Abstract: Techniques described herein involve providing an automated continual learning system for machine learning models. Embodiments include generating, using a machine learning model, an output based on a sample input provided to the machine learning model. Embodiments include evaluating the output based on comparing the output to an associated training output. Embodiments include creating a natural language rule based on the evaluating using a language processing machine learning model and storing the natural language rule in a rule library. Embodiments include receiving an input to the machine learning model and retrieving, in response to the input, one or more relevant rules from the rule library. Embodiments include generating, using the machine learning model, a response based on the input and the one or more relevant rules and performing an action based on the response.

Abstract: At least one processor may receive conversation data comprising a user input and a response to the user input generated by a large language model (LLM). The at least one processor may detect a behavior gap between the conversation data and at least one test conversation data comprising human-to-human communication samples. Based on the behavior gap, the at least one processor may retrain the LLM and/or provide an alternative response workflow for responding to the user input.