Getting RAG right: A complete guide to building faster AI apps
What you'll learn
Large language models are powerful—but limited. Without access to real-time data, they hallucinate, mislead, or just fall flat. Retrieval-augmented generation (RAG) changes that by injecting up-to-date external knowledge directly into AI workflows.
In this in-depth technical guide, you’ll explore:
• How RAG systems work and where they fall short
• Step-by-step RAG architecture, including embedding, retrieval, and generation flows
• Advanced techniques for indexing, reranking, caching, and hybrid search
• Best practices for latency, relevance, and cost optimization
• How Redis powers high-performance RAG apps with vector search, semantic caching, and session memory
Did you know?
RAG-powered systems built on Redis helped reduce customer support response times by 80% while improving accuracy and scalability.
Whether you're building chatbots, search experiences, or internal AI tools, this guide will help you scale RAG the right way—without overpaying for inference or compromising speed.
Deploy fast or fall behind
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