Self-RAG Framework
What it is
A modular Retrieval-Augmented Generation (RAG) framework that incorporates a self-reflection mechanism using three specialized critics (Retrieval, Support, and Completeness). It evaluates the necessity of retrieval, the factual support of the answer, and the completeness of the response before final output.
The problem it solves
Standard RAG systems often provide answers based on irrelevant or insufficient retrieved context without a mechanism to verify the quality of that retrieval or the completeness of the response.
What you could use it for
- Building QA systems that require high factual accuracy and reasoning transparency.
- Creating document-based AI assistants that can self-critique for hallucinations.
- Developing RAG pipelines that need to evaluate whether a retrieved document actually supports the generated answer.
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