Hybrid RAG Implementations: Combining Local and Cloud LLMs for Smarter Retrieval-Augmented Generation
In recent times, Retrieval-Augmented Generation (RAG) has become a powerful approach to make Large Language Models (LLMs) more reliable, accurate, and up-to-date. But as organizations grow, so does their need for flexible and secure RAG systems. That’s where Hybrid RAG comes in — a setup that combines Local and Cloud-based LLMs to get the best […]
Retrieval Augmented Generation (RAG): Beyond the Basics – Improving Contextual Accuracy with Hybrid Vector Databases
Artificial Intelligence models are only as smart as the information they access. While large language models (LLMs) have transformed how we generate insights, summarize data, and automate tasks, they still face one major challenge — staying accurate and relevant when the world changes. This is where Retrieval-Augmented Generation (RAG) steps in — and now, with […]