Shadow Mode Deployment
Shadow Mode Deployment — Running a new model in parallel without affecting decisions.
Definition
Shadow Mode Deployment refers to running a new model in parallel without affecting decisions. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When running a new model in parallel without affecting decisions shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what shadow mode deployment is saying. If running a new model in parallel without affecting decisions moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Shadow Mode Deployment: what would falsify the current reading in the next window?
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