Enhancing a large language model with domain context

A computer system comprising one or more processors, one or more memories, and one or more computer readable hardware storage devices, said one or more hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement a method for enhancing a large language model (LLM) with domain context, said method comprising:

• a validator data processor for:
• accessing a domain-specific ontology and generating a knowledge graph based on a domain-specific ontology of concepts and relationships;
• and
• extracting ontological semantics and factors from the knowledge graph using a semantic query;
• a multi-agent prompt orchestrator for:
• enhancing an input prompt with the extracted ontological semantics and factors;
• generating key performance indicators and conditional parameters from the ontology;
• infusing the LLM with key performance indicators and conditional parameters during LLM training and generating one or more infused input prompts based on the enhanced input prompt infused with the key performance indicators and conditional parameters;
• inputting the one or more infused input prompts into the LLM and obtaining a response;
• and
• comparing the response of the LLM against feedback.

A computer program product for enhancing a large language model (LLM) with domain context, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• access a domain-specific ontology and generating a knowledge graph based on a domain-specific ontology of concepts and relationships;
• extract ontological semantics and factors from the knowledge graph using a semantic query;
• enhance an input prompt with the extracted ontological semantics and factors;
• generate key performance indicators and conditional parameters from the ontology;
• during training of the LLM, infuse the LLM with key performance indicators and conditional parameters;
• generate one or more infused input prompts for based on the enhanced input prompt infused with the key performance indicators and conditional parameters for fine-tuning an LLM output to obtain a response;
• and
• compare the response of the LLM against feedback.