# A Human-Centered Workflow for Using Large Language Models in Content Analysis

arXiv: https://arxiv.org/abs/2603.19271 · HTML: https://mlsanity.com/paper/2603.19271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Best Practices for Text Annotation with Large Language Models](https://mlsanity.com/paper/2402.05129/) (2024)
- [Prompt Stability Scoring for Text Annotation with Large Language Models](https://mlsanity.com/paper/2407.02039/) (2024)
- [Evaluating Large Language Models as Expert Annotators](https://mlsanity.com/paper/2508.07827/) (2025)

## Abstract (arXiv)

While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box nature, prompt sensitivity, and tendency to hallucinate. To facilitate practical implementation, we provide supplementary materials, including a prompt library and Python code in Jupyter Notebook format, accompanied by detailed usage instructions.