Systems and methods for generating generative artificial intelligence responses using context based hierarchical ontological representations

A method comprising:

• receiving, by a processor, input prompt data corresponding to an enterprise from at least one data source, wherein the input prompt data comprises a user query and user requirements;
• generating, by the processor, a query embedding vector representing the received input prompt data using an embedding model, wherein the query embedding vector comprises a numerical representation of the input prompt data indicating a semantic meaning corresponding to the input prompt data;
• retrieving, by the processor, at least one relevant node corresponding to the input prompt data from a pre-determined ontological representation by calculating cosine similarities between the generated query embedding vector and a plurality of embedding vectors of a plurality of nodes within the pre-determined ontological representation using a collapsed tree retrieval model;
• selecting, by the processor, a subset of nodes comprising the cosine similarities exceeding a predetermined retrieval threshold based on the retrieved at least one relevant node;
• generating, by the processor, contextual data corresponding to the input prompt data by aggregating textual content from the selected subset of nodes;
• generating, by the processor, an ontological representation corresponding to the input prompt data based on the generated contextual data, wherein the ontological representation comprises a plurality of nodes comprising the textual content and the embedding vectors derived using the embedding model;
• generating, by the processor, a Generative Artificial Intelligence (Gen AI) response to the input prompt data using at least one Gen AI model based on the generated contextual data and the generated ontological representation;
• validating, by the processor, the generated Gen AI response by comparing the Gen AI response with a reference response using at least one performance metric, wherein the at least one performance metric comprises at least one of accuracy-based measures, similarity-based measures, and quality assessment measures;
• and
• fine-tuning, by the processor, the at least one Gen AI model and the ontological representation based on results of the validation, wherein the fine-tuning of the ontological representation comprises updating a node content and the embedding vectors.

A non-transitory computer-readable medium comprising a processor-executable instructions that cause a processor to:

• receive input prompt data corresponding to an enterprise from at least one data source, wherein the input prompt data comprises a user query and user requirements;
• generate a query embedding vector representing the received input prompt data using an embedding model, wherein the query embedding vector comprises a numerical representation of the input prompt data indicating a semantic meaning corresponding to the input prompt data;
• retrieve at least one relevant node corresponding to the input prompt data from a pre-determined ontological representation by calculating cosine similarities between the generated query embedding vector and a plurality of embedding vectors of a plurality of nodes within the pre-determined ontological representation using a collapsed tree retrieval model;
• select a subset of nodes comprising the cosine similarities exceeding a predetermined retrieval threshold based on the retrieved at least one relevant node;
• generate contextual data corresponding to the input prompt data by aggregating textual content from the selected subset of nodes;
• generate an ontological representation corresponding to the input prompt data based on the generated contextual data, wherein the ontological representation comprises a plurality of nodes comprising the textual content and the embedding vectors derived using the embedding model;
• generate a Generative Artificial Intelligence (Gen AI) response to the input prompt data using at least one Gen AI model based on the generated contextual data and the generated ontological representation;
• validate the generated Gen AI response by comparing the Gen AI response with a reference response using at least one performance metric, wherein the at least one performance metric comprises at least one of accuracy-based measures, similarity-based measures, and quality assessment measures;
• and
• fine-tune the at least one Gen AI model and the ontological representation based on results of the validation, wherein the fine-tuning of the ontological representation comprises updating a node content and the embedding vectors.