llm-aiops · git:20260925.32ce6c0 · 2026-09-25 · sha256 ebb3526aa4c6c2de
llm-aiops git:20260925.32ce6c0A
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--- name: llm-aiops description: "LLM AIOps research reference: a curated map of LLM-based approaches to IT operations — incident management, root cause analysis (RCA), log parsing and anomaly detection, infrastructure-as-code and performance diagnosis, and AIOps Q&A. Use when triaging or designing LLM-driven incident response, fault localization, or log analysis, or when surveying the field's key systems, techniques, and benchmarks. Trigger phrases: AIOps, LLM for AIOps, incident management, incident diagnosis, root cause analysis, RCA, log parsing, log anomaly detection, logging statement generation, infrastructure-as-code, IaC diagnosis, AIOps benchmark, 智能运维, 根因分析, 故障定位, 日志解析, 日志异常检测, 运维问答." --- # LLM AIOps Reference ## Purpose A research reference library applying Large Language Models to AIOps, built on the [awesome-LLM-AIOps](https://github.com/Jun-jie-Huang/awesome-LLM-AIOps) taxonomy. It answers "which systems, techniques, and benchmarks exist for this operations problem, and which should I pick" — it does not run scans, parse logs, or execute anything. ## 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. - Trigger phrases: "LLM for AIOps", "AI for IT operations", "incident management", "root cause analysis", "log parsing", "log anomaly detection", "AIOps benchmark", 「智能运维」「根因分析」「故障定位」「日志解析」「日志异常检测」「运维问答」. ## When NOT to Use - The user wants an **agent to actually perform** triage, log parsing, or diagnosis on their live systems -> this library only names approaches; hand off to the relevant operational tool rather than pretending to execute. - The task is **generic LLM application design** (prompting, RAG, fine-tuning) with no operations context -> this library adds nothing; answer directly. - The user needs the **current state of a specific product or vendor** -> this is a research snapshot of papers through 2025; do not present it as a live product comparison. - The question is about **general software/agent engineering** -> use the repository's coding skills instead. ## Workflow 1. **Classify** the request into one of four topics: incident management, log analysis, infrastructure management, or technique/benchmark selection. 2. **Load exactly one routing target** from the Decision Rules table below. Do not preload the whole `references/` directory. 3. **Answer from the loaded file**, naming the concrete systems and their venue labels as given (e.g. `D-Bot (VLDB 2024)`). Do not paraphrase a system's method beyond what the table states. 4. **Check the pitfalls file** (`references/pitfalls-and-outlook.md`) before recommending a design, so the recommendation does not repeat a known mistake. 5. **State coverage limits** when the answer is not in the library instead of improvising a system or benchmark. ## Decision Rules | If the question is about... | Load | |---|---| | Incident triage, diagnosis, reporting, mitigation, postmortem, AIOps Q&A | `references/incident-management.md` | | Log parsing, log anomaly detection, logging statement generation | `references/log-analysis.md` | | Infrastructure-as-code, training-platform diagnosis, IaC benchmarks | `references/infrastructure-management.md` | | Which technique to use, or which benchmark to evaluate against | `references/techniques-and-benchmarks.md` | | Whether a design is a known anti-pattern, or what is deployed in production | `references/pitfalls-and-outlook.md` | - If the request spans two topics -> load both files, answer per topic, do not merge the taxonomies into one list. - If the request names a system not in the library -> say it is outside this library's coverage; do not invent a venue or year for it. ## Constraints - **Cite only what the library contains.** Never invent a system name, venue, or year. If a publication venue is given as `Preprint`, keep it as `Preprint`. - **Preserve venue labels and dates verbatim** — they are the credibility of the reference. - Treat this as a **research snapshot** (papers through 2025), not a product guide. Flag that publication recency is not implementation readiness. - Do not fabricate benchmark numbers or accuracy figures; the library records no metrics. - Do not claim the library's systems are production-deployed beyond the one sentence in `references/pitfalls-and-outlook.md`. ## Stop Conditions - Stop once the classified topic has been answered from its one reference file. - Do not browse the whole `references/` directory, and do not expand into open-ended web research: this library is the scope. - If two topic files disagree or leave a gap, state both and stop rather than reconciling by assumption. ## Verification - [ ] The answer names only systems present in the library, with their venue labels intact. - [ ] Exactly the topic files needed were loaded — not the whole directory. - [ ] Any design recommendation was checked against the pitfalls file. - [ ] Out-of-scope requests were declined explicitly rather than answered speculatively. ## Output - **Outcome**: a grounded answer naming the relevant systems, techniques, and benchmarks for the user's operations problem. - **Done when**: the question is classified, the matching reference file(s) were consulted, and every cited system traces to this library. - **Evidence**: the named systems with venue labels, plus an explicit statement of any coverage gap. ## References Load only when needed: - `references/incident-management.md` — read when the question concerns incident triage, diagnosis, reporting, mitigation, postmortem, RCA, or AIOps Q&A. - `references/log-analysis.md` — read when the question concerns log parsing, log anomaly detection, or logging statement generation. - `references/infrastructure-management.md` — read when the question concerns infrastructure-as-code, training-platform diagnosis, or IaC benchmarks. - `references/techniques-and-benchmarks.md` — read when choosing a technique or deciding which benchmark to evaluate against. - `references/pitfalls-and-outlook.md` — read before recommending a design, or when asked what is actually deployed and where the field is heading.