You explained an effective prompt versioning and rollout strategy. Describe your experience designing state models and how you integrate business logic within those models.

I treat prompts as first‑class entities stored in a versioned table with fields like prompt_id, version, content, and status. A PromptRegistry service exposes CRUD operations and a get_active_prompt(user_id) method that resolves the correct version based on feature flags or user segments. Business logic is encapsulated in a separate PromptEngine that receives the prompt text and context, applies transformations (e.g., token limits, style adjustments), and returns the final string. State models are simple Pydantic classes representing the prompt payload and its metadata; they enforce validation and provide a clear contract to downstream services. Rollouts are managed via a canary deployment: new prompt versions are tagged as beta, served to a small percentage of traffic, and monitored for latency or error rates. If metrics are acceptable, the status is promoted to stable. This decouples prompt evolution from core business logic, allowing safe experimentation without impacting existing workflows.