Forget RAG, welcome Agentic RAG

𝗡𝗮𝘁𝗶𝘃𝗲 𝗥𝗔𝗚
In Native RAG, the most common implementation nowadays, the user query is processed through a pipeline that includes retrieval, reranking, synthesis, and generation of a response.

This process leverages retrieval and generation-based methods to provide accurate and contextually relevant answers.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚
Agentic RAG is an advanced, agent-based approach to question answering over multiple documents in a coordinated manner. It involves comparing different documents, summarizing specific documents, or comparing various summaries.

Agentic RAG is a flexible framework that supports complex tasks requiring planning, multi-step reasoning, tool use, and learning over time.

𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
- Document Agents: Each document is assigned a dedicated agent capable of answering questions and summarizing within its own document.

- Meta-Agent: A top-level agent manages all the document agents, orchestrating their interactions and integrating their outputs to generate a coherent and comprehensive response.

𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀
- Autonomy: Agents act independently to retrieve, process, and generate information.

- Adaptability: The system can adjust strategies based on new data and changing contexts.

- Proactivity: Agents can anticipate needs and take preemptive actions to achieve goals.

Applications
Agentic RAG is particularly useful in scenarios requiring thorough and nuanced information processing and decision-making.

A few days ago, I discussed how the future of AI lies in AI Agents. RAG is currently the most popular use case, and with an agentic architecture, you will supercharge RAG!