Across industries, individual users are embracing AI as their “digital coworker” – one who’s fast, tireless, and surprisingly helpful. Whether they’re drafting blog posts, crunching data, or writing code. AI can do it all. Yet, many organizations hesitate to fully integrate AI into their workflows.
Retrieval-Augmented Generation (RAG) systems are transforming the way we interact with large-scale language models by integrating external knowledge retrieval into the generation process. But as powerful as RAG is, it comes with its own performance challenges, especially when working with massive datasets and high query volumes. One way to make RAG faster and more efficient?…
This article explores three key areas: Generative AI and its patterns, the Retrieval-Augmented Generation (RAG) framework, and AWS’s role in supporting this journey. What is Generative AI? Generative AI is a type of artificial intelligence focused on the ability of computers to use models to create content like images, text, code, and synthetic data. The…
Throughout the history of technology, we’ve witnessed the evolution of software applications—from massive monolithic servers to sleek microservices and miniaturized platforms. History, indeed, has a way of repeating itself, and Generative AI is no exception to this cyclical progression. Today when you consume ChatGPT, Gemini, CoPilot the intelligence comes from the centralized computing: Consuming Large…