@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}},
}