3. Pragmatic Filter
- For users: Emma recognised that not all tasks require strict factual accuracy. So the pragmatic filter could guide users to assess the practical utility of an output. If the model answer is useful for a particular task - such as generating ideas in a brainstorming session - then it's useful, even if it's not perfectly factual. This filter could remind users to think about the context: Is this a creative or brainstorming scenario where a useful, flexible answer is better than an accurate one?
- For developers: Developers could use this filter to prioritise output based on the intended purpose of user prompts. Contextual fine-tuning could encourage models to produce relevant, pragmatic answers that meet user needs, especially in exploratory or creative applications. In this way, the model could focus on providing answers that are "good enough" for the context, rather than absolute truths, creating a more flexible interaction.