Reflection with LLM: How to Make AI Review Its Own Work

Author(s): Sayanteka Chakraborty

Originally published on Towards AI.

How to Make AI Review Its Own Work

Reflection refers to a structured process where the model evaluates and improves its own output. It typically involves generating an initial response, analyzing that response against defined goals or constraints, and producing a refined version based on detected issues if any. This approach enhances accuracy, consistency, and reliability by enabling self-assessment before finalizing results.

The article discusses the importance of self-reflection in language models, explaining how they can enhance their output by evaluating their generated responses against set objectives. It then illustrates the entire process, from data preparation and schema definition to code generation and execution. The focus lies on demonstrating a structured workflow wherein a language model interprets user queries, produces Python code for data analysis, executes it, and subsequently critiques its generated output, enhancing its performance through iterative refinements.

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Published via Towards AI