Language models are trained to predict statistical patterns in text. They are not trained to "know" whether something is true. When a model answers a question about something outside its training data, or when its training data itself contains errors, it can generate a plausible-sounding but incorrect answer.

The model has no sense of uncertainty in the traditional sense. It generates a correct answer with the same linguistic confidence as an incorrect one. That makes hallucinations hard to detect: the error looks just as reliable as a correct answer.

Hallucinations occur more often in specific situations:

• Questions about facts outside the training data: names, dates, statistics the model does not know exactly but can "fill in"
• Specialised or technical knowledge: domains where the model has less training data
• Questions with an assumed error: if you ask "Why is X true?" when X is not true, the model sometimes goes along with the assumption
• Long outputs: as the output gets longer, the chance of errors increases

The most effective measure against hallucinations is using RAG. Instead of relying on what the model learned during training, you retrieve relevant information from your knowledge base and provide it as context to the model.

Give the model explicit instructions: "Answer questions only based on the provided context. If the context does not contain the answer, say you do not know." This instruction significantly reduces the chance of the model making things up.

Give the model permission to say it does not know something. Models naturally tend to answer, even when they are not certain. Instructions like "If you are not sure of an answer, say so explicitly and refer to a source or colleague" steer this behaviour in the right direction.

Have the model not only give an answer, but also cite the source it is answering from. If an answer is traceable to a specific document, the user can verify it. Untraceable answers are harder to trust.

Not all models hallucinate equally often. Models specifically trained to cite sources, or those connected to search systems, hallucinate less than pure generative models. For applications where factual accuracy is critical, model choice is a factor.

For applications where errors have serious consequences, medical information, legal advice, financial calculations, you build human verification into the process. The AI generates a draft; a human verifies before it goes out.

This is not always possible, but it is the most thorough measure for situations where the cost of an error is high.

Completely eliminating hallucinations is not possible with the current state of technology. They can be limited, but not removed. Being honest about that limitation is important when communicating about AI to stakeholders. An AI that rarely hallucinates is valuable; an AI you guarantee will never hallucinate is unrealistic.

Hallucinations are a real risk with every language model. With the right architectural choices, RAG, good instructions and human verification where needed, they are well manageable. Mach8 builds AI systems where the risks of hallucinations are structurally limited.

Want to build an AI system that is factually reliable? Get in touch with Mach8.