AI Coding with Few-Shot Prompting for Thematic Analysis

Samuel Flanders, Melati Nungsari, Mark Cheong Wing Loong
4/10/2025
cs.CL

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Abstract

This paper explores the use of large language models (LLMs), here represented by GPT 3.5-Turbo to perform coding for a thematic analysis. Coding is highly labor intensive, making it infeasible for most researchers to conduct exhaustive thematic analyses of large corpora. We utilize few-shot prompting with higher quality codes generated on semantically similar passages to enhance the quality of the codes while utilizing a cheap, more easily scalable model.

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Cite this paper

@article{flanders2025coding,
  title  = {AI Coding with Few-Shot Prompting for Thematic Analysis},
  author = {Samuel Flanders and Melati Nungsari and Mark Cheong Wing Loong},
  year   = {2025},
  eprint = {2504.07408},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.07408v1}
}

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