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Your prompt is nowhere near the model's stated limit, and the answers still get worse the more you add to it.
What you'll have at the end
A short test showing your own answer quality drop off at a measured input length, with the length you found written down
You need
You have programmatic API access to the exact model your production prompt calls, plus a real slice of the content that normally fills that prompt, like wiki pages, tickets, or retrieved documents.
Not covered
Fixing a prompt that already tests badly, whether by trimming, summarizing, or routing to a bigger model, is a separate job from measuring where it breaks, and isn't covered here.
Does temperature zero give the same answer twice?
go there first if you're unsure whether a wrong reply near your boundary is real length-driven drift or just temperature-zero's own occasional jitter
Compact old turns before your context window overflows
once you have a measured drop-off length, that's the length to compact toward, covered there
Checked 15 Aug 2026
Part of the Context Engineering cookbook