| name | llm-aiops |
| description | Use when working on AIOps (AI for IT Operations) tasks — incident management, root cause analysis, log parsing/anomaly detection, cloud infrastructure management, or applying LLMs to IT operations. Also use when researching LLM-based solutions for incident response, fault localization, or infrastructure-as-code. |
LLM AIOps Reference
Overview
Curated reference applying Large Language Models to AIOps, based on the awesome-LLM-AIOps research taxonomy. Covers incident management, log analysis, and infrastructure management.
When to Use
- Incidents: triaging, diagnosing, mitigating cloud incidents
- RCA: root cause analysis of system failures
- Logs: parsing, anomaly detection, logging statement generation
- Infrastructure: IaC, performance diagnosis, LLM training platforms
- AIOps QA: domain-specific question answering for operations
Taxonomy & Key Approaches
1. LLM for Incident Management
| Area | Key Approaches | Notable Systems |
|---|
| Surveys & Benchmarks | Evaluation frameworks for AIOps agents | AIOpsLab (MLSys 2025), ITBench (ICML 2025) |
| Incident Diagnosis | LLM agents + diagnostic tools | D-Bot (VLDB 2024), NetAssistant (NSDI 2024), FLASH (Preprint 2024) |
| Incident Reporting | Fine-tuning, CoT prompting for summarization | Oasis (FSE 2023), COLA (ICSE-SEIP 2024), MonitorAssistant (FSE 2024) |
| Root Cause Analysis |
