Thomas Schmidhuber

Innovation Manager Technical Documentation, Rolls-Royce Solutions GmbH

Thomas Schmidhuber is working as Innovation Manager for Technical Documentation at Rolls-Royce. With over 20 years of experience in the application of AI in the fields of software engineering, mechanical engineering and technical documentation he brings in a broad experience.
Thomas holds degrees in information engineering, automotive engineering and business law and is keen on building the bridge between these fields. After a 4-year assignment in Suzhou and Nanjing for an automotive supplier, he joined Rolls-Royce Solutions (at that time MTU Friedrichshafen) in 2012 for building up a globally distributed software-testing team. After a 2-year assignment in a specialist role for knowledge-management he took the lead for a team within the technical documentation department in Rolls-Royce in 2021. Since then, he is pushing the organisation ahead with using modern software- and AI-tools to shorten delivery times, improve quality and making sure, modern standards are fulfilled.

All Sessions by Thomas Schmidhuber

  • May 22


  • TBD  - TBD
    With the advent of generative text AI systems many potentials to improve efficiency in technical documentation could be leveraged. The writing process is often supported with Large Language Models, with proofreading of the generated materials by humans. Using a Large Language Model with a human-in-the-loop for proofreading and checking against the required standards and often (unwritten) rules, the efficiency of the creation process may be further improved.

    Good prompt engineering is key for generating novel texts and making these fit into a company's language. When working with LLMs for proofreading, the quality of a good prompt and the provided additional material is getting even more critical to achieve the results that are expected. During the presentation, Thomas will provide an outline of what makes good input-data good and how to use the available data to make a Large Language Model an efficient proofreader that provides valuable insights. Using a human-in-the-loop approach, the LLM provides both an increased efficiency and also the required quality.

    AI and New Technologies in Technical Communication




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Thomas Schmidhuber