What you'll learn

AI applications are increasingly expected to support users in multiple languages—but traditional RAG pipelines often fall short in these scenarios. In this course, Multilingual and Cross-lingual RAGs, you’ll gain the ability to design and deploy retrieval-augmented generation systems that perform reliably across language boundaries.

First, you’ll explore why standard RAG architectures struggle with multilingual data and how cross-lingual embeddings make semantic retrieval possible, even when queries and content are in different languages. Next, you’ll discover how to implement a multilingual retrieval pipeline using vector databases and RAG frameworks, learning how to preprocess, embed, and retrieve content from diverse language sources. Finally, you’ll learn how to optimize and scale cross-lingual systems for production use, including latency reduction, metadata filtering, and multilingual knowledge base maintenance.

When you’re finished with this course, you’ll have the skills and knowledge of multilingual and cross-lingual RAG architectures needed to build scalable, reliable AI systems for a global user base.