A structured six-step roadmap for building production-grade LLM systems in 2026, covering the full LLMOps lifecycle. Explains how LLMOps differs from traditional MLOps (prompt versioning vs. model versioning, non-deterministic outputs, cost as a first-class metric). Walks through prerequisites (Python, cloud basics, version control), then phases: instrumenting LLM calls with Langfuse tracing, building and evaluating RAG pipelines with RAGAS (faithfulness, answer relevance, context precision, context recall), adding guardrails and cost controls via LiteLLM (semantic caching, model routing, token auditing), and finally building stateful agents with LangGraph and evaluating them using heuristic evals, LLM-as-judge, and human annotation. Includes runnable code examples and a concrete step-by-step learning plan.
