Explores the reflection design pattern in agentic AI systems by building a text-to-SQL agent from scratch using foundation model APIs. Demonstrates how adding feedback loops where LLMs review and refine their outputs can improve accuracy from 70% to 85%. Covers implementation details including direct generation, self-reflection, and reflection with external feedback from database execution results.
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