RAG Agent Dynamics
The discussion delves into the intricacies of RAG agents, particularly the importance of determining how many retrieval attempts can be made. By segmenting problem spaces and optimizing data structures for specific queries, efficiency can be enhanced. Additionally, the potential for utilizing a multitude of tools is explored, emphasizing the need for precision in selecting the right tool for the task at hand.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Why Your RAG Pipeline Is Broken, and How to Fix It with Jason Liu - 709
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?
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?