Retrieval-Augmented Generation (RAG) for Domain-Specific Question Answering

The emergence of Large Language Models (LLMs) has transformed the field of Natural Language Processing, but there are still many challenges to be overcome in the domain of specialized knowledge areas such as law, medicine, and engineering. The common models are plagued by the problems of hallucinations and information dilution, where the loss of specificity occurs in the context of very large datasets. Moreover, LLMs do not have the ability to access the proprietary data necessary for domain-specific accuracy in real-time. The Retrieval-Augmented Generation (RAG) model fills this void by anchoring the responses in trusted knowledge bases. Nevertheless, the conventional RAG models commonly face the problem of context inconsistency, where the documents retrieved contain keyword relevance but not semantic relevance. This paper explores the latest methodologies to improve the precision of RAG models, particularly in the context of the QuIM-RAG (Question-to-question Inverted Index Matching) approach and Context-Aware Similarity Validation. Our proposed solution paradigm changes the retrieval approach to a completely new inverted question-matching approach, where the queries of the user are matched to the hypothetical questions derived from chunks of documents. With the addition of a post-validation layer to remove irrelevant contexts, this approach completely eliminates hallucinations and increases the accuracy of factual information in critical settings.