Imagine asking a question in any language and instantly receiving a comprehensive answer, drawing from the vast, multilingual knowledge of the world. That future is closer than you think, thanks to advancements in multilingual Retrieval-Augmented Generation (mRAG). Traditional AI models often struggle to access and process information beyond English, creating a knowledge gap for non-English speakers. mRAG systems aim to bridge this gap by combining powerful language models with sophisticated retrieval systems capable of searching across diverse languages. At the heart of mRAG lies a two-step process. First, a multilingual retriever sifts through massive datasets like Wikipedia, identifying relevant passages in the user's native language or even related languages. Then, a multilingual language model generates a comprehensive answer, synthesizing information from the retrieved passages. Researchers at Naver Labs have been exploring the key components and challenges of building an effective mRAG system. Their work has uncovered the importance of careful prompt engineering—specifically, tailoring the instructions given to the language model to ensure responses are generated in the correct language and avoid unwanted code-switching. They've also discovered that existing evaluation metrics need adjustments to account for variations in spelling and transliterations across languages. While promising, mRAG is not without its challenges. Researchers identified limitations like occasional fluency errors in generated text, the problem of code-switching within responses, and the need for more robust cross-lingual retrieval systems. However, the potential of mRAG is undeniable. As these models improve, we can envision a future where language barriers no longer hinder access to information, fostering greater understanding and collaboration across cultures.