FixItFlow: Automated Troubleshooting Guide Generation from Cloud Incidents

2026-07-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Expert, extended

Summary

FixItFlow is an automated system designed to generate troubleshooting guides (TSGs) for cloud incidents using large language models. It addresses the significant challenges of manual TSG creation, which often results in incomplete coverage and outdated documentation; for instance, an internal study revealed over half of user complaints cited missing information (32.24%), broken links (13.32%), or incorrect instructions (11.21%). The system processes historical incident data, extracting diagnostic patterns from engineer actions, then synthesizes structured guides that include verified commands. A critical validation mechanism prevents the generation of fabricated content. In an evaluation involving 26 engineers, FixItFlow's generated guides achieved 61.5% positive ratings for clarity and demonstrated a 2.3x reduction in mitigation time for incidents where guides were available. This approach improves incident response efficiency and reduces the documentation burden on engineering teams.

Key takeaway

For Site Reliability Engineers aiming to improve incident response efficiency and documentation quality, FixItFlow demonstrates that automated troubleshooting guide generation is viable. You should explore LLM-driven solutions that incorporate strict anti-hallucination protocols, ensuring all generated commands are directly verifiable from historical incident data. This approach can reduce mitigation times by 2.3x and maintain high guide clarity, freeing your team from manual documentation burdens.

Key insights

FixItFlow automates troubleshooting guide generation from cloud incident data using LLMs, ensuring factual accuracy through strict validation.

Principles

• LLM outputs for technical procedures require character-level grounding to prevent fabrication.
• Incremental data processing and rigorous cleaning enhance signal quality from raw incident logs.
• Structured prompt engineering with role-play and output schemas improves LLM reliability.

Method

FixItFlow employs a four-stage pipeline: data extraction, preprocessing (three-stage cleaning), semantic analysis (LLM as expert SRE, 13 content categories, anti-hallucination), and guide synthesis (structured Symptom, Diagnosis, Mitigation sections).

In practice

• Apply character-by-character command verification when generating technical instructions with LLMs.
• Filter automated system messages and short comments to improve incident data quality for LLM processing.
• Define LLM roles (e.g., "Senior Site Reliability Engineer") and output schemas for structured guide generation.

Topics

• FixItFlow
• Automated Troubleshooting Guides
• Cloud Incident Management
• Large Language Models
• Anti-Hallucination
• Site Reliability Engineering

Best for: Research Scientist, AI Product Manager, CTO, AI Scientist, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.