LLMOps Fundamentals
LLMOps applies DevOps-style discipline — versioning, testing, monitoring — to prompts, models, and the pipelines around them.
Overview
LLMOps is the same instinct as DevOps and MLOps applied to LLM-based systems — treat prompts, evaluation suites, and model choices as versioned artifacts with tests and monitoring, rather than settings tweaked ad hoc in production.
Where It Fits
Prompt / Model Change
Automated Evaluation
Deploy
Production Monitoring
Informs the next change — a continuous loop.
Key Points
- Versioned prompts
- A prompt change is tracked like code, with a diff and a reason, not edited silently in place.
- Automated evaluation
- Changes run against a fixed evaluation suite before shipping, catching regressions before real users see them.
- Continuous monitoring
- Production behavior is watched continuously, since a prompt or model that worked at launch can degrade as usage patterns shift.
Interview Question
How is LLMOps different from traditional MLOps?
MLOps centers on training and deploying a model you own. LLMOps often deals with a third-party model you don’t control, so the operational surface shifts toward prompts, retrieval pipelines, and evaluation suites as the things being versioned, tested, and monitored, alongside model selection rather than model training.
Explain It in 30 Seconds
LLMOps brings DevOps-style discipline — versioning, automated evaluation, continuous monitoring — to prompts and the pipelines around an LLM, since a prompt or model choice can degrade over time just like any other production system.
Real-World Stack
Technologies commonly used to implement this in production.