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Brilliant in the language everyone tests it in. Silent, then wrong, in the one your team actually uses.
What you'll have at the end
A saved test of your guessed blind spot against a real model, marked confirmed or not confirmed
You need
A model you already use for real work, plus one task from your own stack you could grade for certain: a program that must compile and run, a passage a fluent speaker could judge, or a fact you can check against a real document.
Not covered
How to fix a confirmed gap, through fine-tuning, retrieval, or switching models, is a separate, bigger project; this only proves whether the gap is real.
Leans on
A model keeps hallucinating about a product it has never seen
go there when the wrong answer is about one private fact nobody outside your company could know, not a whole subject the model was undertrained on
Read the model card, then decide if you can trust it
go there first if you just want to read what the vendor already documented about known weaknesses, before spending an afternoon building your own test
Checked 18 Aug 2026
Part of the Large Language Models (LLMs) cookbook