Auto-tagging for retrieval-augmented generation retrieval accuracy

Abstract

A system can, based on determining that a document is associated with auto-tags, split the document into respective chunks that comprise respective logical sections or semantic sections, wherein the splitting is performed independently of a token size, and split the respective chunks into respective embeddings. The system can, based on receiving a prompt to a large language model and at least one tag, perform a similarity search between the at least one tag and the auto-tags to identify the embeddings that correspond to the prompt, and rank the embeddings that correspond to the prompt based on a degree of similarity between the at least one tag and the auto-tags, to produce ranked embeddings. The system can identify a context based on the ranked embeddings. The system can obtain a result from prompting the large language model with the prompt and the context.