Intent-based Prompting
A prompt engineering method that emphasizes stating the motivation and goal behind a request in the prompt, enabling the model to reason about objectives and thereby filter information more precisely, adjust the depth of explanation, and customize the output format.
Timeline (last 90 days)
Intent-based prompting emphasizes explaining "why this output is needed," prompting the model to reason about the goal during generation, thereby aligning more closely with real scenarios in information filtering, depth adjustment, and format selection.
Intent-based prompting and in-context learning complement each other: the model infers the task pattern from the examples and background provided in the prompt, while the intent description provides a higher-level semantic anchor for this inference.
Combining intent descriptions with few-shot examples lets the model understand both "why" and "how" at the same time, and is especially applicable to scenarios with complex output formats or strong domain knowledge, significantly reducing the probability of the model misinterpreting the intent.
All Facts (3)
Intent-based prompting emphasizes explaining "why this output is needed," prompting the model to reason about the goal during generation, thereby aligning more closely with real scenarios in information filtering, depth adjustment, and format selection.
50%UnverifiedIntent-based prompting and in-context learning complement each other: the model infers the task pattern from the examples and background provided in the prompt, while the intent description provides a higher-level semantic anchor for this inference.
50%UnverifiedCombining intent descriptions with few-shot examples lets the model understand both "why" and "how" at the same time, and is especially applicable to scenarios with complex output formats or strong domain knowledge, significantly reducing the probability of the model misinterpreting the intent.
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