mlsanityData to 7 Oct 2026

Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study

arXiv 2602.14357 ↗First posted 16 Feb 2026cs.HC cs.AIcited by 1

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Abstract (arXiv)

Large Language Models (LLMs) are increasingly developed for use in complex professional domains, yet little is known about how teams design and evaluate these systems in practice. This paper examines the challenges and trade-offs in LLM development through a 12-week ethnographic study of a team building a pedagogical chatbot. The researcher observed design and evaluation activities and conducted interviews with both developers and domain experts. Analysis revealed four key practices: creating workarounds for data collection, turning to augmentation when expert input was limited, co-developing evaluation criteria with experts, and adopting hybrid expert-developer-LLM evaluation strategies. These practices show how teams made strategic decisions under constraints and demonstrate the central role of domain expertise in shaping the system. Challenges included expert motivation and trust, difficulties structuring participatory design, and questions around ownership and integration of expert knowledge. We propose design opportunities for future LLM development workflows that emphasize AI literacy, transparent consent, and frameworks recognizing evolving expert roles.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 83 references that are in the map. First posted on arXiv 16 Feb 2026.

Builds on

40 of 83 references.

References · 40

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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28 more
  1. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidancecited by 0