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

1. Literature tracking: Follow LLM-AIOps research evolution
2. System design: Learn intelligent operations patterns
3. Benchmark comparison: Evaluate AIOps approaches
4. Research planning: Identify under-explored AIOps problems
5. Industry applications: Bridge research to production AIOps

References

• awesome-LLM-AIOps
• OpsEval Benchmark