How do you design rollback strategies for LLM deployments?

Rollback strategies revert LLM systems to previous stable versions of models, prompts, or retrieval indices when degradation is detected.

LLM systems require rollback mechanisms because changes in prompts, models, or embeddings can degrade performance. Rollbacks involve switching version pointers in prompt registries, model routers, and vector databases. Canary deployments and shadow testing help detect issues before full rollback is needed.