LLMOps: Building Production-Ready LLM Systems

A clear understanding of what LLMOps means and how it is different from MLOps when working with large language models

Hands-on practice building an LLM app architecture with separate ingestion and inference pipelines

Strong skills in RAG, including chunking text, creating embeddings, storing vectors, and checking results with a golden dataset

The ability to manage prompts as versioned system artifacts, enforce strict output formats, and reduce prompt injection risk through structured prompting patterns

Working knowledge of LLM evaluation, including LLM-as-a-judge scoring, repeatable tests, and using human feedback to improve answers

Hands-on experience in production hardening, including OWASP-aligned security controls, deployment using containerization, and capacity planning for cost and latency