Retrieval Augmented Generation
Discover how Retrieval Augmented Generation (RAG) enhances foundation models by enabling them to dynamically retrieve relevant documents from internal databases. This technology significantly reduces the time employees spend searching for information, allowing for quick and accurate responses to queries such as company policies. By augmenting internal knowledge with external data, organizations can streamline operations and improve user experience.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
853: Generative AI for Business — with Kirill Eremenko and Hadelin de Ponteves
Related Questions
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 Vector Databases and the Power of RAG and the clip Evolution of AI, as well as in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data in the episode Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - 672 and the clip Instruction Tuning Insights?
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 Vectoring in on Pinecone with Cohere co-founder Nick Frosst on building LLM apps for business?