Hallucination

What it is

In the context of Generative Artificial Intelligence, a hallucination occurs when the model produces a response that seems coherent and grammatically correct but is pure fiction. This is not about an opinion or a spelling error; it is a failure of factual foundation. The model "invents" names of laws, historical dates, technical product features, or even bibliographic references that never existed.

For an SME, this is the number one risk in implementing customer-facing AI. Unlike a calculation error in traditional software (where 2+2 might become 5 due to a logic error), in AI, hallucination is a consequence of the probabilistic nature of the models: they attempt to predict the most likely next word, rather than consulting a database of absolute truth.

How it works

Large Language Models (LLMs) are trained to be excellent imitators of human language, not verified encyclopedias. When a model hallucinates, it is following statistical patterns. If you ask something for which the model lacks specific data in its training, or if the context window is confusing, the system prefers to provide a plausible (statistically probable) answer rather than admitting it does not know.

There are two main types of hallucinations:

1. Intrinsic: The response contradicts the instructions provided by the user.
2. Extrinsic: The response introduces false external facts that cannot be verified by the training data.

The model's temperature (creativity parameter) influences the frequency of these occurrences. Higher temperatures increase the probability of hallucinations, as the model is encouraged to be less predictable.

When to use

Despite being a negative term, understanding hallucination is fundamental when designing AI systems. This concept must be kept in mind whenever factual accuracy is critical. The focus is not on "using" hallucination, but rather on implementing strategies to mitigate it.

The best way to combat this phenomenon in a business environment is through RAG (Retrieval-Augmented Generation). Instead of letting the AI respond based on what it "knows" from its public training, the system is forced to consult specific company documents (product PDFs, HR manuals, sales history) before generating the response. This anchors the model to the factual reality of the organization.

Common errors

The most frequent error in Portuguese companies is overconfidence. A manager tests the tool three times, gets correct answers, and assumes the system is infallible. This leads to the automation of processes without human supervision (Human-in-the-loop).

Another error is the context vacuum. Asking the AI to analyze quarterly results without providing the supporting files is a direct invitation to hallucinate. The model will try to project numbers based on generic market trends rather than the company's financial reality.

Finally, confusing creativity with truth. If you ask the AI to write a marketing slogan, "hallucination" (inventing scenarios) is desirable. If you ask it to read a legal contract, the same characteristic becomes an unacceptable operational risk.

Practical example for an SME

Imagine a real estate agency in the Algarve that implements a chatbot to answer questions from foreign clients about tax legislation and licensing. Without proper safeguards, a client asks: "Can I build an annex on this land in a protected area?".

The AI model, wanting to be helpful and attentive, might reply: "Yes, according to Article 45-B of the Urbanization Regime, annexes up to 20m² do not require a license". The problem? Article 45-B does not exist with that content or applies to a different region. The real estate agency has just provided false advice that could result in heavy fines for the client and irreversible reputational damage to the agency.

Solution: Limit the chatbot to respond only based on a curated legal database and configure the system prompt to say: "If you cannot find the answer in the provided documents, simply say that they should consult our legal department."

Frequently Asked Questions

Q: Can I eliminate 100% of hallucinations? A: Not entirely. You can reduce them significantly (to values close to zero) using techniques like RAG and Guardrails, but the probabilistic nature of AI always maintains a minimal residual risk.

Q: Is hallucinating the same as lying? A: No. Lying implies an intent to deceive. AI has no consciousness or intention; it simply generates the sequence of words that statistically seems correct following your question.

Q: Does using more expensive models (like GPT-4) solve the problem? A: More robust models tend to hallucinate less because they have better reasoning capabilities, but they still make mistakes. The intelligence of the model does not replace the need for good data sources.

Q: How can I detect a hallucination automatically? A: Through observability tools and evaluation frameworks (evals) that compare the generated response with the original source to verify data fidelity.

• 01AI invents a 50% discount coupon code that never existed in the store's system.
• 02A legal assistant cites a Supreme Court ruling that has a fake case number.
• 03Technical support chatbot invents a feature in a software that the company has not yet developed.
• 04AI generates a biography of a company partner attributing awards to them that they never received.

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