You version your code. You version your schemas. But your prompt templates live in environment variables, config files, or hardcoded strings — unversioned, ungoverned, and one edit away from breaking your AI application in ways that no test suite catches.

In this session, I'll show how to treat prompt templates as first-class versioned artifacts with compatibility rules — the same governance you already apply to Avro schemas or OpenAPI definitions. Using a live demo of a RAG-powered support chatbot built with LangChain4j and Ollama, I'll walk through what happens when a prompt template change silently degrades downstream behavior, and how registry-enforced validation catches it before production. The demo uses a schema registry as the governance layer, but the patterns work with any artifact store that supports versioning and compatibility checks.

Attendees will leave with:
- A reusable pattern for version-controlling prompt templates with compatibility enforcement
- A working example of a Quarkus + LangChain4j chatbot consuming governed prompts at runtime
- A clear framework for deciding when prompt governance pays off and when it's overkill