Practical lesson

How to develop LLMOps

Turn the skill into repeatable behaviour with a staged practice plan.

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.

Start here

  1. 1.Version prompts and model configuration alongside application code
  2. 2.Create a small regression suite for one LLM feature
  3. 3.Record latency, token use and failure categories
  4. 4.Practice rolling a change back rather than editing production manually

Build working proficiency

  1. 1.Add tracing across retrieval and tool calls
  2. 2.Create CI evaluation thresholds for important behaviors
  3. 3.Build dashboards for quality proxies, latency, cost and errors
  4. 4.Compare model changes with controlled experiments

Stretch toward advanced practice

  1. 1.Design multi-model routing with quality and cost policies
  2. 2.Create incident response and rollback procedures for agentic systems
  3. 3.Build privacy-aware production feedback loops
  4. 4.Define SLOs that combine conventional reliability with AI task quality

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.

Keep building this skill

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