AI Dictionary › Prompting
Active prompting is a technique that selectively chooses which examples to include in a few-shot prompt, prioritizing those on which the model shows greater uncertainty or variability in its responses, rather than choosing them randomly or in arbitrary order. The goal is to focus the examples precisely where the model needs them most.
The process involves testing the model on a set of candidate examples, measuring how much its responses vary or how uncertain they appear for each one, and then building the final prompt using primarily the examples that proved most difficult or ambiguous. This way, the prompt does not merely show easy cases the model already handles well, but reinforces precisely the areas of weakness.
This technique is useful when building reusable few-shot prompts for recurring tasks, where selecting the right examples can make a significant difference in response quality: classifying ambiguous texts, solving math problems with multiple variants, or handling edge cases in a customer support flow.
The term "active" echoes the concept of active learning, an idea already familiar in machine learning where a model or system actively chooses which data to label to maximize learning, here transposed to the selection of examples within a prompt.
Grace notices whether, when you build examples to guide the model, you pick the most ambiguous or difficult cases rather than the most obvious ones.
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