. These models ingest enormous amounts of data from the web and use machine learning models to generate answers to user questions. In the early versions of these models, their capabilities are largely determined by pre-launch “in-house” training and testing — the quality of their answers improves only to a moderate extent with more users or more usage.
For example, LLMs could add new features that allow users to save and organize the responses they found most helpful into folders of favorites and delete those they don’t want to keep. They could add a document-creation feature where they would copy and edit LLM responses. They could also create challenge games and leader boards where the AI and users seek to answer questions, and users vote on the answers. And so on.
For example, Fitbit could create an integration with smart thermostats to enable the wearable to automatically control the ambient temperature during a user’s sleep. This would allow the wearable’s AI to adjust the temperature and determine its effect on the quality of a user’s sleep as measured by the wearable. The more a customer uses the wearable , the closer the wearable can come to figuring out the ideal temperature pattern for any given user.
Similarly, LLMs could integrate with whatever software or tools their answers are being used in. For example, they could integrate with content creation/editing software , which would allow them to observe which parts of their answers end up being used in the content created by users and use that data to improve their answers.Still, for many products, finding such ways of making user feedback inherent to product usage — directly or via integration — may be hard or impossible.
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