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From Structured Documentation to Intelligent Self-Service: Leveraging AEM Guides and Large Language Models

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  • Rakesh Konda

Abstract

This article explores the transformative potential of integrating structured documentation systems, specifically Adobe Experience Manager (AEM) Guides, with Large Language Models (LLMs) in the context of technical documentation and customer support. It delves into the foundations of the DITA framework and AEM Guides, highlighting their role in creating modular, reusable content. The article then examines the capabilities and current applications of LLMs in customer support, addressing both their strengths and limitations. The core of the article focuses on the synergy between AEM Guides and LLMs, detailing the technical implementation, data preparation considerations, and the resulting benefits such as improved response accuracy, enhanced scalability, cost reduction, and personalized user experiences. Challenges including data privacy, content quality maintenance, and balancing automation with human expertise are critically discussed. The article concludes by exploring future directions in AI-enhanced documentation and self-service systems, including potential advancements in AI technologies and emerging trends like predictive support and emotional intelligence in AI systems. This comprehensive article analysis provides insights into how the integration of structured documentation with advanced AI can revolutionize customer support and technical communication strategies in the digital age.

Suggested Citation

  • Rakesh Konda, 2025. "From Structured Documentation to Intelligent Self-Service: Leveraging AEM Guides and Large Language Models," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 265-274, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1071
    DOI: 10.32628/CSEIT25112360
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112360
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