# **Addressing Hallucinations in LLMs** Hallucinations occur when LLMs generate outputs that are plausible but factually incorrect or fabricated. # **Key Points** - Hallucinations undermine trust and reliability in LLM applications. - Techniques to reduce hallucinations include chain-of-thought prompting and retrieval-augmented generation (RAG). - Post-generation evaluation systems can flag inaccuracies for correction. # **Insights** - Reducing hallucinations requires both proactive (prompting) and reactive (evaluation) strategies. - Domain-specific models may reduce hallucinations for specialized tasks but at higher costs. # **Connections** - Related Notes: [[Evaluation and Feedback in LLM Applications]], [[Fine-Tuning in LLM Applications]] - Broader Topics: [[Challenges in AI Systems]], [[Improving Model Reliability]] # **Questions/Reflections** - What role can user feedback play in identifying and correcting hallucinations? - How can hallucination metrics be standardized across industries? # **References** - Research on LLM hallucination rates and mitigation techniques. - Case studies on factual consistency in AI systems.