AI Hallucination

A hallucination is a response from an artificial intelligence model that sounds coherent and convincing but is factually false, invented or unverifiable. The model is not lying on purpose: it simply predicts the most likely next word based on the patterns in its training, with no built-in mechanism to tell what is true from what merely sounds true. When it lacks data, it fills the gap with something that fits statistically, and delivers it with the same confident tone as a correct answer.

The phenomenon is inherent to LLMs (large language models) and to generative AI in general. That is why controlling hallucinations is a core requirement for any AI agent that works with a company's real data, and part of what makes a system of AI agents trustworthy. An agent that invents an account balance, a return policy or an order status is not just useless; it is risky for the business.

The good news is that hallucination is not inevitable. With techniques such as anchoring the model to real data sources (grounding and RAG, retrieval augmented generation) and putting control barriers in place (guardrails), you can drastically reduce how often it happens.

Why a model hallucinates

A language model does not have a database of facts that it checks before answering. It works on probability: given a sequence of words, it calculates the most plausible continuation based on what it saw during training. The same mechanics that make it fluent and natural are what produce hallucinations, because plausible is not always true. If you ask it about a regulation that does not exist, the model may draft one that "sounds" like a real regulation, article number included.

Several factors also raise the risk. A high LLM temperature makes the model pick less likely, more creative options, which increases the chance of invention. An ambiguous question or a poorly worded prompt pushes it to "complete" the answer with assumptions. And when the topic is outside its knowledge or very recent (after its training cutoff), the model does not say "I don't know": it fills the gap with something plausible.

Types of hallucination

Not all hallucinations are alike. It helps to tell them apart so you know how to mitigate each one:

• Source contradiction: the model receives a document and still answers something that contradicts it.
• Fact invention: it creates data that does not exist (a quote, a statistic, a name, a date).
• Reference fabrication: it invents sources, links or case files that look real but are not.
• Inconsistent reasoning: it reaches a conclusion that does not follow from the steps it laid out itself.

A concrete example from LATAM

Picture an Argentine bank that launches an AI assistant on WhatsApp to answer customer questions. A user asks: "What is the limit for transfers in the app?" If the agent is not anchored to that customer's real data and to the bank's policy in force, it may reply with an invented amount that sounds reasonable (for example, a generic cap it saw in thousands of similar texts). The customer takes it as true, tries to make the transfer and fails, or worse, makes a financial decision based on false data. The same question, with the agent connected to the core banking system through RAG (retrieval augmented generation), returns the exact limit for that account. The difference between a useful assistant and a dangerous one almost always comes down to whether it answers from the company's data or from its statistical imagination.

How to mitigate it

The goal is not to eliminate hallucination 100% (no technique can achieve that), but to reduce it to a tolerable and detectable level. The main levers are:

1. Grounding and RAG: before answering, the system retrieves the relevant documents or records and forces the model to base its answer on them. If the data is not in the source, the model says so instead of making it up.
2. Guardrails: rules and filters that block out-of-scope answers, validate formats and stop the agent when it has no basis for a reply.
3. Human-in-the-loop: for sensitive decisions, a person reviews or approves before anything is executed.
4. Citing the source: asking the model to show where it got each statement makes verification easier and discourages invention.
5. A clear, narrow prompt: precise instructions and an explicit "answer only with the information provided" reduce the room for filler.

How it differs from an ordinary software bug

A traditional bug is deterministic and reproducible: with the same input, it always returns the same wrong result. A hallucination is probabilistic: the same question can get a correct answer once and an invented one the next time. This table sums up the contrast:

Why it matters for the business

In a B2B context, a hallucination is not a technical curiosity: it is an operational and reputational risk. An agent that invents prices, contract terms or available stock can create a commercial commitment that is impossible to keep, a regulatory penalty or the loss of a customer's trust. That is why, in any serious AI deployment on company data, the question is not "how smart does it look?" but "how do we make sure it answers from the organization's truth and not from its own invention?".