Hallucination (AI)

General definition

Hallucination occurs because language models are trained to produce statistically plausible text, not to retrieve verified facts. The model has no internal knowledge store it can check; it generates each token based on patterns learned during training. When the training distribution does not cover a topic well, or when the model is asked to recall specific facts, it may produce confident-sounding but incorrect output.

Hallucinations range from subtle errors, such as a wrong date or a misattributed quote, to wholesale fabrication of sources, people, or events. In regulated industries, even small factual errors in clinical, legal, or financial output can carry serious consequences.

• Grounding with retrieval-augmented generation is the most common mitigation: retrieved passages constrain what the model can plausibly say
• Instructing the model to cite sources and express uncertainty reduces confident hallucination
• Output validation pipelines check model claims against a trusted data source before surfacing them to users
• Fine-tuning on high-quality domain data can reduce hallucination in narrowly scoped tasks
• Prompt injection attacks can deliberately trigger hallucinated or manipulated responses

In the Ethora ecosystem

Ethora’s AI SDK supports RAG-grounded bot configurations where the model answers from your own knowledge base rather than relying on its training alone. This is the primary guardrail against hallucination in production deployments: the model is told explicitly what it knows and should say nothing outside that scope.

For healthcare and finance use cases where a hallucinated answer could mislead a patient or investor, combining retrieval grounding with an output review layer is the recommended approach when building agents with the self-hosted LLM agent stack.