Gen AI-based improved end-to-end data analytics tool

A generative artificial intelligence-based method for providing an improved end-to-end data analytics tool, the method is implemented by a processor executing instructions stored in a memory, the method comprises:

• collecting data from input unit(s) associated with multiple data sources located at disparate locations, wherein a data quality assessment is performed based on one or more pre-determined criteria;
• transforming the collected data using one or more data transformation operations based on the data quality assessment;
• determining relationships and patterns within the transformed data by analyzing one or more data parameters associated with the transformed data;
• generating prompts related to operational issues associated with a specific domain using one or more prompt generation technique and one or more prompt generation templates based on the determined relationships and patterns within the transformed data, wherein the prompts are provided to a Large Language Models (LLMs) as an input for generating outputs comprising diagnostic data and insights related to the operational issues,
• generating one or more domain specific recommendations by the LLM based on the generated outputs for resolving the operational issues;
• and
• determining an optimizal value of one or more modifiable prompt parameters associated with the generated prompts based on an iterative testing of the prompts and the generated outputs for customizing the LLM to generate the outputs relevant to the specific domain.

A computer program product comprising:

• a non-transitory computer-readable medium having computer program code stored thereon, the computer-readable program code comprising instructions that, when executed by a processor, causes the processor to:
• collect data from input unit(s) associated with multiple data sources located at disparate locations, wherein a data quality assessment is performed based on one or more pre-determined criteria;
• transform the collected data using one or more data transformation operations based on the data quality assessment;
• determine relationships and patterns within the transformed data by analyzing one or more data parameters associated with the transformed data;
• generate prompts related to operational issues associated with a specific domain using one or more prompt generation technique and one or more prompt generation templates based on the determined relationships and patterns within the transformed data, wherein the prompts are provided to a Large Language Model (LLM) as an input for generating outputs comprising diagnostic data and insights related to the operational issues;
• generate one or more domain specific recommendations by the LLM based on the generated outputs for resolving the operational issues;
• and
• determine an optimal value of one or more modifiable prompt parameters associated with the generated prompts based on an iterative testing of the prompts and the generated outputs for customizing the LLM to generate the outputs relevant to the specific domain.