Agentic RAG: Embracing The Evolution
Imagine an AI assistant that doesn’t just retrieve documents from a static index, but actively plans, reasons, and adapts - diving into multiple knowledge sources, rerouting based on ambiguous queries, and validating its own outputs. This, in a nutshell, is the promise of Agentic Retrieval-Augmented Generation (RAG).
As LLM-powered
