  The advent of Large Language Models has revolutionized information retrieval,
ushering in a new era of expansive knowledge accessibility. While these models
excel in providing open-world knowledge, effectively extracting answers in
diverse linguistic environments with varying levels of literacy remains a
formidable challenge. Retrieval Augmented Generation (RAG) emerges as a
promising solution, bridging the gap between information availability and
multilingual comprehension. However, deploying RAG models in real-world
scenarios demands careful consideration of various factors. This paper
addresses the critical challenges associated with implementing RAG models in
multicultural environments. We delve into essential considerations, including
data feeding strategies, timely updates, mitigation of hallucinations,
prevention of erroneous responses, and optimization of delivery speed. Our work
involves the integration of a diverse array of tools, meticulously combined to
facilitate the seamless adoption of RAG models across languages and literacy
levels within a multicultural organizational context. Through strategic tweaks
in our approaches, we achieve not only effectiveness but also efficiency,
ensuring the accelerated and accurate delivery of information in a manner that
is tailored to the unique requirements of multilingual and multicultural
settings.
