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Post-RAG Evolution: AI’s Journey from Information Retrieval to Real-Time Reasoning
- Retrieval-Augmented Generation (RAG) has revolutionized information retrieval by leveraging generative AI to generate coherent responses from vast sources.
- RAG excels at retrieving and generating text, but lacks deep reasoning capabilities and transparency in its decision-making process.
- Researchers are enhancing RAG to enable real-time reasoning, problem-solving, and decision-making with transparent logic.
- Structured reasoning advancements like Chain-of-thought reasoning (CoT) have improved large language models by enhancing context understanding.
- Agentic AI further enables AI to plan and execute tasks, improving reasoning and decision-making processes.
- Integrating CoT and agentic AI with RAG enhances its abilities for deeper reasoning, real-time knowledge discovery, and structured decision-making.
- RAG's core functionality involves converting data into embeddings for efficient retrieval and integrating real-time data for accurate responses.
- Retrieval-Augmented Thoughts (RAT) enhance RAG by continuously retrieving and reassessing information to refine conclusions.
- Retrieval-Augmented Reasoning (RAR) integrates symbolic reasoning techniques to ensure AI processes information through logical steps.
- Agentic Retrieval-Augmented Reasoning (Agentic RAR) embeds autonomous decision-making capabilities, improving adaptability to real-world challenges.
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