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Forget RAG and welcome Agentic RAG!

By Armand Ruiz posted 27 days ago

  

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!


#GenerativeAI
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27 days ago

Great insights in your post! One concern I have with Agentic RAG is the challenge of managing operational costs and maintaining predictability, especially since it heavily relies on user behavior and the complexity of interactions. The high token usage for retrieval and response generation often results in unpredictable and potentially unsustainable costs as demand grows. Even Sam Altman has pointed out that their $200 subscription doesn’t fully cover operational expenses, which highlights how tough it can be to balance advanced capabilities with financial sustainability.

What are your thoughts on tackling these cost challenges? Could shifting to smaller, localized models help improve cost predictability? Or perhaps optimizing token efficiency with specialized hardware for inference, like IBM NorthPole or Grog LPU, could be the key? Or maybe it’s a combination of both? Would love to hear your thoughts!

27 days ago

AI agents are the the next big thing!