Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation

Retrieval-Augmented Generation (RAG) systems enhance large language models by grounding responses in external knowledge bases, but conventional RAG architectures operate with static corpora that cannot evolve from user interactions. Bidirectional RAG introduces a novel architecture that enables safe corpus expansion through validated write-back of high-quality generated responses, employing a multi-stage acceptance layer to prevent hallucination while enabling knowledge accumulation.