Building RAG Systems at the Edge

Retrieval-Augmented Generation (RAG) has revolutionized how we build AI applications, but deploying them at scale can be challenging. In this comprehensive guide, we explore how to build production-ready RAG systems using edge computing.

Traditional RAG implementations often struggle with latency and scalability issues. By leveraging edge infrastructure, we can significantly reduce response times and improve user experience globally.

We cover vector databases, embedding strategies, retrieval optimization, and prompt engineering techniques that work specifically well in edge environments.

Key topics include: chunking strategies, embedding models optimization, caching mechanisms, and handling real-time updates to your knowledge base.