7 strategies to conquer hallucinations #mentalhealth

Hallucinations are a significant limitation of generative AI, leading to errors in fact and logic. Large Language Models (LLMs) like ChatGPT can produce incorrect or fictional outputs confidently, often lacking the most recent information and including biased data. To mitigate hallucinations, techniques like fact-checking, providing explicit instructions, and using retrieval-augmented generation can be implemented. Errors in fact and logic can lead to reputational damage, as seen with Google Bard’s incorrect claims about the James Webb telescope. Strategies to reduce hallucinations include increasing awareness, using more advanced models like GPT-4, providing explicit instructions, example answers, full context, validating outputs, and implementing retrieval-augmented generation. Engineers building software products on top of LLMs can use retrieval-augmented generation to ground answers in facts, reducing hallucinations. Understanding and mitigating hallucinations in AI technologies can help organizations leverage generative AI effectively to enhance productivity and improve client and employee experiences. The series also covers explainability, with the first part discussing how LLMs work and do not work. It is essential to be cautious when using generative AI for high-stakes or regulated industries and always validate outputs against trusted sources.

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