RAG is effective in addressing challenges such as hallucinations and outdated knowledge. The Retrieval-Augmented Generation (RAG) architecture is a two-part process involving a retriever component and a generator component.
You must make various implementation decisions as you design your RAG solution. The following diagram illustrates some of the questions you should ask when you make those decisions.
The collection covers foundational RAG patterns to advanced multi-agent approaches, showing the evolution and sophistication of retrieval-augmented generation systems in modern AI applications.
Simple RAGarchitecture serves as the foundation for retrieval augmented generation systems, particularly suited for small e-commerce businesses that need to improve customer interactions without extensive technical infrastructure investments.
RAGdiagrams visually map this process, showing how user queries flow through data ingestion, vector databases, and language models. These diagrams are invaluable for understanding workflows, identifying bottlenecks, and planning integrations.
TL;DR RAG isn't dead it has evolved. Modern AI systems now use smarter, more specialized retrieval architectures to overcome the limits of basic "vector search + LLM" pipelines. The seven essential types you need to know are: Vanilla RAG, Self-RAG, Corrective RAG, Graph RAG, Hybrid RAG, Agentic RAG, and Multi-Agent RAG. Each solves a different weakness in traditional retrieval, from ...
The RAGpattern, shown in the diagram below, is made up of two parts: data embedding during build time, and user prompting (or returning search results) during runtime.
The seven RAG models discussed here are not merely current trends — they represent a forward-thinking approach to building systems that are both adaptive and robust.
