Few-Shot Prompting Implementation
Few-Shot Prompting — including 2–10 input-output examples in prompt. Model generalizes pattern to new inputs. Most effective for exact output format requirements or hard-to-describe behaviors.
Basic Few-Shot
Classification examples (sentiment, category). Model learns from pattern. 3–7 examples optimal.
Dynamic Few-Shot Selection
Use embeddings to select most relevant examples for each query based on similarity.
Few-Shot for Custom Style
Train model in corporate style through examples.
Example Selection Recommendations
- Diversity: cover different patterns
- Quality: only correct examples
- Size: 3–7 optimal
- Order: last example most relevant
- Balance: equal classes
Timeline
- Basic few-shot: 0.5–1 day
- Dynamic selection: 3–5 days
- A/B testing: 1–2 days