@misc{13334,
  abstract     = {{Retrieval-augmented generation (RAG) based on large language models (LLMs) has established itself as a key technology for combining domain-specific information with generative language skills, thereby providing transparent, up-to-date information. Many firms are already piloting such LLM-based information systems, but report a high degree of complexity in planning and implementation. A generally accepted regulatory framework that consistently maps key decisions is not yet available to companies. This article therefore presents a multi-level system that organizes design decisions throughout the configuration process. This framework is intended to support users in the planning, realizing, evaluation, and further development of an LLM-based information system. To achieve this goal, a qualitative-empirical research design was chosen. First, publications from the period 2022 to 2025 were identified and selected using a systematic literature search in accordance with the PRISMA guideline. The selected publications were then evaluated using a qualitative content analysis. The result is a system that was reviewed, revised and finalized at an expert workshop.}},
  author       = {{Ullrich, Dominik and Wallys, Jens and Hinrichsen, Sven}},
  booktitle    = {{Intelligent Human Systems Integration (IHSI 2026): Disruptive and Innovative Technologies}},
  editor       = {{Ahram, Tareq and Karwowski, Waldemar and Giraldi , Laura and Benelli , Elisabetta}},
  isbn         = {{978-1-964867-76-2}},
  issn         = {{2771-0718}},
  keywords     = {{Retrieval-Augmented Generation, LLM-Based Information System, Conceptual Framework}},
  location     = {{Florence}},
  pages        = {{63--73}},
  publisher    = {{AHFE International}},
  title        = {{{Conceptual Framework for Designing Domain-Specific LLM-Based Information Systems}}},
  doi          = {{10.54941/ahfe1007065}},
  volume       = {{200}},
  year         = {{2026}},
}

@misc{13291,
  abstract     = {{The application of Large Language Models (LLMs) for the automated generation of assembly instructions shows significant potential for improving work preparation in production processes. However, challenges remain regarding the overall information quality and precision of the generated instructions. In light of these challenges, this study explores how the information quality of automatically generated assembly instructions can be enhanced through the targeted provision of structured input data, such as Assembly and Quantity BOMs (Bills of Materials), as well as the use of optimized prompt chaining techniques. The methodology employs ChatGPT-4o in combination with Retrieval Augmented Generation (RAG) within the Microsoft Azure environment. The results demonstrate that structured data inputs, particularly the use of Assembly BOMs with defined Tool-to-Component relations, significantly improve the precision and relevance of the generated instructions. Despite these advancements, achieving consistent information quality remains a barrier to broader practical implementation. Therefore, feedback loops should be integrated into the assembly instruction generation process to ensure continuous refinement and reliability. Future research should investigate the use of RAG or similar frameworks, focusing on optimizing data structures and implementing feedback mechanisms to enhance the automated generation of assembly instructions.}},
  author       = {{Herbort, Robin and Green, Dominik and Hinrichsen, Sven}},
  booktitle    = {{Intelligent Human Systems Integration (IHSI 2025): Integrating People and Intelligent Systems}},
  editor       = {{Ahram, Tareq  and Karwowski, Waldemar  and Martino, Carlo  and Di Bucchianico, Giuseppe  and Maselli, Vincenzo }},
  isbn         = {{978-1-964867-36-6}},
  issn         = {{2771-0718}},
  keywords     = {{Assembly Instruction, Retrieval Augmented Generation (RAG), Large Language Model (LLM)}},
  location     = {{Rome, Italy}},
  pages        = {{765--775}},
  publisher    = {{AHFE }},
  title        = {{{Automatic Creation of Assembly Instructions by Using Retrieval Augmented Generation}}},
  doi          = {{10.54941/ahfe1005883}},
  volume       = {{160}},
  year         = {{2025}},
}

@misc{11330,
  abstract     = {{With the increasing complexity in manual assembly and a demographic decline in skilled workforce, the importance of well-documented processes through assembly instructions has grown. Creating these instructions is a time-consuming and knowledge-intensive task that typically relies on experienced employees. Although various automation solutions have been proposed to assist in generating assembly instructions, they often fall short in providing detailed textual guidance. With the rise of generative artificial intelligence (AI), new potentials arise in this domain. Therefore, this paper explores these potentials by employing various large language models (LLMs), prompting techniques and input data in an experimental setup for generating detailed assembly instructions, including the planning of assembly sequences as well as textual guidance on tools, assembly activities, and quality assurance measures. The findings reveal promising opportunities in leveraging LLMs but also substantial challenges, particularly in assembly sequence planning. To improve the reliability of generating assembly instructions, we propose a multi-agent concept that decomposes the complex task into simpler subtasks, each managed by specialized agents.}},
  author       = {{Meyer, Frederic and Freitag, Lennart and Hinrichsen, Sven and Niggemann, Oliver}},
  booktitle    = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{979-8-3503-6123-0}},
  keywords     = {{assembly instruction, GPT, large language model, LLM, prompt}},
  location     = {{Padova, Italy}},
  publisher    = {{IEEE}},
  title        = {{{Potentials of Large Language Models for Generating Assembly Instructions}}},
  doi          = {{https://doi.org/10.1109/ETFA61755.2024.10710806}},
  volume       = {{78}},
  year         = {{2024}},
}