RAG Workflows Explained
Discover the power of retrieval-augmented generation (RAG) workflows, which enhance the capabilities of generative AI by injecting external data into prompts. This approach allows for more accurate responses by leveraging relevant information from various sources, such as documentation and webinars. The discussion highlights the effectiveness of this method, even in its simplest form, and explores the nuances of fine-tuning versus strategic data insertion.In this clip
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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 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, 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, as discussed in the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating RAG Systems?