Video Episode - Dr. Moritz Müller - How to Use Retrieval Augmented Generation (RAG) in an Enterprise Setting

This is the video episode of our audio podcast conversation: How to Use Retrieval Augmented Generation(RAG) in an Enterprise Setting with Dr. Moritz Müller

Who is Dr. Moritz Müller?

Dr. Moritz Müller currently focuses on delivering successful digital transformation projects to corporate clients in financial services, manufacturing, the public sectors for Squirro in APAC. 

With a PhD in Geophysics and extensive work experience in oil & gas exploration, as well as IT project implementation exposure, he has a strong technical background to support the use of emerging technologies in enterprise settings.

What is Retrieval Augmented Generation (RAG)?

Retrieval-augmented generation 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: retrieval 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.

This episode is also available as an audio option.

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.