How do you design LLM systems that remain stable under model upgrades?

Stability under model upgrades is achieved using version pinning, canary deployments, shadow testing, and backward-compatible prompt design.

Model upgrades can change output behavior significantly. To maintain stability, systems use version pinning so services explicitly choose model versions. Canary deployments test new models on small traffic slices. Shadow testing runs new models in parallel without affecting production. Prompt engineering is also designed to be robust across model changes.