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Flip the order of my few-shot examples, and the exact same input gets a different answer.
What you'll have at the end
A balanced, auto-shuffled example set, plus a tallied log across several shuffles that settles on one majority label for a hard, ambiguous test case instead of flipping with whichever example happens to run last
You need
A working few-shot prompt for a two-label classification task, with at least four labeled examples already in it.
Not covered
Recalibrating the model's own answer probabilities with a content-free prompt; that needs access most chat interfaces don't expose, so this recipe fixes the bias through example hygiene instead.
Fix wrong-format answers with two or three few-shot examples
when you don't have few-shot examples picked yet at all, not diagnosing bias in ones you already have.
Vote across several chatbot answers with self-consistency prompting
when the same prompt gives different answers on repeat calls even with the examples held fixed, a reasoning-variance problem rather than an example-arrangement one.
Checked 18 Aug 2026
Part of the Prompt Engineering cookbook