Method and system of determining anomalies in a target system using a large language model

A system for determining anomalies in a target system using a large language model (LLM), the system comprising:

• a processor;
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
• receive a user experience corresponding to the target system and specification data of the target system from a user;
• determine a cause corresponding to at least one anomaly in the target system by prompting the LLM based on a first interrogative prompt,
• wherein the first interrogative prompt is determined based on the first set of parameters;
• determine an impact of the cause corresponding to the at least one anomaly by prompting the LLM based on a second interrogative prompt,
• wherein the LLM is configured to determine the impact of the cause based on determination of a level of deviation of a user-defined behaviour of the target system with respect to a predefined behaviour;
• determine at least one region of interest (ROI) in the target system corresponding to the at least one anomaly by prompting the LLM based on a third interrogative prompt;
• determine one or more correlated ROIs in the target system corresponding to the at least one ROI by prompting the LLM based on a fourth interrogative prompt;
• and
• determine one or more correlational anomalies in the one or more correlated ROIs by prompting the LLM based on a fifth interrogative prompt,
• wherein the fifth interrogative prompt is determined based on a fifth set of parameters, and
• wherein the fifth set of parameters are determined based on a relational information of the one or more correlated ROIs and the at least one ROI, domain data of the target system and the specification information.

A non-transitory computer-readable medium storing computer-executable instructions for determining anomalies in a target system using a large language model (LLM), the computer-executable instructions configured for:

• receiving a user experience corresponding to the target system, and specification data of the target system from a user;
• determining a first set of parameters based on the user experience and the specification data;
• determining a cause corresponding to at least one anomaly in the target system by prompting the LLM based on a first interrogative prompt,
• wherein the first interrogative prompt is determined based on the first set of parameters;
• determining an impact of the cause corresponding to the at least one anomaly by prompting the LLM based on a second interrogative prompt,
• wherein the LLM is configured to determine the impact of the cause based on determination of a level of deviation of a user-defined behaviour of the target system with respect to a predefined behaviour;
• determining at least one region of interest (ROI) in the target system corresponding to the at least one anomaly by prompting the LLM based on a third interrogative prompt;
• determining one or more correlated region of interests (ROIs) in the target system corresponding to the at least one ROI by prompting the LLM based on a fourth interrogative prompt;
• and
• determining one or more correlational anomalies in the one or more correlated ROIs by prompting the LLM based on a fifth interrogative prompt,
• wherein the fifth interrogative prompt is determined based on a fifth set of parameters, and
• wherein the fifth set of parameters are determined based on a relational information of the one or more correlated ROIs and the at least one ROI, domain data of the target system and the specification information.