From Retrieval to Reasoning: Building Self-Correcting AI with Multi-Agent ReRAG

RAG systems combine the power of large language models with external knowledge retrieval, allowing AI to ground responses in relevant documents and data. However, current implementations typically follow a simple pattern: retrieve once, generate once, and deliver the result. This approach works well for straightforward questions but struggles with nuanced reasoning tasks that require deeper analysis, cross-referencing multiple sources, or identifying potential inconsistencies.

Enter Multi-Agent Reflective RAG (ReRAG), a design that enhances traditional RAG with reflection capabilities and specialized agents working in concert. By incorporating self-evaluation, peer review, and iterative refinement, ReRAG systems can catch errors, improve reasoning quality, and provide more reliable outputs for complex queries.