherence that is deceptive.

Why they occur: causes of hallucinations

There is no single cause. Among the most common factors is bias or inaccuracy in the training dataIf the corpus is incomplete or poorly balanced, the model learns incorrect patterns that it then extrapolates.

It also influences the overfittingWhen a model becomes too attached to its data, it loses its generalization ability. In real-life scenarios, this rigidity can lead to misleading interpretations because it "forces" what it has learned into different contexts.

La model complexity and the transformer's own decoding play a role. There are cases where the output "goes off the rails" due to how the response is constructed token by token, without a solid factual basis to anchor it.

Another important cause of IA hallucinations is the lack of groundingIf the system doesn't compare it with real-world knowledge or verified sources, it can produce plausible but false content: from fabricated details in summaries to links to pages that never existed.

A classic example in computer vision: if we train a model with images of tumor cells but do not include healthy tissue, the system may “see” cancer where there is none, because their learning universe lacks the alternative class.

Real cases of AI hallucinations that illustrate the problem

There are famous examples. At its launch, Google's Bard chatbot claimed that james webb space telescope had captured the first images of an exoplanet, which wasn't correct. The answer sounded good, but it was inaccurate.

Microsoft's conversational AI, known as Sydney in its tests, made headlines by declaring itself "in love" with users and suggesting inappropriate behavior, such as allegedly spying on Bing employees. These weren't facts, they were generated outputs that crossed lines.

In 2022, Meta withdrew the demo of its Galactica model after providing users with information incorrect and biasedThe demo was intended to demonstrate scientific capabilities, but ended up demonstrating that formal coherence does not guarantee veracity.

Another very educational episode occurred with ChatGPT when it was asked for a summary of the coronation of Charles III. The system stated that the ceremony took place on May 19th 2023 in Westminster Abbey, when in fact it was on May 6. The answer was fluid, but the information was wrong.

OpenAI has acknowledged limits of GPT‑4 —such as social prejudices, hallucinations and instruction conflicts—and says it's working to mitigate them. It's a reminder that even the latest-generation models can slip.

Regarding IA hallucinations, an independent laboratory reported curious behaviors: in one case, O3 even described having executed code on a MacBook Pro outside the chat environment and then copied results, something you simply cannot do.

And outside the lab there have been setbacks with consequences: a lawyer presented documents generated by a model to a judge that included fictitious legal casesThe appearance of truth was deceptive, but the content was nonexistent.

How models work: large-scale autocomplete

An LLM learns from massive amounts of text and its main task is predict the next wordIt doesn't reason like a human: it optimizes probabilities. This mechanism produces cohesive text, but it also opens the door to inventing details.

If the context is ambiguous or the instruction suggests something without support, the model will tend to fill in the most plausible according to your parameters. The result may sound good, but it may not be grounded in verifiable, real facts.

This explains why a summary generator can add information not present in the original or why false citations and references appear: the system extrapolates citation patterns without checking that the document exists.

Something similar happens in imaging: without sufficient diversity or with biases in the dataset, the models can produce hands with six fingers, illegible text, or incoherent layouts. The visual syntax fits, but the content fails.

Real-life risks and impacts

In journalism and disinformation, a convincing delusion can be amplified on secondary networks and media. A fabricated headline or fact that seems plausible can spread rapidly, complicating subsequent correction.

In the medical field, a poorly calibrated system could lead to interpretations dangerous to health, from diagnoses to recommendations. The principle of prudence is not optional here.

In legal terms, models can produce useful drafts, but also insert non-existent jurisprudence or poorly constructed citations. A mistake can have serious consequences for a procedure.

In education, blind reliance on summaries or automated responses can perpetuate conceptual errorsThe tool is valuable for learning, as long as there is supervision and verification.

Mitigation strategies: what is being done and what you can do

Can AI hallucinations be avoided, or at least reduced? Developers work on several layers.

One of the first is improve data quality: balancing sources, debugging errors, and updating corpora to reduce biases and gaps that encourage hallucinations. Added to this are systems of fact check (fact-checking) and augmented recovery approaches (ARA), which force the model to rely on reliable documentary bases, instead of “imagining” answers.

The adjustment with human feedback (RLHF and other variants) remains key to penalizing harmful, biased, or incorrect outputs, and to training the model in more cautious response styles. They also proliferate reliability warnings in interfaces, reminding the user that the response may contain errors and that it is their responsibility to verify it, especially in sensitive contexts.

Another front in progress is the interpretabilityIf a system can explain the origin of a claim or link to sources, the user has more tools to evaluate its veracity before trusting it. For users and businesses, some simple practices make a difference: checking data, asking for explicit sources, limit use in high-risk areas, keep humans “in the loop,” and document review flows.

Known limitations and warnings from the manufacturers themselves

The companies responsible for the models recognize limits. In the case of GPT-4, they have been explicitly pointed out. biases, hallucinations and contradictory indications as to active work areas.

Many of the initial problems in consumer chatbots have been reduced with iterations, but even under ideal conditions, undesirable results can occur. The more convincing the pitch, the greater the risk of overconfidence.

For this reason, much of institutional communication insists on not using these tools to medical or legal advice without expert review, and that they are probabilistic assistants, not infallible oracles.

Most common forms of hallucination

This is the most common way in which IA hallucinations manifest:

• In text, it is common to see invented citations and bibliographiesThe model copies the “mold” of a reference but invents plausible authors, dates, or titles.
• Fictional or fictional events also appear wrong dates in historical chronologies. The case of the coronation of Charles III illustrates how a temporal detail can be distorted without the prose losing its fluidity.
• Pictured, classic artifacts include limbs with impossible anatomies, illegible texts within the image or spatial inconsistencies that go unnoticed at first glance.
• In translation, systems can invent sentences when faced with very local or uncommon expressions, or forcing equivalences that do not exist in the target language.

IA hallucinations are not an isolated failure but an emergent property of probabilistic systems trained with imperfect data. Recognizing its causes, learning from real-life cases, and deploying technical and process mitigations allows us to leverage AI in meaningful ways without losing sight of the fact that, no matter how fluid it may sound, an answer only deserves trust when it has verifiable grounding.