RAG and GenAI Insights
The discussion delves into the nuances of retrieval augmented generation (RAG) and its relationship with generative AI models. While RAG allows users to enhance prompts with their own data, it doesn't alter the underlying model parameters. Key distinctions are made between pre-training, fine-tuning, and the use of external knowledge bases to optimize model performance at runtime.In this clip
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Practical AI
Only as good as the data
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 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 Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating RAG Systems?
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?