Clairos Learn Interactive UNIX, Web, and AI Operations lessons for practical technical skills
AI Ops command reference

Understand the command before you run it.

Review the bounded commands used throughout the AI operations track, then open a related course to practice each one in context.

Track progress 0/180 lessons 0/18 missions 0/11 briefings 12 courses
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Command index

Bounded commands, clear jobs, related practice.

Use this page as a quick refresher. Lessons provide the exact task, explanation, common mistake, and evidence needed to build the habit.

aiops models

Inspect the simulated model catalog, manifests, and version comparisons.

aiops prompts

Review versioned prompt metadata and deterministic prompt diffs.

aiops evals

Run and inspect bounded evaluation fixtures without model execution.

aiops rag

Inspect retrieval documents, chunks, index status, and reviewed results.

aiops traces

Read deterministic request traces, summaries, and latency evidence.

aiops guardrails

Review simulated guardrail policies, checks, and audit summaries.

aiops cost

Estimate cost and capacity from fixed training metrics.

aiops incidents

Review incident timelines, evidence bundles, and operator notes.

curl

Read only fixed loopback training endpoints; external URLs and request bodies are blocked.

jq

Query JSON fixtures with a small declarative path subset; no expression execution occurs.

docker

Inspect fixed container, log, stats, and compose fixtures; execution and pulls are blocked.

kubectl

Inspect or dry-run fixed Kubernetes fixtures; cluster mutations are blocked.

nvidia-smi

Read fixed accelerator and memory signals for capacity practice.

systemctl

Inspect the simulated AI gateway and supporting service state.

journalctl

Read bounded simulated AI service logs.

ss

Inspect the simulated loopback gateway listener.

Practice by course

Move from lookup to hands-on use.

0/15 AI systems foundations

Understand models, requests, components, failure boundaries, and the operator's evidence-first role.

15 short lessons · 1 checkpoint 0/12 CLI and workspace hygiene

Build careful command, path, redaction, configuration, and evidence habits for AI operations work.

12 short lessons · 3 checkpoints 0/16 Models, artifacts, and runtimes

Inspect model manifests, versions, artifacts, runtime compatibility, accelerators, and rollout evidence.

16 short lessons · 2 checkpoints 0/16 Inference requests and APIs

Inspect bounded loopback requests, response envelopes, status codes, timeouts, retries, and streaming signals.

16 short lessons · 3 checkpoints 0/14 Prompt and configuration operations

Version prompts and configuration, compare changes, validate variables, and prepare controlled rollbacks.

14 short lessons · 2 checkpoints 0/18 Evaluations and datasets

Operate golden sets, evaluation runs, thresholds, regressions, slices, annotations, and release evidence.

18 short lessons · 3 checkpoints 0/16 Retrieval and embeddings

Inspect document ingestion, chunks, embeddings, indexes, retrieval quality, freshness, and grounded evidence.

16 short lessons · 3 checkpoints 0/17 Serving, containers, and resources

Inspect services, containers, Kubernetes resources, GPU signals, health, configuration, and rollout readiness.

17 short lessons · 1 checkpoint 0/15 Observability and tracing

Use logs, metrics, traces, request identifiers, latency percentiles, and error budgets to explain behavior.

15 short lessons · 2 checkpoints 0/15 Safety, privacy, and governance

Review guardrails, redaction, data boundaries, access, retention, model cards, and operational approvals.

15 short lessons · 2 checkpoints 0/14 Cost, capacity, and reliability

Inspect token cost, capacity, concurrency, caching, fallback strategy, load signals, and reliability tradeoffs.

14 short lessons · 2 checkpoints 0/12 Incident response and capstones

Combine system, model, retrieval, serving, safety, cost, and evidence habits in operator-style incidents.

12 short lessons · 3 checkpoints