A generator produces a result, an evaluator scores it against specific criteria, and the generator optimizes it based on the feedback. Typical conditions for use: Quality criteria can be explicitly formulated; Iterative improvement is measurable. The central trade-off: Better output quality is gained against additional evaluation complexity and latency.
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An evaluator-optimizer has a generator create a draft and a separate evaluator improve it repeatedly through feedback.
The pattern comes from iterative optimization and review processes and has been described for language models as an evaluator-optimizer workflow. It revives the older idea of coupling production and quality checking through feedback instead of accepting a one-shot draft unchecked.
The optimizer produces an output; the evaluator checks it against criteria and returns actionable feedback. The loop ends at sufficient quality, an abort condition, or an exhausted budget.
An evaluator-optimizer has a generator create a draft and a separate evaluator improve it repeatedly through feedback. Conceptual starting point.
The optimizer produces an output; the evaluator checks it against criteria and returns actionable feedback. The loop ends at sufficient quality, an abort condition, or an exhausted budget. Practical guiding question.
The pattern improves consistency and verifiability when criteria are observable. It wastes effort when evaluation is vague or the evaluator repeats the generator’s errors.
The pattern improves consistency and verifiability when criteria are observable. It wastes effort when evaluation is vague or the evaluator repeats the generator’s errors.
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