RAG and LLMs
The discussion highlights the transformative impact of retrieval-augmented generation (RAG) on the capabilities of large language models. This technique allows for interaction with proprietary or classified data without the need for direct training, opening up a wealth of applications. Notably, platforms like Bing are harnessing this approach to enhance user experience and data accessibility.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - 666
Related Questions
Do I understand correctly that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256 and the clip Evaluating LLM Responses?