Retrieval-Augmented Generation
Discover the intricacies of retrieval-augmented generation as Patrick shares his expertise on the evolution of language models and the challenges of prompt engineering. He emphasizes the importance of evaluation metrics like answerability, fluency, and perceived utility in assessing model performance. With practical tips for implementing RAG systems, this conversation is a must-listen for anyone interested in the future of AI and language processing.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Patrick Lewis (Cohere) - Retrieval Augmented Generation
Related Questions
How do you use Retrieval-Augmented Generation (RAG) with a Language Model as discussed in the episode Patrick Lewis on Retrieval-Augmented Generation - Weaviate Podcast #76! and the clip Atlas Paper 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 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 Patrick Lewis on Retrieval-Augmented Generation - Weaviate Podcast #76! and the clip Unsupervised Retrieval Models?