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

📰 ArXiv cs.AI

Researchers propose a human-centered workflow for using Large Language Models in content analysis via APIs

advanced Published 23 Mar 2026

Action Steps

Conceptualize LLMs as universal text processing machines
Employ LLMs via application programming interfaces (APIs)
Apply LLMs to three content analysis tasks: annotation, information extraction, and text generation
Evaluate and refine the workflow based on qualitative and quantitative results

Who Needs to Know This

Data scientists and AI engineers on a team can benefit from this workflow to improve content analysis tasks, while product managers can utilize it to develop more efficient content analysis tools

Key Insight

💡 LLMs can be used as universal text processing machines to enhance content analysis tasks

Key Takeaways

Researchers propose a human-centered workflow for using Large Language Models in content analysis via APIs

Full Article

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

Abstract:
arXiv:2603.19271v1 Announce Type: cross Abstract: 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 classificati

Read full paper → ← Back to Reads