Practical lesson

Examples LLMOps

See where the skill appears in realistic work situations and what strong execution looks like.

The idea in one minute

LLMOps extends production engineering and MLOps practices to applications built around large language models. Because behavior depends on more than model weights, practitioners must manage prompts, model versions, retrieval indexes, tools, agent instructions, safety policies, evaluation sets and application code as a connected system. LLMOps includes repeatable development environments, versioning, CI/CD, evaluation gates, tracing, observability, latency and token-cost management, security, incident response, rollback, feedback loops and controlled experimentation. The central skill is making probabilistic AI behavior observable and changeable without losing evidence about what changed and why.

This capability connects directly with DevOps, AI Security, Retrieval-Augmented Generation (RAG). Open those concepts when the lesson depends on them rather than treating LLMOps as an isolated ability.

Real-world situations

  1. 1.Blocking a prompt update because a regression suite detects worse policy compliance
  2. 2.Tracing a RAG failure from user question through retrieval, prompt construction and model response
  3. 3.Routing low-risk requests to a cheaper model while preserving quality thresholds
  4. 4.Rolling back an agent release after tool-call errors exceed an operational threshold

What strong execution looks like

  1. 1.A capable practitioner versions every behavior-changing component, establishes offline evaluations before release, captures production traces safely, defines service and quality indicators, compares changes against baselines, and can roll back quickly. They distinguish model, retrieval, prompt, tool and application failures; monitor cost and latency alongside quality; and turn production incidents into new regression tests.
  2. 2.AI and platform engineers build deployment and observability systems; product teams define quality targets; security teams monitor abuse; data teams maintain feedback and evaluation datasets; operations teams manage incidents and service levels.

Build the surrounding skill cluster

DevOpsProfessional competency in devops for solving complex technical challenges and driving innovation.AI SecurityThe ability to protect AI-enabled systems from adversarial manipulation, data exposure, unsafe tool use, compromised dependencies, excessive permissions, and other attacks across models, applications, agents, and their surrounding infrastructure.Retrieval-Augmented Generation (RAG)The ability to design and evaluate AI systems that retrieve relevant external knowledge at query time and supply it as grounding context for generated answers or actions.AI Evaluation & BenchmarkingThe ability to design repeatable tests that measure whether an AI system is accurate, useful, safe, reliable, efficient, and fit for a specific real-world purpose.AI LiteracyThe understanding of artificial intelligence capabilities, limitations, and applications, combined with the practical ability to work effectively with AI tools and systems in professional contexts.

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