Enhancing Incident Response with AI - Assisted Runbooks: A Framework for Smarter Troubleshooting
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Study Papers | Computer Science and Information Technology | United States of America | Volume 14 Issue 2, February 2025 | Popularity: 4.7 / 10


     

Enhancing Incident Response with AI - Assisted Runbooks: A Framework for Smarter Troubleshooting

Binoj Melath Nalinakshan Nair


Abstract: Site Reliability Engineering (SRE) teams rely on runbooks to help them troubleshoot and manage incidents, but traditional runbooks can be rigid, outdated, and hard to maintain - especially in fast - evolving tech environments. This paper explores how integrating AI - assisted runbooks, powered by structured prompts, can make incident response faster, more efficient, and more reliable. We present a framework that uses Large Language Models (LLMs) and structured prompting to create flexible, context - aware troubleshooting guides. Techniques like few - shot prompting, chain - of - thought reasoning, and self - refinement are key to this approach.


Keywords: Prompt Engineering, Site Reliability Engineering, AI - assisted Runbooks, Large Language Models, Incident Management, Observability


Edition: Volume 14 Issue 2, February 2025


Pages: 714 - 716


DOI: https://www.doi.org/10.21275/SR25212002547


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Binoj Melath Nalinakshan Nair, "Enhancing Incident Response with AI - Assisted Runbooks: A Framework for Smarter Troubleshooting", International Journal of Science and Research (IJSR), Volume 14 Issue 2, February 2025, pp. 714-716, https://www.ijsr.net/getabstract.php?paperid=SR25212002547, DOI: https://www.doi.org/10.21275/SR25212002547

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