In LLM-driven applications, prompts dictate core business logic, safety boundaries, and data schemas—yet they are routinely buried in application code as unvalidated text strings. When a prompt change silently alters output formatting, degrades accuracy, or inflates token costs, traditional unit tests and linters pass without a warning.

This talk presents a cloud-native pattern for shipping prompts like software: treating them as first-class, declarative artifacts managed through GitOps.

We will explore how to package prompts into versioned manifests containing typed inputs, model parameters, and strict JSON output contracts. You will learn how to build an automated CI/CD pipeline using open-source evaluation tools to run golden datasets, LLM-as-judge assertions, and cost regression checks on every Pull Request.

Walk away with a concrete, vendor-neutral blueprint to version, test, and gate prompt deployments with the same engineering discipline as your microservices.