Selecting and Formatting Effective Examples
Explore techniques for creating effective few-shot prompts by deliberately selecting diverse, representative examples, formatting them consistently, and sequencing them strategically to enhance model reliability. Understand how coverage, formatting, and sequencing reduce variability and improve model performance in real applications.
The quality of your few-shot examples determines whether the model produces reliable, consistent outputs or drifts unpredictably across calls. The previous lesson established that correctness, representativeness, and consistency are the three quality dimensions that separate helpful few-shot prompts from harmful ones. This lesson translates those principles into concrete, repeatable techniques you can apply immediately. Most prompt engineers add examples ad hoc, grabbing whatever is convenient rather than deliberately curating a set that reflects what the model will actually encounter. Consider a support-ticket classifier that performs flawlessly on short, polite tickets but completely fails on long, frustrated ones, simply because every few-shot example happened to be short and polite. The model never learned that angry, rambling tickets exist. This lesson is built on three pillars that prevent exactly this kind of failure. Coverage addresses which examples to pick. Formatting addresses how to present them. Sequencing addresses what order to place them in. Each decision directly influences
Covering the input distribution
Input distribution coverage means that your set of examples should span the variety of inputs the model will encounter in production. This includes different lengths, tones, edge cases, and category boundaries. If your examples only represent one slice of reality, the model treats that slice as the entire world.
A useful principle to internalize ...