[{"data":1,"prerenderedAt":379},["ShallowReactive",2],{"/blog/retrieval-augmented-generation-a-simple-guide-with-analogies":3,"authors":359},{"id":4,"title":5,"author":6,"body":7,"category":345,"date":346,"description":347,"extension":348,"image":349,"meta":350,"navigation":351,"path":352,"reviewer":353,"seo":354,"slug":355,"status":356,"stem":357,"__hash__":358},"blog/blog/retrieval-augmented-generation-a-simple-guide-with-analogies.md","What is Retrieval Augmented Generation (RAG): A simple guide with analogies","nyior-clement",{"type":8,"value":9,"toc":338},"minimark",[10,14,17,30,33,36,39,44,47,50,59,64,67,81,91,94,105,113,116,124,127,130,133,136,147,150,154,161,165,169,172,175,201,207,211,215,229,235,240,292,296,299,302,305,309,312],[11,12,13],"p",{},"Before discussing the Retrieval-Augmented Generation (RAG) pipeline, it helps to\nask a simple question: what exactly goes wrong when you use a foundation model on its own?",[11,15,16],{},"Most teams see familiar patterns:",[18,19,20,24,27],"ul",{},[21,22,23],"li",{},"The model hallucinated details that do not match reality.",[21,25,26],{},"It did not know the internal data.",[21,28,29],{},"You could not easily update its knowledge without retraining a model.",[11,31,32],{},"Imagine your company has product docs, support tickets, and internal guides. You ask a\nfoundation model a question like “Does our enterprise plan support SSO with provider X?”\nThe model has no idea what your plan actually includes, so it guesses based on patterns from\nthe general internet. Sometimes it is close. Sometimes it is dangerously wrong.",[11,34,35],{},"You need a way to give the model fresh, trusted context when the question is asked.\nYou also need a way to do this without retraining a model every time your documentation changes.",[11,37,38],{},"This is the idea behind RAG pipelines.",[40,41,43],"h1",{"id":42},"what-is-a-rag-pipeline","What is a RAG pipeline?",[11,45,46],{},"A RAG pipeline (Retrieval-Augmented Generation pipeline) is a system that helps an AI model\nanswer questions using your own data, not just what it learned during training.",[11,48,49],{},"Instead of asking the model to “know everything,” you let it:",[51,52,53,56],"ol",{},[21,54,55],{},"Retrieve the most relevant pieces of your own data for a given question, on the fly.",[21,57,58],{},"Augment the prompt so the model answers with that context in front of it.",[60,61,63],"h2",{"id":62},"rag-with-an-analogy","RAG with an Analogy",[11,65,66],{},"RAG is just a simple idea wrapped in a new language. Here is the pattern in plain English.",[18,68,69,72,75,78],{},[21,70,71],{},"You start with your documents",[21,73,74],{},"You break them into small, readable pieces",[21,76,77],{},"You convert each piece into a mathematical representation called an embedding",[21,79,80],{},"You store those embeddings in a vector database",[11,82,83,84],{},"That is the ",[85,86,87],"em",{},[88,89,90],"strong",{},"library.",[11,92,93],{},"When a user asks a question:",[18,95,96,99,102],{},[21,97,98],{},"You convert the question into an embedding",[21,100,101],{},"You search the vector database for the closest matches",[21,103,104],{},"You retrieve the pieces most likely to contain the answer",[11,106,83,107,112],{},[85,108,109],{},[88,110,111],{},"librarian",".",[11,114,115],{},"Then you pass the question and those retrieved pieces into a language model.",[18,117,118,121],{},[21,119,120],{},"The model uses your data as context",[21,122,123],{},"The answer becomes grounded, not guessed",[11,125,126],{},"That is the storyteller.",[11,128,129],{},"This is why RAG matters: it gives your model something it does not naturally have.\nAccess to your data, at the moment it is needed.",[11,131,132],{},"Everything else… is just an implementation detail.",[11,134,135],{},"If you are new to RAG, remember this:",[18,137,138,141,144],{},[21,139,140],{},"A library",[21,142,143],{},"A librarian",[21,145,146],{},"A storyteller",[11,148,149],{},"That’s the system. Everything else is just implementation detail.",[40,151,153],{"id":152},"key-stages-of-a-rag-pipeline","Key stages of a RAG pipeline",[11,155,156],{},[157,158],"img",{"alt":159,"src":160},"Key stages of a RAG pipeline ","/images/blogs/rag/rag-pipeline.png",[11,162,163],{},[85,164,153],{},[40,166,168],{"id":167},"stage-1-data-ingestion","Stage 1: Data ingestion",[11,170,171],{},"This stage answers the question “What information should my model have access to?”",[11,173,174],{},"Typical sources include product documentation, knowledge base articles,\nNotion pages, Confluence spaces, PDFs in cloud storage, or support\ntickets. During ingestion, you:",[51,176,177,183,189,195],{},[21,178,179,182],{},[88,180,181],{},"Load data:"," This is the stage where you connect to your chosen source\nand pull in the documents you want the system to work with. It could be files,\npages, or any other text-based content your pipeline relies on.",[21,184,185,188],{},[88,186,187],{},"Split data:"," Long documents are split into smaller segments to make them\neasier for the model to process. These pieces are usually kept below a specific size,\nfor example, around 500 characters, to make retrieval more precise.",[21,190,191,194],{},[88,192,193],{},"Embed data:"," Each chunk of text is transformed into a vector using an\nembedding model. This converts the text's meaning into a numerical\nform that the system can compare and work with.",[21,196,197,200],{},[88,198,199],{},"Store data:"," The vectors are then placed into a vector database.\nThis allows users to quickly find the most relevant chunks when they ask a question.",[11,202,203],{},[157,204],{"alt":205,"src":206},"RAG Stage 1: Data ingestion","/images/blogs/rag/rag-ingestion-pipeline.png",[11,208,209],{},[85,210,168],{},[40,212,214],{"id":213},"stage-2-retrieval-augmentation-and-generation","Stage 2: Retrieval, augmentation, and generation",[51,216,217,223],{},[21,218,219,222],{},[88,220,221],{},"Retrieval:"," When a user asks a question, the system converts that\nquestion into a vector using the same embedding model used during ingestion.\nThis query vector is then compared against all vectors in the database to\nfind the closest matches. These matches represent the pieces of text most\nlikely to contain helpful information for answering the question.",[21,224,225,228],{},[88,226,227],{},"Generation:"," The language model receives two things. The user’s\nquestion and the relevant text retrieved from the vector database. It\ncombines both inputs to produce a grounded response, using the retrieved\ninformation as context for the answer.",[11,230,231],{},[157,232],{"alt":233,"src":234},"RAG Stage 2: Retrieval and generation","/images/blogs/rag/rag-retrieval-pipeline.png",[11,236,237],{},[85,238,239],{},"Stage 2: Retrieval and generation",[241,242,243,244,247,250,253,267,270,273],"aside",{},"\n💡\n",[11,245,246],{},"A note on RAG",[11,248,249],{},"What we have described in this guide is the most basic form of RAG,\noften called naive RAG. It is the right place to start.",[11,251,252],{},"But as you build real systems, new challenges begin to appear:",[18,254,255,258,261,264],{},[21,256,257],{},"Retrieving the right context every time",[21,259,260],{},"Handling multi-step questions",[21,262,263],{},"Managing memory across conversations",[21,265,266],{},"Evaluating and improving reliability",[11,268,269],{},"These challenges lead to more advanced RAG patterns.\nWe are working on a follow-up guide that explores these ideas in depth.",[11,271,272],{},"We spend a lot of time crafting a guidelike that.\nAs we build these guides, we share small pieces along the way.\nLittle insights, simple explanations, things that might help you just a bit sooner.",[11,274,275,276,291],{},"If that kind of learning resonates with you, you’ll enjoy what we share on\non ",[88,277,278,285,286],{},[279,280,284],"a",{"href":281,"rel":282},"https://www.linkedin.com/company/guidely-ai-guides/",[283],"nofollow","LinkedIn"," and ",[279,287,290],{"href":288,"rel":289},"https://x.com/GuidelyAIGuides",[283],"X",".\nPlease follow us to catch them.",[60,293,295],{"id":294},"conclusion","Conclusion",[11,297,298],{},"At its core, RAG is not a complicated idea. It is a shift in how we use language models.",[11,300,301],{},"Instead of expecting the model to know everything, we give it the right information\nat the right time. That is the entire system. Retrieve what matters, add it to\nthe prompt, and let the model answer with context.",[11,303,304],{},"This small change has a big impact. A model that once guessed can now answer\nusing real, trusted information. A system that was static becomes dynamic,\nupdating as your data changes.",[60,306,308],{"id":307},"going-further","Going further",[11,310,311],{},"If you want to go further, a few resources will help:",[18,313,314,322,330],{},[21,315,316,317],{},"If you’re looking for a structured path to learning AI engineering from the ground up,\n",[279,318,321],{"href":319,"rel":320},"https://guidely.tech/blog/ai-engineering-roadmap",[283],"start with our guide on breaking into AI engineering",[21,323,324,325],{},"If you are still getting familiar with the core AI terminologies,\n",[279,326,329],{"href":327,"rel":328},"https://www.youtube.com/watch?v=72yyLA2wRWc",[283],"watch our YouTube video comparing AI, Machine Learning, Deep Learning, and Generative AI",[21,331,332,333],{},"If you want to understand what is happening under the hood,\n",[279,334,337],{"href":335,"rel":336},"https://guidely.tech/guides/neural-networks/",[283],"our Neural Networks series breaks things down step by step",{"title":339,"searchDepth":340,"depth":340,"links":341},"",2,[342,343,344],{"id":62,"depth":340,"text":63},{"id":294,"depth":340,"text":295},{"id":307,"depth":340,"text":308},null,"2026-04-19","A clear, beginner-friendly guide to Retrieval-Augmented Generation (RAG). Learn how it works, why it matters, and how to build AI systems that answer using your own data instead of guessing.","md","/images/blogs/rag/rag-header.png",{},true,"/blog/retrieval-augmented-generation-a-simple-guide-with-analogies","patrick-fleith",{"title":5,"description":347},"retrieval-augmented-generation-a-simple-guide-with-analogies","latest","blog/retrieval-augmented-generation-a-simple-guide-with-analogies","DMdGq6VHI5oafHX7NCUJQJvmAPLtbMvodOo8EHTha4E",{"id":360,"authors":361,"extension":348,"meta":370,"stem":377,"__hash__":378},"authors/authors.md",[362,366],{"name":363,"slug":6,"bio":364,"image":365},"Nyior Clement","Nyior Clement is a self-taught AI engineer with a strong background in software engineering and developer education. He loves taking complex ideas, understanding them deeply, and explaining them in a way anyone can follow. He’s now building Guidely, a community helping people from all backgrounds learn and grow with AI.","/images/authors/nyior_clement.jpg",{"name":367,"slug":353,"bio":368,"image":369},"Patrick Fleith","Patrick Fleith is a Freelance Senior AI Engineer. Right now he builds AI applications to improve space mission design and operations. His expertise lies in large language models and time series analysis. Patrick contributes to the community by creating AI-ready datasets. He is also the creator of datafast, an open-source Python library for synthetic dataset generation.","/images/authors/patrick_fleith.png",{"path":371,"title":372,"description":339,"body":373},"/authors","Authors",{"type":8,"value":374,"toc":375},[],{"title":339,"searchDepth":340,"depth":340,"links":376},[],"authors","dLvyBuVmmT1YPL_LXOzKeNK-CQ_ayU9JxtPI_z7YXm8",1776643897377]