RAG augments a standard LLM pipeline with a retrieval step; injecting external, often domain-specific data into the prompt before generation. This improves factual accuracy, enables source attribution, and avoids the need for expensive retraining. By decoupling retrieval from generation, each component does what it does best. In our new course on Retrieval Augmented Generation, you’ll learn how to build production-grade RAG systems, covering architecture, retrieval strategies, prompt design, and

Want to study Law? Here are LLM student, Rebecca’s 3 cool things about Law at Herts! 🧑‍⚖️ 🔎 Click the link in our bio or search “Law at Herts” to apply! #UniOfHerts #Law #Study [Video description: Rebecca talking to camera and showing us around the Law Court Building, including places like the Mock Courtroom, the Law Clinic, and the Alumni Wall.] | University of Hertfordshire