What causes hallucinations in LLMs and how do you mitigate them?

For AI / LLM Engineer ML Engineer Data Scientist

The short answer

Hallucinations are fluent but unsupported or false outputs, arising because LLMs predict likely text rather than retrieve verified facts and have no built-in grounding. Mitigations include retrieval-augmented grounding with citations, constraining the model to answer only from provided context, lower temperature, verification or self-check steps, and faithfulness-focused evaluation.

How to think about it

Hallucinations are fluent but unsupported or false outputs, arising because LLMs predict likely text rather than retrieve verified facts and have no built-in grounding. Mitigations include retrieval-augmented grounding with citations, constraining the model to answer only from provided context, lower temperature, verification or self-check steps, and faithfulness-focused evaluation.

Learn it properly Hallucination & grounding

Keep practising

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