brycewang-stanford/llm-aiops-guide
skills/43-wentorai-research-plugins/skills/domains/cs/llm-aiops-guide/SKILL.md
Papers on LLMs for IT operations and AIOps research
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SKILL.md
- name:
- llm-aiops-guide
- description:
- Papers on LLMs for IT operations and AIOps research
- emoji:
- 🖥️
- category:
- domains
- subcategory:
- cs
- keywords:
- ["AIOps", "LLM operations", "IT automation", "log analysis", "incident management", "DevOps AI"]
- source:
- https://github.com/Jun-jie-Huang/awesome-LLM-AIOps
LLM for AIOps Guide
Overview
A curated collection of research on applying LLMs to IT Operations (AIOps) — log analysis, anomaly detection, incident management, root cause analysis, and automated remediation. Tracks how foundation models are transforming traditional rule-based operations tooling into intelligent, adaptive systems. Relevant for CS researchers at the intersection of systems, NLP, and operations.
Research Areas
LLM for AIOps
├── Log Analysis
│ ├── Log parsing (template extraction)
│ ├── Anomaly detection (from log sequences)
│ ├── Log summarization
│ └── Root cause from logs
├── Incident Management
│ ├── Incident triage and routing
│ ├── Severity classification
│ ├── Similar incident retrieval
│ └── Resolution recommendation
├── Root Cause Analysis
│ ├── Topology-aware diagnosis
│ ├── Multi-signal correlation
│ └── Causal inference
├── Monitoring & Alerting
│ ├── Metric anomaly detection
│ ├── Alert correlation
│ ├── Noise reduction
│ └── Capacity planning
└── Automated Remediation
├── Runbook generation
├── Script generation
├── Self-healing systems
└── Change impact analysis
Key Papers
| Paper | Year | Focus | |-------|------|-------| | LogPPT | 2023 | Few-shot log parsing with prompt tuning | | OpsEval | 2024 | Benchmark for evaluating LLMs in AIOps | | D-Bot | 2024 | LLM-based database diagnosis | | RCAgent | 2024 | Agent for root cause analysis | | LogAgent | 2024 | Autonomous log analysis agent |
Use Cases
- Literature tracking: Follow LLM-AIOps research evolution
- System design: Learn intelligent operations patterns
- Benchmark comparison: Evaluate AIOps approaches
- Research planning: Identify under-explored AIOps problems
- Industry applications: Bridge research to production AIOps
References
- awesome-LLM-AIOps
- OpsEval Benchmark
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