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DeepNewz, mobile.
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Retrieval Augmented Generation (RAG) is gaining prominence in the AI industry, enhancing the capabilities of large language models (LLMs) by incorporating factual data from external sources. This technology is being discussed for its potential to improve accuracy, scalability, security, and efficiency in AI systems. Several organizations, including SingleStore, Pryon, and BeePartners, are actively promoting RAG through webinars, new products, and educational courses. The technology is highlighted for fostering transparency in AI interactions and is considered a significant advancement over traditional fine-tuning methods in generative AI.
Are you facing challenges related to #data efficiency, #bias mitigation, or ensuring interpretability in your #RAG-powered #LLM development?🤔 ➡️Be a part of the discussion: https://t.co/l2rqHQL8x0 #LargeLanguageModels #RetrievalAugmentedGeneration #LLMApplications https://t.co/DWzuq20Dg0
What is RAG? This was widely requested and way overdue for a video. Retrieval Augmented Generation is something I learned about at @huggingface two years ago (authors of the paper were at hugging face). It's turned into the hottest Generative AI use case inside enterprises.… https://t.co/9HOC3lB2dx
Retrieval augmented generation. What is it, and why are all the AI gigabrains talking about it? @chiefbuidl dives deep into RAG, its challenges and limitations, why it’s being adopted as an alternative to fine-tuning, and what the next gen of LLMs might look like in this SxT… https://t.co/jUjrKADMZE
