Shadow Deployment

Shadow deployment, also called shadow mode or shadow testing, is the technique of running a new model in production alongside the current model — receiving the same real-traffic inputs — but discarding its responses rather than returning them to users, so that performance can be measured on real traffic without any user-visible risk. For LLM deployments, shadow mode lets you evaluate a candidate model (new base model, new fine-tune, new prompt version, new RAG configuration) against real user queries before exposing it. The setup: requests hit the production endpoint, get routed to the current model for the user response, and are concurrently dispatched (asynchronously, off the critical path) to the candidate model; the candidate's responses are logged alongside the production responses for offline comparison. Comparison can be automated (LLM-as-judge scoring, structured-output diff, latency and cost tracking) or sampled for human review. The technique is invaluable because offline eval sets always diverge from real traffic distribution — users ask things you didn't anticipate, in phrasings you didn't see, on topics that have shifted since your eval set was built. Shadow mode catches this drift. Practical considerations: duplicated traffic doubles inference cost, so shadow runs are often sampled (1-10% of traffic) rather than full duplication; user-identifying data should be handled identically in shadow and production for valid comparison; cumulative shadow logs over a 1-2 week window typically suffice for go/no-go decisions. AI governance teams require shadow evaluation before any model swap in regulated workflows because pure offline metrics have repeatedly proven insufficient to predict real-world performance.

Shadow rollouts from 25 years of safe-deployment practice: Centralpoint's content-deployment heritage — pre-production rehearsal, audience-restricted rollout, comparison against the live experience — is the same discipline that shadow mode requires of AI models. Shadow infrastructure stays on-premise, tokens meter per skill (including shadow tokens), and shadow-tested chatbots deploy through one line of JavaScript.


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