How to Build an Agentic Decision-Tree RAG System with Intelligent Query Routing, Self-Checking, and Iterative Refinement?

The article introduces an advanced Agentic Retrieval-Augmented Generation (RAG) system that enhances traditional question-answering capabilities by incorporating intelligent query routing, self-assessment, and iterative response refinement. This system leverages open-source tools such as FAISS for efficient similarity search, SentenceTransformers for semantic embedding, and Flan-T5 for text generation, creating a decision-tree-style pipeline that mimics human-like reasoning processes. This development signifies a notable step forward in AI system design, enabling more accurate and context-aware responses through dynamic knowledge source selection and self-evaluation mechanisms. By integrating these components, the