RAG and UI Evolution

Patrick discusses the importance of specific prompts in switching models to a more reliable, fact-based mode, enhancing their utility with retrieved information. Tim reflects on the evolution of user interfaces, contrasting the old command-line systems with modern text-based interactions, while highlighting the transparency offered by tools like Gemini in code generation. The conversation raises questions about the balance between structured interfaces and textual communication.

In this clip
From this podcast
Machine Learning Street Talk (MLST)
Patrick Lewis (Cohere) - Retrieval Augmented Generation
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 in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256?
- 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?

RAG and UI Evolution

In this clip

From this podcast

Machine Learning Street Talk (MLST)

Patrick Lewis (Cohere) - Retrieval Augmented Generation

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 in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256?

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?