Few-shot prompting gives different answers on near-identical inputs. How do you stabilize it?
Few-shot accuracy can swing on example order by itself. If your prompt is fragile to things that shouldn't matter, the fix is structural, not lucky example-hunting. Here is what genuinely reduces variance.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Few-shot accuracy can swing on example order by itself. If your prompt is fragile to things that shouldn't matter, the fix is structural, not lucky example-hunting. Here is what genuinely reduces variance.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
