Adaptive RAG: The Smart, Self-Correcting Framework for Complex AI Queries

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

Introduction: Why Adaptive RAG is a Game-Changer for AI Retrieval

When you ask your AI assistant a question, have you ever wondered how it decides whether to answer quickly from its memory or deep-dive into a knowledge base?

The article discusses Adaptive Retrieval-Augmented Generation (Adaptive RAG), a framework designed to enhance AI’s retrieval capabilities by balancing speed, accuracy, and smart decision-making. It outlines how Adaptive RAG determines the complexity of user queries and chooses the optimal source for answers—whether it be from memory, web search, or internal databases. The piece elaborates on its operational workflow, emphasizing features like query classification, routing decisions, self-correction, and hallucination checks, ensuring that AI responses are not only quick but also contextually relevant and accurate. Ultimately, Adaptive RAG represents an evolution in AI systems, making them more dynamic and capable of effective, intelligent retrieval in response to user queries.

Read the full blog for free on Medium.

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Published via Towards AI