Module 01: LLMOps

LLMOps is the engineering discipline that closes the gap between a working LLM prototype and a production system you can trust, maintain, and improve over time. This module covers the full lifecycle: prompt management, fine-tuning pipelines, CI/CD for non-deterministic systems, dataset curation, and the platforms that make it all observable.

What You Will Learn

Lesson	Topic	Key Skill
01	What is LLMOps	Understand the LLMOps lifecycle and why it differs from MLOps
02	Prompt Versioning	Treat prompts as code - version, branch, and roll back
03	Fine-Tuning Pipelines	Build end-to-end pipelines from data to deployed model
04	LLM CI/CD	Non-deterministic CI gates, canary deployments, rollback
05	Dataset Curation	Build high-quality fine-tuning datasets from production logs
06	Evaluation-Driven Development	Write evals before prompts - the test-first mindset for AI
07	LLMOps Platforms	Compare LangSmith, Langfuse, W&B Weave, Arize Phoenix

Module Architecture

Key Concepts

• Prompt as code - prompts deserve the same version control, review, and testing discipline as application code
• Non-determinism - LLM outputs are probabilistic; traditional assertion-based testing breaks
• Silent degradation - model updates, prompt drift, and data distribution shifts degrade quality invisibly
• Eval-first - the only way to know if your system improved is to measure it; build evals before you build features
• The LLMOps flywheel - traces → feedback → datasets → evals → better prompts/models → better traces

Prerequisites

• Python 3.10+
• Familiarity with REST APIs and async Python
• Basic understanding of how LLMs work (tokenization, temperature, context window)
• Git fundamentals

Tools Covered

Prompt management: LangSmith Hub, Langfuse, PromptLayer

Observability: LangSmith, Langfuse, Arize Phoenix, W&B Weave

Fine-tuning: OpenAI Fine-tuning API, Hugging Face TRL, datasets library

CI/CD: GitHub Actions, custom Python eval harnesses

SDKs: anthropic, openai, langchain, langfuse, langsmith