Within a contact center, many caller and virtual agent (VA) interactions eventually reach a human agent (HA) for the proper resolution of a caller’s issues. This may happen despite the presence of a state-of-the-art artificial intelligence (AI)-powered VA component (that can, for example, search an organization’s knowledge bases (KBs) for the most relevant answers corresponding to a caller’s queries) through, for example, outdated KBs. As a result, a caller’s experience is negatively impacted. Techniques are presented herein that support an intelligent, proactive, auto-learning generative knowledge finder (KF) component which can be leveraged by a VA to automatically improve its competency level even if an organization's KBs are not current. Such a KF component may extract information from past caller-HA interactions and enrich itself with the help of an N-shot learning paradigm. Thus, without any manual intervention, a KF component, powered by generative AI, can enhance itself and, in turn, a VA's competency level beyond the VA's original intelligence (which may have been acquired through training and knowledge management system (KMS) access), exhibit a highly optimized reaction time, and dramatically lessen a HA’s involvement.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.