Retrieval Augmented Generation

RAG, or retrieval augmented generation, enhances the capabilities of generative models by integrating relevant data from private domains. While many are still focused on improving prompts rather than fine-tuning models, the landscape is rapidly evolving with applications and models advancing at a remarkable pace. The discussion highlights the importance of leveraging community interactions and feedback to refine these systems effectively.

In this clip
From this podcast
Gradient Dissent - A Machine Learning Podcast
Spatial Data and AI: The Next Frontier in Technological Innovation with Paul Copplestone
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 and the clip Model Evaluation Insights?

Retrieval Augmented Generation

In this clip

From this podcast

Gradient Dissent - A Machine Learning Podcast

Spatial Data and AI: The Next Frontier in Technological Innovation with Paul Copplestone

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 and the clip Model Evaluation Insights?