What is LLMOps?

You've built an AI application. It works great in development. But how do you know it's working well in production? How do you measure quality? How do you catch problems before users do?

This is where LLMOps comes in.

LLMOps vs MLOps vs DevOps

Practice	Focus	Key Activities
DevOps	Software delivery	CI/CD, infrastructure, deployment
MLOps	Machine learning models	Training pipelines, model versioning, feature stores
LLMOps	Large language models	Prompt management, evaluation, token optimization

LLMOps inherits practices from both DevOps and MLOps, but addresses unique LLM challenges:

• Non-deterministic outputs: The same prompt can produce different responses
• Token economics: Every API call costs money based on input/output tokens
• Prompt engineering: Small prompt changes can dramatically affect quality
• Hallucinations: Models can confidently produce incorrect information

The LLMOps Stack

A complete LLMOps practice includes:

1. Observability - Tracing every LLM call, logging inputs/outputs
2. Evaluation - Measuring quality systematically, not just "it looks good"
3. Experimentation - A/B testing prompts, models, and configurations
4. Cost Management - Tracking and optimizing token usage
5. Alerting - Detecting quality degradation before users notice

Why LLMOps Matters

Without LLMOps, you're flying blind:

"We deployed our chatbot and it worked... until it didn't. We had no idea our response quality dropped 40% after a model update."

With LLMOps, you have confidence:

"We caught a 15% quality drop in our evaluation pipeline before it reached production. We rolled back in minutes."

In the next lesson, we'll explore the LLM production lifecycle and where evaluation fits in. :::