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DeepNewz, mobile.
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Leading AI companies have introduced RAG 2.0, an end-to-end system for developing production-grade AI. RAG enhances the accuracy and depth of LLMs by accessing external data, offering better performance on downstream tasks. RAGTune, an open-source tool, allows easy experimentation for optimal RAG app configurations.
RAG for long context LLMs: Video Will long context LLMs really kill RAG? This is a talk @RLanceMartin gave at a few recent meetups that pull together threads from a few different projects to take a stab at addressing this. Multi-needle in a haystack shows limitations in… https://t.co/gGbps7pYHD
Retrieval-augmented generation (RAG) is the best way to specialize an LLM over your own data. Researchers have recently discovered a finetuning approach that makes LLMs much better at RAG... RAFT and specializing LLMs. Most use cases with LLMs require specializing the model to… https://t.co/1Da0RPuxJc
🆕📨 Newsletter 🚀 We're covering the Efficient Frontier of LLMs—achieving better, faster, cheaper AI solutions and how Knowledge Graphs are are being integrated into RAG systems for more coherent and accurate outputs 🌐 #LLM #GenAI #RAG https://t.co/EVf9AjEk8l
