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In this episode of our podcast, we are joined by Frank Wrubel, who works in research and development for the SAP Learning Systems team and is an experienced expert in learning technology. Our conversation revolves around the different areas and use cases of AI in SAP product learning. Frank differentiates between services for authors, including the generation of text and quizzes embedded in the workflow, and services for learners, such as knowledge discovery and learning advisory. He shares valuable lessons learned from leveraging AI for search, including strategies to achieve confidence scores as high as 92% by leveraging structured data and grounding techniques. He also provides recommendations for companies interested in leveraging AI for product learning. As the AI field progressively develops, we look into the future with topics like Auto Gen Studio (multi-agent frameworks) and applied prompt engineering. Finally, we get a glimpse into Frank's personal learning journey. This episode is a must-listen for anyone interested in AI and its application in learning.

Links

Frank Wrubel on LinkedIn: https://www.linkedin.com/in/frank-wrubel/
Matt Wolfe podcast: Matt Wolfe - YouTube: https://www.youtube.com/@mreflow/videos
What Is A Large Action Model (LAM)? - Dataconomy: https://dataconomy.com/2024/01/15/what-is-a-large-action-model-lam/#:~:text=A%20Large%20Action%20Model%20%28LAM%29%20is%20a%20sophisticated,understand%20and%20perform%20human%20actions%20on%20computer%20applications.
Multi-agent frameworks: https://microsoft.github.io/autogen

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Back to series:
Back to series:
INFORMATION CHAPTERS TRANSCRIPT SHARE FILES PLAYLIST

In this episode of our podcast, we are joined by Frank Wrubel, who works in research and development for the SAP Learning Systems team and is an experienced expert in learning technology. Our conversation revolves around the different areas and use cases of AI in SAP product learning. Frank differentiates between services for authors, including the generation of text and quizzes embedded in the workflow, and services for learners, such as knowledge discovery and learning advisory. He shares valuable lessons learned from leveraging AI for search, including strategies to achieve confidence scores as high as 92% by leveraging structured data and grounding techniques. He also provides recommendations for companies interested in leveraging AI for product learning. As the AI field progressively develops, we look into the future with topics like Auto Gen Studio (multi-agent frameworks) and applied prompt engineering. Finally, we get a glimpse into Frank’s personal learning journey. This episode is a must-listen for anyone interested in AI and its application in learning.

Links

Frank Wrubel on LinkedIn: https://www.linkedin.com/in/frank-wrubel/
Matt Wolfe podcast: Matt Wolfe – YouTube: https://www.youtube.com/@mreflow/videos
What Is A Large Action Model (LAM)? – Dataconomy: https://dataconomy.com/2024/01/15/what-is-a-large-action-model-lam/#:~:text=A%20Large%20Action%20Model%20%28LAM%29%20is%20a%20sophisticated,understand%20and%20perform%20human%20actions%20on%20computer%20applications.
Multi-agent frameworks: https://microsoft.github.io/autogen

Download transcript