  Retrieving answers in a quick and low cost manner without hallucinations from
a combination of structured and unstructured data using Language models is a
major hurdle. This is what prevents employment of Language models in knowledge
retrieval automation. This becomes accentuated when one wants to integrate a
speech interface on top of a text based knowledge retrieval system. Besides,
for commercial search and chat-bot applications, complete reliance on
commercial large language models (LLMs) like GPT 3.5 etc. can be very costly.
In the present study, the authors have addressed the aforementioned problem by
first developing a keyword based search framework which augments discovery of
the context from the document to be provided to the LLM. The keywords in turn
are generated by a relatively smaller LLM and cached for comparison with
keywords generated by the same smaller LLM against the query raised. This
significantly reduces time and cost to find the context within documents. Once
the context is set, a larger LLM uses that to provide answers based on a prompt
tailored for Q\&A. This research work demonstrates that use of keywords in
context identification reduces the overall inference time and cost of
information retrieval. Given this reduction in inference time and cost with the
keyword augmented retrieval framework, a speech based interface for user input
and response readout was integrated. This allowed a seamless interaction with
the language model.
