You are evaluating prompting claims in a peer’s design document.

Yes, Yes, No, Yes, No

Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the model receives both the permissible labels and their meanings. Performance should then be measured against an evaluation set before additional prompt complexity is introduced. Few-shot examples are also sound for extraction because examples containing the exact field names demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as one of the most reliable methods for steering output format, structure, and consistency. Prompting Best Practices

Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task requires substantive decomposition or complex judgment, but routine classification and extraction may gain little while consuming more tokens and increasing latency. The technique should be selected according to evaluated task requirements.

An even-handed comparison governed by explicit criteria mitigates leading-question bias by requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion. Finally, prompt validation is model-specific. New model versions may follow instructions differently, change formatting behavior, alter tool selection, or produce different latency and cost characteristics. Anthropic’s migration guidance consequently recommends reevaluating prompts and workloads against the new baseline. Model Migration Guide

Study Guide references/topics: Zero-shot prompting; few-shot examples; reasoning techniques; neutral framing; model migration and regression evaluation.

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