With Dr. Moritz Müller
In this Episode, Dr. Moritz Müller - How to Use Retrieval Augmented Generation (RAG) in an Enterprise Setting - Lauren Hawker Zafer is joined by Dr. Moritz Müller
Retrieval-augmented generation (RAG), including enterprise RAG use cases, refers to a combination of two powerful natural language processing techniques: retrieval-based models and generative models. The approach is gaining significance and increasing attention for several reasons:
Improved Content Generation: retrieval-augmented generation allows generative models to access and integrate information from a broader context. By retrieving relevant information from a database or the internet, generative models can produce more accurate, coherent, and contextually relevant content.
Better Understanding and Contextualization: enterprise RAG helps generative models understand the context and topic more comprehensively. It enables the model to draw upon a wide range of knowledge sources, which is particularly important when dealing with complex or specialized topics.
Enhanced Abstraction and Creativity: by combining retrieval and generation, AI models can exhibit both the creativity of generative models and the grounded information retrieval capabilities. This leads to more creative content generation while maintaining accuracy.
Join Dr. Moritz Müller and our host Lauren Hawker Zafer to discover what RAG is and how it can be used in an enterprise setting.
