Abstract: A computer-implemented method is disclosed for using generative AI for customer support. An AI model may be fine-tuned on the task of generating a template workflow answer given a prompt of real answers. In some implementations, an AI empathy model is trained/fine-tuned to customize template answers to be more empathic. In some implementations, the template workflow answer may include an API call step.

Abstract: Disclosed herein are techniques for generating contextual follow recommendations. Consistent with embodiments of the present invention, for each of several specific contexts—for example, a member opts to follow another specific member—a set of contextual follow recommendations are pre-computed. Then, in real time, when follow recommendations are being presented to the member, the recommendation system will first make a determination as to whether a member has taken action consistent with any particular context, and if so, a set of pre-computed contextual follow recommendations will be retrieved for possible presentation to the member.

Abstract: Described herein is a technique to generate and present follow recommendations. During a first stage or phase, training data are obtained by presenting follow recommendations to some randomly selected set of members, and then observing the collective members' responses. Using the training data, first and second predictive machine-learned scoring models are derived—the first scoring model for use in predicting when a member will opt to follow an entity being recommended, and the second scoring model for use in predicting if the member will engage with content presented via a newly formed follow edge. Then, using the scoring models, follow recommendations are derived, scored, and ultimately selected—based on their scores—for presentation to a member.