Unlocking the Power of RAG: How Retrieval-Augmented Generation Enhances AI 🤖

Retrieval-Augmented Generation (RAG) is one of the most powerful architectural patterns in GenAI today—combining the strengths of large language models (LLMs) with real-time, external context from your own data. In this session, Brian Sam-Bodden breaks down what RAG is, why it matters, and how each component—from query rewriting to dense retrieval to semantic chunking—works behind the scenes to power more accurate, grounded, and up-to-date responses. Chapters 0:00 What is RAG and why does it matter? 0:40 LLM evolution and limitations 3:50 RAG: retrieval + generation 6:20 Vector databases and dense retrieval 8:30 Chunking and context windows 10:30 RAG query pipeline breakdown 14:50 Sample RAG interaction (chatbot demo) 16:40 Final thoughts Topics covered: LLMs and hallucinations Prompt engineering Semantic search with vector embeddings Chunking strategies Full RAG pipeline architecture Real-world examples of retrieval-powered AI Learn more about RAG with Redis: https://redis.io/docs/latest/develop/get-started/rag/ Watch our full Redis for AI playlist: https://www.youtube.com/playlist?list=PL83Wfqi-zYZFkvzYzKNgNTUFBYkrpz3p_ Resources & links Docs: https://redis.io/docs Try it out: https://redis.com/try-free Have questions? Drop them in the comments, we’re here to help. Subscribe for the rest of the series! https://www.youtube.com/@Redisinc?sub_confirmation=1 #Redis #retrievalaugmentedgeneration #llm About Redis We’re the world’s fastest in-memory database. From our open source origins in 2011 to becoming the #1 cited brand for caching solutions, we’ve helped more than 10,000 customers build, scale, and deploy the apps our world runs on. With cloud and on-prem databases for caching, vector search, and more, we’re helping digital businesses set a new standard for speed.