Evaluator-Optimizer LLM Workflow: A Pattern for Self-Improving AI Systems | Sebastian Gutierrez
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Evaluator-Optimizer Workflow: In this workflow, one LLM call generates a response while another provides evaluation and feedback in a loop.
The two signs of good fit are: LLM responses can be demonstrably improved when feedback is provided The LLM can provide meaningful feedback itself
The Evaluator-Optimizer pattern caught my attention because it represents something I’ve been thinking about: how do we move from static prompt engineering to truly dynamic AI systems?
The key insight here is elegant in its simplicity: use one LLM to generate solutions and another to evaluate them in a continuous feedback loop until a success criteria is passed.
