Reflection prompting is the prompting technique that asks the model to review and critique its own output before producing a revised answer - it introduces a self-correction loop that often improves final quality.

What Is Reflection prompting?

• Definition: Two-stage or multi-stage prompt pattern of generate, critique, and refine.
• Review Focus: Can target factual errors, logic gaps, formatting defects, or policy compliance issues.
• Execution Modes: Single-model self-review or separate critic and generator roles.
• Task Fit: Especially useful for coding, analytical writing, and high-precision structured outputs.

Why Reflection prompting Matters

• Quality Improvement: Self-audit frequently catches issues missed in first-pass generation.
• Reliability Gain: Iterative refinement reduces obvious errors and inconsistencies.
• Process Transparency: Reflection output provides rationale for revisions.
• Alignment Support: Critique stage can enforce style, safety, and domain constraints.
• Cost Tradeoff: Extra passes increase latency but can reduce downstream correction effort.

How It Is Used in Practice

• Critique Template: Define explicit review criteria and severity tags for detected issues.
• Revision Rules: Require second pass to address each critique point directly.
• Stop Conditions: Limit iteration count and use quality thresholds to control runtime.

Reflection prompting is a practical self-improvement loop for LLM outputs - structured review and revision cycles improve correctness and robustness in production prompt workflows.