Abstract: A method for generating an hallucination-free, retrospectively verified document on a specific subject begins with a processor receiving an original query related to the specific subject. The processor then apples a system query refiner and a user query to the original query to generate a refined query. Next, the processor generates an embedding of the refined query and applies the embedding to a vector database containing vector representations of data objects in a big data source to determine a similarity between the embedding and the vector representations. The processor then applies the refined query and the most similar vectors to a large language model, applies the large language model to the big data source, generates the verified document using data objects from the big data source, and returning the verified document and an identification for each data object.

Abstract: An artificial intelligence (AI) method includes a processor receiving, from a human user, a query on an external database; performing transformations on the query to produce a refined query; generating an embedding of the refined query; and generating a system prompt and a user prompt based on the refined query. The processor then applies the embedding to a vector database by executing a similarity routine to identify discrete vectors in the vector database most similar to the embedding, and collects the most similar vectors for application to a large language model. Next, the processor applies the most similar vectors, the refined query, and the system and user prompts to a large language model to generate a comprehensive response to the query. The response includes a text document that is generated by execution of the large language model.