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Even if you’re new to AI.",[402,405,408,411,414,417],{"id":403,"workflowId":404},656,"2753",{"id":406,"workflowId":407},657,"2982",{"id":409,"workflowId":410},658,"4827",{"id":412,"workflowId":413},659,"2845",{"id":415,"workflowId":416},660,"2440",{"id":418,"workflowId":419},661,"5148",[421],{"id":422,"text":423,"url":424,"type":348},1649,"Browse all AI RAG templates","/workflows/categories/ai-rag/ ",{"__component":426,"id":427,"heading":428,"videos":433,"settings":13},"components.video-grid",24,{"id":429,"topTagline":430,"mainHeading":431,"bottomSummary":432,"variant":77,"subtitle":13},1434,"RAG agent video tutorials","Prepare to be AI-mazed","Watch our community’s most popular videos on building RAG workflows.",[434,443,452,461],{"id":435,"title":436,"description":437,"youtubeVideoId":438,"videoUrl":439,"author":440},93,"n8n at SCALE: Practical Strategies for Optimizing RAG","Join host, Angel Menendez, and expert guest, Mary Newhauser, as we dive into Retrieval-Augmented Generation (RAG), one of the most effective ways to enhance large language models with your own knowledge. This session will focus on practical strategies for optimizing RAG implementations directly in n8n.","S2UwtJJW0io","https://www.youtube.com/watch?v=S2UwtJJW0io",{"id":441,"name":442,"role":13,"avatar":13},498,"n8n",{"id":444,"title":445,"description":446,"youtubeVideoId":447,"videoUrl":448,"author":449},94,"From Zero to RAG Agent: Full Beginner's Course (no code)","Ready to build your first AI agent with real knowledge of your own data? In this step-by-step tutorial, I’ll show you how to go from zero to building your very first RAG (Retrieval-Augmented Generation) agent in just 20 minutes—using n8n and a Supabase vector database. ","cCD303XsUjI","https://www.youtube.com/watch?v=cCD303XsUjI",{"id":450,"name":451,"role":13,"avatar":13},499," Nate Herk | AI Automation",{"id":453,"title":454,"description":455,"youtubeVideoId":456,"videoUrl":457,"author":458},95,"Your ULTIMATE n8n RAG AI Agent Template just got a Massive Upgrade","This video introduces the ultimate n8n RAG template, which fixes the major flaws of traditional RAG—like lost context, poor cross-document understanding, and limited analysis. It showcases how Agentic RAG, Reranking, and Agentic Chunking work together to create an AI agent that intelligently explores your knowledge base, connects insights, and delivers truly comprehensive, context-aware answers.","iV5RZ_XKXBc","https://www.youtube.com/watch?v=iV5RZ_XKXBc",{"id":459,"name":460,"role":13,"avatar":13},500,"Cole Medin",{"id":462,"title":463,"description":464,"youtubeVideoId":465,"videoUrl":466,"author":467},96,"n8n RAG Masterclass - Build AI Agents + Systems that Actually Work","This video is your ultimate n8n RAG masterclass — a hands-on, step-by-step guide to building a production-ready Retrieval-Augmented Generation system using n8n and Supabase. You’ll learn what RAG is, how to build a no-code RAG agent, ingest files from Google Drive, apply metadata filters, manage records, OCR scanned files, set up web scraping triggers, and use hybrid search with reranking for top-quality results.","75lwkzFxyLs","https://www.youtube.com/watch?v=75lwkzFxyLs",{"id":468,"name":469,"role":13,"avatar":13},501,"The AI Automators",{"__component":471,"id":472,"heading":473,"blogPost":476,"settings":13},"components.related-blog-posts",28,{"id":474,"topTagline":13,"mainHeading":475,"bottomSummary":13,"variant":77,"subtitle":13},1435,"RAG guides to get you going",[477,480,483,486,489,492],{"id":478,"blogPostUrl":479},139,"https://blog.n8n.io/agentic-rag/",{"id":481,"blogPostUrl":482},140,"https://blog.n8n.io/rag-chatbot/",{"id":484,"blogPostUrl":485},141,"https://blog.n8n.io/evaluating-rag-aka-optimizing-the-optimization/",{"id":487,"blogPostUrl":488},142,"https://blog.n8n.io/llamaindex-vs-langchain/",{"id":490,"blogPostUrl":491},143,"https://blog.n8n.io/practical-evaluation-methods-for-enterprise-ready-llms/",{"id":493,"blogPostUrl":494},144,"https://blog.n8n.io/iterations-hallucinations-and-lessons-learned-rebuilding-our-ai-assistant-on-n8n/",{"__component":496,"id":497,"topTagline":13,"title":498,"description":13,"variant":499,"topImage":13,"badge":13,"ctaButtons":500,"settings":13},"components.cta-rich-section",191,"Push your RAG pipelines to prod faster with n8n","simple",[501,504],{"id":502,"text":503,"url":31,"type":32},1646,"Start building for free",{"id":505,"text":506,"url":36,"type":37},1647,"Get a demo",{"__component":508,"id":509,"layout":13,"heading":510,"faqElements":514,"settings":13},"components.faq",75,{"id":511,"topTagline":512,"mainHeading":513,"bottomSummary":13,"variant":77,"subtitle":13},1436,"FAQ about building RAG","Yes, we’re SOC2 certified. And other answers",[515,519,523,527,530,533,536,539,543],{"id":516,"question":517,"answer":518},468,"Why use RAG applications?","Retrieval-Augmented Generation (RAG) combines language models with external data sources instead of relying solely on the model's internal training data. This gives you:\n- Access to fresh and domain-specific data: RAG augments LLMs with up-to-date and proprietary information beyond pre-training limitations.\n- Improved trust and relevance: By grounding responses in real sources, RAG mitigates hallucination and enhances factual accuracy and user confidence.\n- Cost-effectiveness: RAG is more efficient than retraining LLMs for new content; just inject retrieval logic.",{"id":520,"question":521,"answer":522},469,"What is agentic RAG?","Standard RAG is accurate but inflexible. Agentic RAG is the upgrade, using smart AI agents in dynamic workflows. These agents choose the right tool for any query, like a database or web search, and verify their own answers.",{"id":524,"question":525,"answer":526},470,"What is a vector store?","A vector store is a special database designed to store and search high-dimensional vectors: numerical representations of text, images, or other data. When you upload a document, the vector store splits it into chunks and converts each chunk into a vector using an embedding model.",{"id":217,"question":528,"answer":529},"Can I build a RAG workflow without coding?","Yes! n8n’s hundreds of native integrations include all the most popular LLMs, vector stores, and databases. You can build an entire RAG agent in n8n’s drag-and-drop visual builder — from uploading to retrieving your data. Without writing a single script. Building a more complex agentic RAG system? Extending with code is available, too.",{"id":231,"question":531,"answer":532},"How does RAG improve the accuracy of generative AI models?","RAG sharpens model outputs and prevents reliance on vague or outdated training data by retrieving contextually relevant information from reliable sources.",{"id":243,"question":534,"answer":535},"How does RAG reduce AI hallucinations in n8n?","RAG anchors responses in fact-based documents rather than the model's internal memories. This reduces hallucinations by improving your data quality. But it may not eliminate all errors. Using n8n’s Evaluations feature, you can analyze and optimize outputs to reduce hallucinations in your RAG system even further.",{"id":255,"question":537,"answer":538},"Which vector databases does n8n support?","n8n has native integrations with Pinecone, Qdrant, Supabase, Weaviate, Milvus, Zep, MongoDB Atlas, and Postgres PGVector. You can access additional vector stores via API or custom setups.",{"id":540,"question":541,"answer":542},475,"How do I evaluate RAG applications in n8n?","n8n allows you to evaluate RAG workflows using techniques like string similarity, exact match, and LLM-as-a-Judge. You can also define custom metrics (like factual correctness and document relevance) to measure the relevance of the retrieved documents. 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alternatives",{"id":702,"url":1723,"label":1724,"hint":13},"https://blog.n8n.io/make-vs-zapier/","Make vs Zapier",{"id":1726,"url":1727,"label":1728,"hint":13},62,"https://blog.n8n.io/ai-agents/","AI agents",{"id":1730,"url":1731,"label":1732,"hint":13},63,"https://blog.n8n.io/ai-coding-assistants/","AI coding assistants",{"id":911,"url":1734,"label":1735,"hint":13},"https://blog.n8n.io/create-chatgpt-discord-bot/","ChatGPT Discord bot",{"id":1737,"url":1738,"label":1739,"hint":13},65,"https://blog.n8n.io/best-ai-chatbot/","Best AI chatbot",{"left":553,"top":553,"width":621,"height":621,"rotate":553,"vFlip":7,"hFlip":7,"body":1741},"\u003Cg fill=\"none\">\u003Cpath\n    fill=\"#fff\"\n    fill-rule=\"evenodd\"\n    d=\"M7.678 1.36H.481V0H10v9.52H8.64v-7.2L.962 10 0 9.038 7.678 1.36Z\"\n  />\u003C/g>",{"pageCount":565,"posts":1743},[1744,1751,1758,1764,1770,1776],{"title":1745,"excerpt":1746,"url":479,"imageUrl":1747,"tags":1748,"allTags":1750},"Agentic RAG: A Guide to Building Autonomous AI Systems","Standard RAG is accurate but inflexible. Agentic RAG is the upgrade, using smart AI agents in dynamic workflows. These agents choose the right tool for any query, like a database or web search, and verify their own answers. This guide explains what Agentic RAG is and shows you practical examples.","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/09/workflow-template--1-.png",[594,1749],"Guide",[594,1749],{"title":1752,"excerpt":1753,"url":482,"imageUrl":1754,"tags":1755,"allTags":1757},"Build a custom knowledge RAG chatbot using n8n","Learn how to build powerful RAG chatbots with n8n's visual workflow automation. This step-by-step guide demonstrates how to connect to any knowledge source, index it in a vector database, and create an AI-powered chatbot that provides accurate, context-aware answers.","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/01/Slide-16_9---86--1-.png",[594,1756],"Tutorial",[594,1756],{"title":1759,"excerpt":1760,"url":485,"imageUrl":1761,"tags":1762,"allTags":1763},"Evaluating RAG, aka Optimizing the Optimization","RAG isn’t foolproof. Explore common hallucinations, evaluation metrics, and how to improve RAG accuracy in n8n.","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/08/workflow-template--1-.png",[594],[594],{"title":1765,"excerpt":1766,"url":488,"imageUrl":1767,"tags":1768,"allTags":1769},"LlamaIndex vs LangChain: Which RAG tool is right for you?","LlamaIndex vs LangChain: Which is best for your LLM application? This guide compares these frameworks, highlighting their strengths and limitations for RAG use cases. We also introduce n8n as a low-code alternative that combines LangChain's flexibility with a user-friendly interface.","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/03/llamaindex-vs-langchain2-8--1-.png",[594,1749],[594,1749],{"title":1771,"excerpt":1772,"url":491,"imageUrl":1773,"tags":1774,"allTags":1775},"Practical Evaluation Methods for Enterprise-Ready LLMs","Discover practical evaluation methods for enterprise-ready LLMs. Learn how to measure accuracy, safety, and reliability—and see how n8n’s built-in evaluation tools make it easy to test and improve AI workflows.","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/08/meteric-based-evaluations--1-.jpg",[594],[594],{"title":1777,"excerpt":1778,"url":494,"imageUrl":1779,"tags":1780,"allTags":1781},"Iterations, hallucinations, and lessons learned: Rebuilding our AI Assistant on n8n","We like drinking our own champagne at n8n, so when it came to rebuilding our internal AI assistant, we decided to see if we could do it using our own tooling. And as engineers who think in code, it was an enticing challenge to step away from the command line and experiment on building with workflows. It took us a few months, but we did it, and pretty successfully too! Plus, we learned some valuable lessons along the way. If you’re planning on building AI tools, we hope our endeavors will prove j","https://storage.ghost.io/c/0d/78/0d78b34c-0c5f-4975-900e-61d00ccb1c2d/content/images/2025/02/rebuilding-ai-assistant1.png",[594],[594],1777565818405]