Reflection (AI)

Reflection is an AI design pattern where a model reviews and critiques its own outputs, then uses that critique to produce improved results. Rather than accepting the first response a model generates, reflection adds a self-evaluation loop that catches errors, identifies weaknesses, and generates a refined output. The typical reflection pattern works in three steps: generate an initial response, critique the response (identifying factual errors, logical gaps, missing information, or quality issues), and generate an improved response that addresses the identified issues. This process can repeat multiple times for further refinement. Reflection can be implemented using a single model (the same model generates and critiques) or multiple models (one generates, another critiques). It can focus on specific quality dimensions — factual accuracy, completeness, tone, format adherence — depending on the application's requirements.

Reflection significantly improves the quality and reliability of AI outputs, particularly for complex tasks where first-draft quality is insufficient. For business applications where accuracy and polish matter — report generation, customer communications, technical documentation, data analysis — reflection can bridge the gap between draft and production quality. The trade-off is latency and cost: reflection requires additional model calls, increasing both response time and token usage. For applications where quality is more important than speed, this trade-off is easily justified. For real-time interactions, simpler reflection approaches (single critique pass) can still improve quality with modest latency impact. Reflection also improves transparency. The critique step produces an audit trail showing what issues were identified and how they were addressed. This is valuable for compliance, quality assurance, and building trust in AI-generated outputs.