  Natural language processing applications, such as conversational agents and
their question-answering capabilities, are widely used in the real world.
Despite the wide popularity of large language models (LLMs), few real-world
conversational agents take advantage of LLMs. Extensive resources consumed by
LLMs disable developers from integrating them into end-user applications. In
this study, we leverage an inverted indexing mechanism combined with LLMs to
improve the efficiency of question-answering models for closed-domain
questions. Our experiments show that using the index improves the average
response time by 97.44%. In addition, due to the reduced search scope, the
average BLEU score improved by 0.23 while using the inverted index.
