Method for group relative policy optimization reinforcement learning on structured data for code generation

A system comprising:

• one or more processors;
• and
• a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to:
• integrate received structured data and received unstructured data from at least one enterprise system for a plurality of data outputs;
• generate one or more smart prompts based on an alignment of one or more data outputs of the plurality of data outputs;
• retrieve documentation relevant to a particular workflow and appending the particular workflow to the one or more smart prompts;
• utilize a large language model to produce a plurality of candidate outputs based on the one or more smart prompts;
• score each candidate output of the plurality of candidate outputs using a language model scoring function and at least one domain-specific scoring function,
• wherein the domain-specific scoring function provides additional weighted values to one or more candidate outputs;
• calculate an average score across the plurality of candidate outputs;
• select at least one candidate output that exceed a predetermined threshold, the predetermined threshold associated with the average score across the plurality of candidate outputs;
• and
• update a plurality of parameters associated with a large language model based on the at least one selected candidate output.

A computer-implemented method comprising:

• integrating, by a processor, received structured data and received unstructured data from at least one enterprise system for a plurality of data outputs;
• generating, by the processor, one or more smart prompts based on an alignment of one or more data outputs of the plurality of data outputs;
• retrieving, by the processor, documentation relevant to a particular workflow and appending the particular workflow to the one or more smart prompts;
• utilizing, by the processor, a large language model to produce a plurality of candidate outputs based on the one or more smart prompts;
• scoring, by the processor, each candidate output of the plurality of candidate outputs using a language model scoring function and at least one domain-specific scoring function,
• wherein the domain-specific scoring function provides additional weighted values to one or more candidate outputs;
• calculating, by the processor, an average score across the plurality of candidate outputs;
• selecting, by the processor, at least one candidate output that exceed a predetermined threshold, the predetermined threshold associated with the average score across the plurality of candidate outputs;
• automatically generating, by the processor, code associated with the at least one selected candidate output;
• and
• updating, by the processor, a plurality of parameters associated with a large language model based on the at least one selected candidate output.