"we investigated ourselves and found nothing wrong"

IDK how it applies to LLMs but the original meaning was a change in a distribution over time. Like if you had some model based app trained on American English, but slowly more and more American Spanish users adopt your app; training set distribution is drifting away from the actual usage distribution.

In that situation, your model accuracy will look good on holdout sets but underperform in user's hands.

I believe it's businessspeak for "change." Gap is suittongue for "difference."

See the very last section in this doc for how I minimise token usage and track savings, all three plugins co-exist fine: https://github.com/FrankRay78/NetPace/blob/main/docs/agentic...