Self-refine is the iterative prompting method where a model repeatedly generates output, evaluates it, and refines it toward better quality - it formalizes draft-to-revision behavior within inference time.

What Is Self-refine?

• Definition: Closed-loop generation pattern of initial draft, self-feedback, and improved rewrite.
• Iteration Structure: Can run fixed rounds or terminate when quality criteria are satisfied.
• Feedback Source: Self-generated critique, rubric scoring, or external validator signals.
• Task Applicability: Useful for writing, code generation, and constrained-format responses.

Why Self-refine Matters

• Output Quality: Multiple passes usually produce clearer and more accurate final responses.
• Error Recovery: Early draft mistakes can be corrected before final delivery.
• Prompt Control: Refine loop can enforce style, completeness, and policy constraints.
• Operational Flexibility: Works without model retraining, using only inference-time logic.
• Cost Balance: Additional passes add compute cost but can reduce human rework.

How It Is Used in Practice

• Rubric Design: Define explicit criteria for what counts as improved output.
• Iteration Limits: Set max rounds and quality thresholds to control latency.
• Verification Step: Add final consistency check before returning refined response.

Self-refine is a practical iterative-improvement framework for LLM applications - structured revision loops can significantly enhance final-output reliability with manageable inference-time overhead.