Agentic RAG Technology

The usage of AI agents opens up new possibilities for building more powerful, robust, and versatile Large Language Model(LLM)-powered applications. One possibility is enhancing RAG pipelines with AI agents in agentic RAG pipelines.

Mr Ed and his Agent Buddies are in the RAG pipelines.

Fundamentals of Agentic RAG

Agentic RAG describes an AI agent-based implementation of RAG. Before we go any further, let’s quickly recap the fundamental concepts of RAG and AI agents.

What is Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique for building LLM-powered applications. It leverages an external knowledge source to provide the LLM with relevant context and reduce hallucinations.

A naive RAG pipeline consists of a retrieval component (typically composed of an embedding model and a vector database) and a generative component (an LLM). At inference time, the user query is used to run a similarity search over the indexed documents to retrieve the most similar documents to the query and provide the LLM with additional context.

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