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Breaking complex prompt tasks into subtasks can improve accuracy and reduce hallucinations.

RAG combines pre-trained LLMs with external knowledge retrieval. The LLM's knowledge is augmented by fetching relevant information from a private or dynamic dataset before generating a response, reducing hallucinations and improving factual accuracy.

LLMs can sometimes 'hallucinate,' meaning they generate plausible-sounding but incorrect or nonsensical information. Always verify critical information from LLM outputs.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.

If accuracy on specific facts is crucial, provide those facts within the prompt. This helps to ground the LLM and reduce the chance of hallucinations on those specific points.

To reduce hallucinations, explicitly instruct the LLM to say 'I don't know' or a similar phrase if it cannot answer a question confidently or accurately based on the provided context.

LLM 'hallucinations' occur when models generate false or nonsensical information presented as factual. This happens because models predict plausible-sounding text rather than retrieving verified facts.