Abstract: Implementations described here are direct towards methods for generating content based on user prompts by intelligently selecting a model from a pool of available models. The selection process can be based on explicit user selection, analysis of user data (such as occupation, interests, or previously used models), and/or classification of the available models. Once a model is selected, it processes the prompt, potentially supplemented with publicly available or user-provided data, to generate content. The generated content is presented to the user, and further actions can be initiated based on the user's interaction with selectable GUI elements. Additionally, the system can recommend suitable models to the user based on their data and the community's model usage. By processing the user data and taking that into consideration to select the model that best suits a user’s needs it helps to eliminate and reduce the amount of wasted computation resources.

Abstract: Implementations described here are direct towards methods for generating content based on user prompts by intelligently selecting a model from a pool of available models. The selection process can be based on explicit user selection, analysis of user data (such as occupation, interests, or previously used models), and/or classification of the available models. Once a model is selected, it processes the prompt, potentially supplemented with publicly available or user-provided data, to generate content. The generated content is presented to the user, and further actions can be initiated based on the user's interaction with selectable GUI elements. Additionally, the system can recommend suitable models to the user based on their data and the community's model usage. By processing the user data and taking that into consideration to select the model that best suits a user's needs it helps to eliminate and reduce the amount of wasted computation resources.