Technique: Recursive Refinement
Instead of asking the AI to produce perfect output in one shot, recursive refinement breaks the task into critique-then-improve cycles. First, the model identifies weaknesses in the content, then rewrites to address each one. This mimics how professional editors work — draft, review, revise.
This technique consistently outperforms single-pass generation because it separates the “creative” and “critical” thinking modes. The first pass focuses on getting ideas down; the refinement pass focuses on quality, accuracy, and polish. In agent architectures, this becomes the “Evaluator-Optimizer” pattern where one LLM generates and another critiques in a loop.
When to use: Polishing drafts, improving code quality, refining business proposals, enhancing creative writing, and any scenario where iterative improvement adds value. Especially effective when combined with specific quality criteria.