A developer finds that zero-shot prompting (instructions only, no examples) produces inconsistent classification results. What is the BEST next step?
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Short Explanation and Infographic
When zero-shot falls short, the first and cheapest fix is few-shot examples. Show Claude exactly what good classification looks like with 2-5 representative examples.
Full explanation below image
Full Explanation
The prompt engineering ladder for improving performance: (1) zero-shot (instructions only), (2) few-shot (add examples), (3) more/better examples, (4) chain-of-thought, (5) fine-tuning. When zero-shot produces inconsistent results, adding high-quality examples is the fastest, cheapest improvement to try. Select diverse, representative examples that cover different cases the model will encounter. Option A (upgrade model) is premature and expensive when few-shot often resolves inconsistency. Option C (remove instructions) would perform worse. Option D (increase temperature) is counterproductive — lower temperature improves consistency for classification.