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RAG is essential for a successful GenAI implementation. But what is it and why does it matter? We've got you covered with a quick and simple FAQ on RAG, check it out here: https://t.co/Ii4JSM0I7f #RAG #GenerativeAI #GenAI #AI https://t.co/mbxZPQMGDC
THIS is one of the best resources I’ve seen on RAG It has 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 you would need for figuring out how to build a full RAG pipeline, from chunking techniques, to vector search and filtering, to reranking or LLM fine-tuning. If you’re wondering what you need to… https://t.co/2Knawaik98
🙌Introducing Memory RAG—a simpler approach to RAG that leverages embed-time compute to create more intelligent, validated data representations. Build mini-agents with a simple prompt. Get the paper: https://t.co/X0sdzAuX2m https://t.co/N4dAqUIncF
Recent advancements in Retrieval-Augmented Generation (RAG) technology have been highlighted, particularly the introduction of Memory RAG, which utilizes embed-time compute to enhance accuracy by over 90% for mini LLMs. This new approach aims to optimize resource usage during inference by shifting computational demands to the embedding generation phase. The Memory RAG system is designed to create more intelligent and validated data representations, facilitating the development of specialized and cost-effective mini-agents. Additionally, comprehensive resources on building a full RAG pipeline have been shared, covering various techniques from chunking to LLM fine-tuning, emphasizing the importance of RAG in successful Generative AI implementations.
