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The Journey to a Consistent Quality Assurance Framework for RAG-based Answer Generation

  • Presentation
  • Artificial Intelligence (AI) in Technical Communication
  • 11. November
  • 14:00 - 14:45 PM (CET)
  • Plenum 1
  • Dr Gesine Stanienda

    Dr Gesine Stanienda

    • SAP SE
  •  Michael  Pflanz

    Michael Pflanz

    • SAP SE

Contents

A case study of the implementation of a QA framework in the SAP Joule Agent for RAG-based answer generation.


The presentation covers the initial problem statement and the 1.5-year development process from manual to fully automated evaluation of the different QA steps including state-of-the-art statistical methods and customized quality metrics derived from industry standards. We will be addressing questions regarding subjective and objective QA criteria, using LLM-as-judge metrics and repeatability of tests through automation and standardization. In addition, we will focus on a hybrid approach of Human-in-the-Loop and Full Automation, which we will showcase in the different phases of the QA process.

 

Takeaways

The audience will learn how AI-based answers can be evaluated using a “human-only” versus a “human-LLM-hybrid” approach to run at scale in a business software context.

Prior knowledge

Keine

Speakers

Dr Gesine Stanienda

Dr Gesine Stanienda

  • SAP SE
Biography

Geboren 1973 in Würzburg

Promotion 2002 in Statistischer Linguistik an der LMU München

Seit 2004 in der SAP:

  • Globalization Services
  • User Assistance
  • Machine Translation
  • Software Implementation

Seit 2023 im Bereich Generative AI in der SAP

Schwerpunkte:

  • Qualitätssicherung der RAG-basierten Konversation
  • Erweiterung und Vertiefung des internen AI Portfolios
 Michael  Pflanz

Michael Pflanz

  • SAP SE
Biography

Geboren in Schwetzingen 1976

2003 Diplom Betriebswirt  - Innovationsmanagement - FH Heidelberg 

Seit 2008 in der SAP: 

  • Knowledge Management
  • Learning Experience (MOOCs)
  • Internal Communication

Seit 2022 im Bereich User Assistance

Schwerpunkte:

  • Cross Process Integration
  • Agile Methodology ( Scrum)
  • Erweiterung und Vertiefung des internen AI Portfolios