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

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Indhold leveret af Squirro. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Squirro eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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

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 will also be available as a video episode and can be accessed both as a video and audio file.

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.

#retrievalaugmentedgeneration #ai #techpodcast #llms #generativeai

  continue reading

91 episoder

Artwork
iconDel
 
Manage episode 381477344 series 3454356
Indhold leveret af Squirro. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Squirro eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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

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 will also be available as a video episode and can be accessed both as a video and audio file.

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.

#retrievalaugmentedgeneration #ai #techpodcast #llms #generativeai

  continue reading

91 episoder

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