Practical lesson

Techniques & frameworks LLMOps

Use concrete methods, subskills, and practice structures instead of relying on vague advice.

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.

Core techniques and subskills

  1. 1.Release engineering
  2. 2.AI evaluation
  3. 3.Tracing
  4. 4.Observability
  5. 5.Reliability
  6. 6.Cost management
  7. 7.Feedback loops
  8. 8.Incident response

Ways to develop them

  1. 1.Develop LLMOps through a progression from observation to controlled practice to ownership. Use the existing beginner, intermediate, and advanced actions as a deliberate practice ladder. For each attempt, record the situation, method, expected outcome, result, feedback, and one change for the next attempt. Increase complexity only after results become repeatable.
  2. 2.Take one AI application from prototype to an intentionally operated service. Put prompts and configuration in version control, build regression tests, add traces, define release criteria, simulate a bad release and rollback, then use observed failures to expand the test suite.

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

Return to the complete guide for career context, evidence, related skills, practice and progression.

Open the complete LLMOps guide →