Understanding RAG
RAG, or retrieval augmented generation, emerged from advancements in generative language models, particularly around 2019-2020. The combination of sequence-to-sequence models with dense embeddings laid the groundwork for modern retrieval systems. The rise of vector databases has significantly enhanced the scalability and efficiency of these retrieval methods, driving the growth of RAG applications since 2021.In this clip
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Practical AI
GraphRAG (beyond the hype)
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 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 with Cohere co-founder Nick Frosst on building LLM apps for business, in the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating RAG Systems?