Patent ID: 11916767
Assignee: DROPZONE.AI, INC.
Field: Digital communication (Electrical engineering)
Classification: CPC H  G | IPC H

Claim 14:
15. A processor readable non-transitory storage media that includes instructions configured for monitoring activity in a computing environment, wherein execution of the instructions by one or more processors on one or more network computers performs actions, comprising:
providing one or more events associated with one or more resources or one or more activities in the computing environment;
determining one or more prompt fragments based on the one or more events;
generating a data structure for a prompt for training a large language model (LLM) based on a prompt template and the one or more prompt fragments, wherein the one or more prompt fragments are included in the prompt, and wherein information associated with the one or more events is included in the prompt; and
employing the prompt to train the LLM by performing further actions, including:
determining one or more actions for evaluating the one or more events based on machine-readable information included in a response provided by the LLM being trained;
executing the one or more actions to evaluate the one or more events, wherein a portion of the one or more events are classified based on the evaluation;
determining one or more portions of the response based on the one or more prompt fragments, wherein each determined portion of the response corresponds to at least one of the one or more prompt fragments; and
determining a performance score for each prompt fragment based on its corresponding determined portion of the response by causing further actions, including:
generating one or more synthetic events based on one or more scenarios, wherein an expected classification of each synthetic event is known in advance:
modifying the prompt to include the one or more synthetic events, wherein the prompt is modified to exclude each portion of the prompt fragments associated with a value of the performance score that is less than a threshold value, and wherein the modified prompt is provided to the LLM to generate another response;
determining one or more other portions of the other response based on the one or more prompt fragments, wherein each determined other portion of the other response corresponds to a prompt fragment;
comparing each determined other portion of the response to the expected classification of each synthetic event; and
updating the performance score for each prompt fragment based on the comparison; and

employing the modified prompt to retrain the LLM to execute one or more other actions and classify one or more other events.