Practice vocabulary for designing few-shot prompts: example selection, negative examples, edge case coverage, and example ordering effects.
0 / 5 completed
1 / 5
A prompt engineer says 'The examples demonstrate the desired format.' What is the primary purpose of format-demonstrating examples?
Format-demonstrating examples teach the model the output structure through demonstration — showing the exact JSON schema, tone, length, or style expected. This is often more effective than instructions alone.
2 / 5
Your colleague says 'The examples should cover edge cases.' Why is edge case coverage important in few-shot design?
If your few-shot examples only show typical inputs, the model will generalize from those and may fail on unusual or ambiguous cases. Including edge case examples explicitly teaches the model how to handle them.
3 / 5
A prompt design guide mentions 'negative examples (what NOT to do).' When are negative examples useful?
Negative examples show the model what an incorrect or undesired output looks like, helping it avoid common mistakes. They are particularly useful when there's a specific failure pattern you want to guard against.
4 / 5
A researcher notes 'Order of examples affects output.' How does example ordering typically influence model behavior?
Research shows that recency effects in few-shot prompting mean the last examples tend to have more influence on the model's output. Placing your most representative or important examples near the end of the few-shot list can improve consistency.
5 / 5
A prompt engineer says 'The examples should span the input distribution.' What does this mean?
Spanning the input distribution means selecting examples that represent the full variety of inputs the model will encounter in production — different lengths, topics, phrasings, and difficulty levels — so the model learns robust patterns.