Prioritizing the use of high-quality data sets and naturally existing gold standards for AI development, focusing on attaining the best possible answers rather than solely creating data sets. Valuing naturally existing human expertise as a benchmark for evaluating AI work and leveraging user feedback. Emphasizing a factored verification approach to assess components such as text support, accuracy, hallucination, and relevance to the original question for better results. Structuring interactions to encourage more detailed evaluations by both developers and users.

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