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SubgraphRAG uses simple perceptrons to fetch knowledge graph data, making LLMs smarter and faster 🎯 Original Problem: LLMs face issues like hallucinations and outdated knowledge. Knowledge Graph-based Retrieval-Augmented Generation (RAG) can help by grounding LLM outputs in… https://t.co/AORg4f9xFA
Explore the evolution from Retrieval-Augmented Generation (RAG) to Agentic RAG, enhancing AI's decision-making and autonomy. #AI #MachineLearning #RAG #AgenticRAG #ArtificialIntelligence #Sora #OpenAI #Google Read Here: https://t.co/ECRlj5KmHd https://t.co/xMwPXfaGqN
Agentic RAG is one of the most exciting developments in AI. What is Agentic RAG and why is it useful? The core idea is to build a more robust RAG system that can access external tools such as search engines, knowledge bases, and even other LLM-powered reasoning chains. The… https://t.co/UFY2b1ZENg
Microsoft researchers have unveiled LazyGraphRAG, a groundbreaking system designed to enhance Retrieval-Augmented Generation (RAG) capabilities without the need for prior summarization of source data. This innovative approach addresses the limitations of existing tools while leveraging their strengths. The development is part of a broader evolution in AI, which includes concepts like Agentic RAG, aimed at improving decision-making and autonomy in AI systems. Additionally, SubgraphRAG has been introduced, utilizing simple perceptrons to access knowledge graph data, thereby enhancing the intelligence and speed of large language models (LLMs). These advancements highlight ongoing efforts to refine AI technologies and improve their efficiency and reliability.
