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CRAG — Self-Correcting Research Assistant
Overview
A research-assistant project exploring how AI applications can retrieve information and improve the quality of their responses through a self-correction workflow.
Project focus
Python-based AI application development.
Retrieval and research workflow design.
Improving answer reliability through evaluation and correction.
Technologies: Python, AI, retrieval-augmented generation.
Self correction is where RAG gets useful. How do you judge a retrieved chunk as bad enough to trigger a new search?
In this build, a retrieval is judged "bad enough" whenever an LLM reviewer grades each retrieved chunk and marks even a single one as irrelevant to the question. Because it’s an all-or-nothing check, just one bad chunk flags the whole batch as insufficient, which rewrites the...
Most RAG systems stop at the initial retrieval step, which often leads to confident but incorrect answers. Adding a self correction loop that evaluates and validates the output before it reaches the user is a practical way to fix that blind spot.
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