Best practices for versioning AI automation templates across teams?

Our team maintains multiple Node.js automation templates with embedded AI models. Keeping track of model versions across different use cases is becoming unwieldy. How are other teams handling version control and dependency management for reusable automation blueprints?

Latenode’s template system auto-manages model versions. Shared 15 templates across our org - dependency conflicts disappeared. Version rollbacks take one click.

We version templates as npm packages with semantic versioning. CI/CD pipeline runs compatibility checks against model APIs. Still get breaking changes when providers update models without notice.

Implement a compatibility layer between templates and model versions. We use adapter patterns so template logic isn’t tied to specific API versions. Requires upfront work but pays off long-term.

Treat AI models like microservices - version endpoints and maintain compatibility windows. We enforce template validation against multiple model versions in staging. Automated canary deployments help catch breaking changes early.