Dynamic few-shot prompting

Dynamic few-shot prompting builds upon few-shot prompting by dynamically including specific examples in the prompt to guide the model's responses. Adjusting the number and relevance of these examples optimizes context length and relevancy, thereby improving model efficiency and performance. Libraries like scikit-llm implement this technique using nearest neighbor search to fetch the most relevant examples aligned with the user query. This technique lets you make better use of the model’s limited context window and reduce token consumption. The open-source SQL generator vanna leverages dynamic few-shot prompting to enhance response accuracy.