hardGleanDatabricksOpenAI

The customer's corpus grew from one million documents to ten million and retrieval p99 tripled. Your code did not change. Find out why.

Everyone reaches for a bigger machine. The two mechanisms that actually cause this both scale with corpus size, both hit the tail before the median, and one of them is the permission filter your security review insisted on.

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UP NEXT ON YOUR JOURNEY

Next in this trackYou upgraded the embedding model and re-embedded the corpus. Nothing errored, no alert fired, and answer quality fell. Find out why.Next in this trackYour agent writes what it learns into memory. A wrong conclusion from three weeks ago now shapes every answer. Find it and remove it.Next in this trackThe agent works in the pilot and falls apart somewhere between ten and fifty concurrent runs. Diagnose it before you add capacity.

FEDITOR'S NOTE

Strong answers open by noticing that p99 moved and p50 barely did. Candidates who treat that as noise miss the whole diagnosis, because both real causes here are distributional: they lengthen the worst queries first and leave the typical one alone. The second-order point worth listening for is that the ACL filter, which someone added for good reasons, gets more expensive every time the corpus grows while the user's own slice stays the same size, and it does so with no code change and no alert.

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