---
_id: '13334'
abstract:
- lang: eng
  text: 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:
- first_name: Dominik
  full_name: Ullrich, Dominik
  id: '85823'
  last_name: Ullrich
- first_name: Jens
  full_name: Wallys, Jens
  id: '79046'
  last_name: Wallys
- first_name: Sven
  full_name: Hinrichsen, Sven
  id: '49010'
  last_name: Hinrichsen
citation:
  ama: Ullrich D, Wallys J, Hinrichsen S. <i>Conceptual Framework for Designing Domain-Specific
    LLM-Based Information Systems</i>. Vol 200. (Ahram T, Karwowski W, Giraldi  L,
    Benelli  E, AHFE Open Access, eds.). AHFE International; 2026:63-73. doi:<a href="https://doi.org/10.54941/ahfe1007065">10.54941/ahfe1007065</a>
  apa: 'Ullrich, D., Wallys, J., &#38; Hinrichsen, S. (2026). Conceptual Framework
    for Designing Domain-Specific LLM-Based Information Systems. In T. Ahram, W. Karwowski,
    L. Giraldi , E. Benelli , &#38; AHFE Open Access (Eds.), <i>Intelligent Human
    Systems Integration (IHSI 2026): Disruptive and Innovative Technologies</i> (Vol.
    200, pp. 63–73). AHFE International. <a href="https://doi.org/10.54941/ahfe1007065">https://doi.org/10.54941/ahfe1007065</a>'
  bjps: '<b>Ullrich D, Wallys J and Hinrichsen S</b> (2026) <i>Conceptual Framework
    for Designing Domain-Specific LLM-Based Information Systems</i>, Ahram T et al.
    (eds). USA: AHFE International.'
  chicago: 'Ullrich, Dominik, Jens Wallys, and Sven Hinrichsen. <i>Conceptual Framework
    for Designing Domain-Specific LLM-Based Information Systems</i>. Edited by Tareq
    Ahram, Waldemar Karwowski, Laura Giraldi , Elisabetta Benelli , and AHFE Open
    Access. <i>Intelligent Human Systems Integration (IHSI 2026): Disruptive and Innovative
    Technologies</i>. Vol. 200. AHFE International. USA: AHFE International, 2026.
    <a href="https://doi.org/10.54941/ahfe1007065">https://doi.org/10.54941/ahfe1007065</a>.'
  chicago-de: 'Ullrich, Dominik, Jens Wallys und Sven Hinrichsen. 2026. <i>Conceptual
    Framework for Designing Domain-Specific LLM-Based Information Systems</i>. Hg.
    von Tareq Ahram, Waldemar Karwowski, Laura Giraldi , Elisabetta Benelli , und
    AHFE Open Access. <i>Intelligent Human Systems Integration (IHSI 2026): Disruptive
    and Innovative Technologies</i>. Bd. 200. AHFE International. USA: AHFE International.
    doi:<a href="https://doi.org/10.54941/ahfe1007065">10.54941/ahfe1007065</a>, .'
  din1505-2-1: '<span style="font-variant:small-caps;">Ullrich, Dominik</span> ; <span
    style="font-variant:small-caps;">Wallys, Jens</span> ; <span style="font-variant:small-caps;">Hinrichsen,
    Sven</span> ; <span style="font-variant:small-caps;">Ahram, T.</span> ; <span
    style="font-variant:small-caps;">Karwowski, W.</span> ; <span style="font-variant:small-caps;">Giraldi
    , L.</span> ; <span style="font-variant:small-caps;">Benelli , E.</span> ; <span
    style="font-variant:small-caps;">AHFE Open Access</span> (Hrsg.): <i>Conceptual
    Framework for Designing Domain-Specific LLM-Based Information Systems</i>, <i>AHFE
    International</i>. Bd. 200. USA : AHFE International, 2026'
  havard: D. Ullrich, J. Wallys, S. Hinrichsen, Conceptual Framework for Designing
    Domain-Specific LLM-Based Information Systems, AHFE International, USA, 2026.
  ieee: 'D. Ullrich, J. Wallys, and S. Hinrichsen, <i>Conceptual Framework for Designing
    Domain-Specific LLM-Based Information Systems</i>, vol. 200. USA: AHFE International,
    2026, pp. 63–73. doi: <a href="https://doi.org/10.54941/ahfe1007065">10.54941/ahfe1007065</a>.'
  mla: 'Ullrich, Dominik, et al. “Conceptual Framework for Designing Domain-Specific
    LLM-Based Information Systems.” <i>Intelligent Human Systems Integration (IHSI
    2026): Disruptive and Innovative Technologies</i>, edited by Tareq Ahram et al.,
    vol. 200, AHFE International, 2026, pp. 63–73, <a href="https://doi.org/10.54941/ahfe1007065">https://doi.org/10.54941/ahfe1007065</a>.'
  short: D. Ullrich, J. Wallys, S. Hinrichsen, Conceptual Framework for Designing
    Domain-Specific LLM-Based Information Systems, AHFE International, USA, 2026.
  ufg: '<b>Ullrich, Dominik/Wallys, Jens/Hinrichsen, Sven</b>: Conceptual Framework
    for Designing Domain-Specific LLM-Based Information Systems, Bd. 200, hg. von
    Ahram, Tareq u. a., USA 2026 (AHFE International).'
  van: 'Ullrich D, Wallys J, Hinrichsen S. Conceptual Framework for Designing Domain-Specific
    LLM-Based Information Systems. Ahram T, Karwowski W, Giraldi  L, Benelli  E, AHFE
    Open Access, editors. Intelligent Human Systems Integration (IHSI 2026): Disruptive
    and Innovative Technologies. USA: AHFE International; 2026. (AHFE International;
    vol. 200).'
conference:
  end_date: 2026-02-13
  location: Florence
  name: '9th Intelligent Human Systems Integration (IHSI 2026): Disruptive and Innovative
    Technologies'
  start_date: 2026-02-11
corporate_editor:
- AHFE Open Access
date_created: 2026-01-09T07:27:32Z
date_updated: 2026-01-12T09:36:40Z
department:
- _id: DEP7020
- _id: DEP1305
- _id: DEP7000
doi: 10.54941/ahfe1007065
editor:
- first_name: Tareq
  full_name: Ahram, Tareq
  last_name: Ahram
- first_name: Waldemar
  full_name: Karwowski, Waldemar
  last_name: Karwowski
- first_name: Laura
  full_name: Giraldi , Laura
  last_name: 'Giraldi '
- first_name: Elisabetta
  full_name: Benelli , Elisabetta
  last_name: 'Benelli '
intvolume: '       200'
keyword:
- Retrieval-Augmented Generation
- LLM-Based Information System
- Conceptual Framework
language:
- iso: eng
page: 63-73
place: USA
publication: 'Intelligent Human Systems Integration (IHSI 2026): Disruptive and Innovative
  Technologies'
publication_identifier:
  isbn:
  - 978-1-964867-76-2
  issn:
  - 2771-0718
publication_status: published
publisher: AHFE International
quality_controlled: '1'
series_title: AHFE International
status: public
title: Conceptual Framework for Designing Domain-Specific LLM-Based Information Systems
type: conference_editor_article
user_id: '83781'
volume: 200
year: '2026'
...
---
_id: '11330'
abstract:
- lang: eng
  text: 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:
- first_name: Frederic
  full_name: Meyer, Frederic
  id: '70963'
  last_name: Meyer
- first_name: Lennart
  full_name: Freitag, Lennart
  id: '73431'
  last_name: Freitag
- first_name: Sven
  full_name: Hinrichsen, Sven
  id: '49010'
  last_name: Hinrichsen
- first_name: Oliver
  full_name: Niggemann, Oliver
  id: '10876'
  last_name: Niggemann
citation:
  ama: Meyer F, Freitag L, Hinrichsen S, Niggemann O. <i>Potentials of Large Language
    Models for Generating Assembly Instructions</i>. Vol 78. (IEEE, ed.). IEEE; 2024.
    doi:<a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>
  apa: Meyer, F., Freitag, L., Hinrichsen, S., &#38; Niggemann, O. (2024). Potentials
    of Large Language Models for Generating Assembly Instructions. In IEEE (Ed.),
    <i>2024 IEEE 29th International Conference on Emerging Technologies and Factory
    Automation (ETFA)</i> (Vol. 78). IEEE. <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>
  bjps: '<b>Meyer F <i>et al.</i></b> (2024) <i>Potentials of Large Language Models
    for Generating Assembly Instructions</i>, IEEE (ed.). Piscataway, NJ: IEEE.'
  chicago: 'Meyer, Frederic, Lennart Freitag, Sven Hinrichsen, and Oliver Niggemann.
    <i>Potentials of Large Language Models for Generating Assembly Instructions</i>.
    Edited by IEEE. <i>2024 IEEE 29th International Conference on Emerging Technologies
    and Factory Automation (ETFA)</i>. Vol. 78. Piscataway, NJ: IEEE, 2024. <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.'
  chicago-de: 'Meyer, Frederic, Lennart Freitag, Sven Hinrichsen und Oliver Niggemann.
    2024. <i>Potentials of Large Language Models for Generating Assembly Instructions</i>.
    Hg. von IEEE. <i>2024 IEEE 29th International Conference on Emerging Technologies
    and Factory Automation (ETFA)</i>. Bd. 78. Piscataway, NJ: IEEE. doi:<a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Meyer, Frederic</span> ; <span
    style="font-variant:small-caps;">Freitag, Lennart</span> ; <span style="font-variant:small-caps;">Hinrichsen,
    Sven</span> ; <span style="font-variant:small-caps;">Niggemann, Oliver</span>
    ; <span style="font-variant:small-caps;">IEEE</span> (Hrsg.): <i>Potentials of
    Large Language Models for Generating Assembly Instructions</i>. Bd. 78. Piscataway,
    NJ : IEEE, 2024'
  havard: F. Meyer, L. Freitag, S. Hinrichsen, O. Niggemann, Potentials of Large Language
    Models for Generating Assembly Instructions, IEEE, Piscataway, NJ, 2024.
  ieee: 'F. Meyer, L. Freitag, S. Hinrichsen, and O. Niggemann, <i>Potentials of Large
    Language Models for Generating Assembly Instructions</i>, vol. 78. Piscataway,
    NJ: IEEE, 2024. doi: <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.'
  mla: Meyer, Frederic, et al. “Potentials of Large Language Models for Generating
    Assembly Instructions.” <i>2024 IEEE 29th International Conference on Emerging
    Technologies and Factory Automation (ETFA)</i>, edited by IEEE, vol. 78, IEEE,
    2024, <a href="https://doi.org/10.1109/ETFA61755.2024.10710806">https://doi.org/10.1109/ETFA61755.2024.10710806</a>.
  short: F. Meyer, L. Freitag, S. Hinrichsen, O. Niggemann, Potentials of Large Language
    Models for Generating Assembly Instructions, IEEE, Piscataway, NJ, 2024.
  ufg: '<b>Meyer, Frederic u. a.</b>: Potentials of Large Language Models for Generating
    Assembly Instructions, Bd. 78, hg. von IEEE, Piscataway, NJ 2024.'
  van: 'Meyer F, Freitag L, Hinrichsen S, Niggemann O. Potentials of Large Language
    Models for Generating Assembly Instructions. IEEE, editor. Vol. 78, 2024 IEEE
    29th International Conference on Emerging Technologies and Factory Automation
    (ETFA). Piscataway, NJ: IEEE; 2024.'
conference:
  end_date: 2024-09-13
  location: Padova, Italy
  name: 29th International Conference on Emerging Technologies and Factory Automation
    (ETFA)
  start_date: 2024-09-10
corporate_editor:
- IEEE
date_created: 2024-04-12T07:06:41Z
date_updated: 2024-10-22T07:28:35Z
department:
- _id: DEP7020
- _id: DEP1305
doi: https://doi.org/10.1109/ETFA61755.2024.10710806
intvolume: '        78'
keyword:
- assembly instruction
- GPT
- large language model
- LLM
- prompt
language:
- iso: eng
place: Piscataway, NJ
publication: 2024 IEEE 29th International Conference on Emerging Technologies and
  Factory Automation (ETFA)
publication_identifier:
  eisbn:
  - 979-8-3503-6122-3
  isbn:
  - 979-8-3503-6123-0
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Potentials of Large Language Models for Generating Assembly Instructions
type: conference_editor_article
user_id: '83781'
volume: 78
year: '2024'
...