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.
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