The evaluator-optimizer pattern involves one AI agent generating output while another evaluates it and provides feedback in a loop, aiming to refine the response. Anthropic refers to this as the evaluator-optimizer, while Google frames it as a generator-critic loop for correctness and an iterative refinement loop for quality. Both approaches require a clear exit condition to prevent excessive costs and system hangs, with a maximum iteration count being a crucial safety measure. AI
IMPACT This pattern can improve the quality and correctness of AI-generated outputs by introducing iterative refinement and critique.
RANK_REASON Describes a pattern/workflow for using LLMs, not a new model release or core research.
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