AI can be used to automatically evaluate and improve the outputs of other AI models, ensuring higher quality and safer responses. Practical implementation includes patterns like the evaluator-optimizer pattern, comparative evaluation, and simple quality threshold checks. Cost optimization strategies include using smaller models, sampling, self-evaluation for simple checks, and discrete scoring. Best practices involve defining clear criteria, using examples, and tracking consistency over time.
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