{"id":111372,"date":"2024-06-27T07:58:25","date_gmt":"2024-06-27T14:58:25","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=111372"},"modified":"2025-12-11T12:25:27","modified_gmt":"2025-12-11T20:25:27","slug":"ai-101-why-rag-is-all-the-rage","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/ai-101-why-rag-is-all-the-rage\/","title":{"rendered":"AI 101: Why RAG Is All the RAGe"},"content":{"rendered":"<div class=\"wp-block-image\">\r\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1440\" height=\"820\" class=\"wp-image-111376\" src=\"https:\/\/www.backblaze.com\/blog\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG-1.png\" alt=\"A decorative image showing an AI chip connecting icons of representing different files.\" srcset=\"https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG-1.png 1440w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG-1-300x171.png 300w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG-1-1024x583.png 1024w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG-1-768x437.png 768w\" sizes=\"auto, (max-width: 1440px) 100vw, 1440px\" \/><\/figure>\r\n<\/div>\r\n\r\n\r\n<div class=\"wp-block-spacer\" style=\"height: 15px;\" aria-hidden=\"true\">\u00a0<\/div>\r\n\r\n\r\n\r\n<p>At the risk of being called the stick in the mud of the tech world, we here at Backblaze have often bemoaned our industry\u2019s love of <a href=\"https:\/\/www.backblaze.com\/blog\/anything-as-a-service-all-the-as-a-service-acronyms-you-didnt-know-you-needed\/\" target=\"_blank\" rel=\"noreferrer noopener\">making up new acronyms<\/a>. The most recent culprit, hailing from the fast-moving artificial intelligence\/machine learning (AI\/ML) space, is truly memorable: RAG, aka retrieval-augmented generation. For the record, <a href=\"https:\/\/blogs.nvidia.com\/blog\/what-is-retrieval-augmented-generation\/\" target=\"_blank\" rel=\"noreferrer noopener\">its creator has apologized<\/a> for inflicting it upon the world.<\/p>\r\n\r\n\r\n\r\n<p>Given how useful it is, we\u2019re willing to forgive. (I\u2019m sure he was holding his breath for that news.) Today, our AI 101 series is back to talk about what RAG is\u2014and the big problem it solves.\u00a0<\/p>\r\n\r\n\r\n\r\n<div class=\"abstract\" style=\"line-height: 1.8; margin: 24px 12px; padding: 24px 12px 10px 12px;\">\r\n<h4>Read more AI 101<\/h4>\r\n<p>This article is part of a series that attempts to understand the evolving world of AI\/ML. Check out our previous articles for more context:<\/p>\r\n<ul>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-how-cognitive-science-and-computer-processors-create-artificial-intelligence\/\" target=\"_blank\" rel=\"noopener\">How Cognitive Science and Computer Processors Create Artificial Intelligence<\/a><\/li>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-gpu-vs-tpu-vs-npu\/\" target=\"_blank\" rel=\"noopener\">GPU vs. TPU vs. NPU<\/a><\/li>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-do-the-dollars-make-sense\/\" target=\"_blank\" rel=\"noopener\">Do the Dollars Make Sense?<\/a><\/li>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/your-ai-toolbox-16-must-have-products\/\" target=\"_blank\" rel=\"noopener\">Your AI Toolbox: 16 Must-Have Products<\/a><\/li>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-training-vs-inference\/\" target=\"_blank\" rel=\"noopener\">Training vs. Inference<\/a><\/li>\r\n<li><a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-what-is-model-serving\/\" target=\"_blank\" rel=\"noopener\">What Is Model Serving?<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\">Let\u2019s start with large language models (LLMs)<\/h2>\r\n\r\n\r\n\r\n<p>LLMs are the most recognizable <a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-how-cognitive-science-and-computer-processors-create-artificial-intelligence\/\" target=\"_blank\" rel=\"noreferrer noopener\">expression of AI<\/a> in our current zeitgeist. (Arguably, you could append that with \u201cthat we\u2019re all paying attention to,\u201d given that ML algorithms have been behind many tools for decades now.) LLMs underpin tools like ChatGPT, Google Gemini, and Claude, as well as things like service-oriented chatbots, natural language processing tasks, and so on. They\u2019re trained on vast amounts of data with algorithmic guardrails known as parameters and hyperparameters guiding their training. Once trained, we query them through a process known as <a href=\"https:\/\/www.backblaze.com\/blog\/ai-101-training-vs-inference\/\" target=\"_blank\" rel=\"noreferrer noopener\">inference<\/a>.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>Fabulous! The possibilities are endless. However, one of the biggest challenges we\u2019ve experienced (and laughed about on the internet) is that LLMs can <a href=\"https:\/\/outshift.cisco.com\/blog\/responsible-enterprise-LLMs-llm-bias\" target=\"_blank\" rel=\"noreferrer noopener\">return inaccurate results<\/a>, while sounding very, very reasonable. Additionally, LLMs don\u2019t know what they don\u2019t know. Their answers can only be as good as the data they draw from\u2014so, if their training dataset is outdated or contains a systematic bias, it will impact your results. As AI tools have become more widely adopted, we\u2019ve seen LLM inaccuracies range from \u201c<a href=\"https:\/\/gizmodo.com\/google-search-ai-overview-giant-hallucination-1851499031\" target=\"_blank\" rel=\"noreferrer noopener\">funny and widely mocked<\/a>\u201d to \u201c<a href=\"https:\/\/www.cio.com\/article\/190888\/5-famous-analytics-and-ai-disasters.html\" target=\"_blank\" rel=\"noreferrer noopener\">oh, that\u2019s actually serious.<\/a>\u201d<\/p>\r\n\r\n\r\n<div class=\"wp-block-image\">\r\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d36faf71143&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d36faf71143\" class=\"aligncenter size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"936\" height=\"604\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" class=\"wp-image-111373\" src=\"https:\/\/www.backblaze.com\/blog\/wp-content\/uploads\/2024\/06\/AI-101-RAG_1_Illegal-Cuteness.png\" alt=\"A screenshot of an AI search result claiming that there are illegal levels of cuteness. \" srcset=\"https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_1_Illegal-Cuteness.png 936w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_1_Illegal-Cuteness-300x194.png 300w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_1_Illegal-Cuteness-768x496.png 768w\" sizes=\"auto, (max-width: 936px) 100vw, 936px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button>\r\n<figcaption class=\"wp-element-caption\">Shoutout to Reddit users for finally getting this one into trusted sources.<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n<div class=\"wp-block-spacer\" style=\"height: 15px;\" aria-hidden=\"true\">\u00a0<\/div>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\">Enter retrieval-augmented generation (Fine! RAG)<\/h2>\r\n\r\n\r\n\r\n<p>RAG is a solution to these problems. Instead of relying on <em>only<\/em> an LLM\u2019s dataset, RAG queries external sources before returning a response. It\u2019s more complicated than \u201clet me google that for you,\u201d as the process takes that external data, turns it into a vectored database, and then balances external data with an LLM\u2019s \u201cgeneral knowledge\u201d generated response and skill at responding to conversational queries.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>This has several advantages. Users now have sources they can cite, and recent information is taken into account. From a development perspective, it means that you don\u2019t have to re-train a model as frequently. And, it can be implemented in <a href=\"https:\/\/ai.meta.com\/blog\/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">as few as five lines of code.<\/a>\u00a0<\/p>\r\n\r\n\r\n\r\n<p>One important nuance is that when you\u2019re building RAG into your product, you can set its sources. For industries like medicine and law, that means you can point them towards industry journals and trusted sources, outweighing the often misquoted or mis-cited examples you might see in a general database.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>Another example: For a <a href=\"https:\/\/www.backblaze.com\/docs\" target=\"_blank\" rel=\"noreferrer noopener\">technical documentation portal<\/a>, you can take an LLM, trained on general information and the nuts and bolts of conversational querying, and direct it to rely on your organization\u2019s help articles as its most important sources. Your organization controls the authoritative data, and how often\/when changes are made. Users can trust that they\u2019re getting the most recent security patches and <a href=\"https:\/\/www.theregister.com\/2024\/03\/28\/ai_bots_hallucinate_software_packages\/\" target=\"_blank\" rel=\"noreferrer noopener\">correct code<\/a>. And, you can do so quickly, easily, and\u2014most importantly\u2014cost-effectively.\u00a0<\/p>\r\n\r\n\r\n<div class=\"wp-block-image\">\r\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d36faf719e2&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d36faf719e2\" class=\"aligncenter size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"936\" height=\"554\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" class=\"wp-image-111374\" src=\"https:\/\/www.backblaze.com\/blog\/wp-content\/uploads\/2024\/06\/AI-101-RAG_2_RAG-Diagram.png\" alt=\"A diagram showing how RAG incorporates into the query-process with an LLM, before returning a result to the user. \" srcset=\"https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_2_RAG-Diagram.png 936w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_2_RAG-Diagram-300x178.png 300w, https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/AI-101-RAG_2_RAG-Diagram-768x455.png 768w\" sizes=\"auto, (max-width: 936px) 100vw, 936px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button>\r\n<figcaption class=\"wp-element-caption\"><a href=\"https:\/\/aws.amazon.com\/what-is\/retrieval-augmented-generation\/\" target=\"_blank\" rel=\"noreferrer noopener\">Source.<\/a><\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n<div class=\"wp-block-spacer\" style=\"height: 15px;\" aria-hidden=\"true\">\u00a0<\/div>\r\n\r\n\r\n\r\n<h2 class=\"wp-block-heading\">RAG doesn\u2019t mean foolproof AI<\/h2>\r\n\r\n\r\n\r\n<p>RAG is a great, straightforward method for keeping LLM tools updated with current, high-quality information and giving users more transparency around where their answers are coming from. However, as we mentioned above, AI is only ever as good as the data it uses. Keep in mind, that\u2019s a deceptively simple thing to say. It\u2019s an entire, specialized job to validate datasets, and that expertise is built into the research and monitoring that happens while training an LLM.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>RAG gives a new source of data a privileged position\u2014you\u2019re saying \u201cthis data is more authoritative than that data\u201d and, since the LLM doesn\u2019t have anything in its general database, it may not have a counter argument. If you\u2019re not paying attention to your RAG data source standards, and doing so on an ongoing basis, it\u2019s possible, and even likely, that data bias, low quality data, etc. could creep into your model.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>Think of it this way: If you\u2019re pointing to a new feature in your tech docs and there\u2019s an error, that impact is magnified because an LLM will give more weight to the RAG data. At least in that case, you\u2019re the one who controls the source data. In our other examples of legal or medical AI tools pointing to journal updates, things can get, well, more complicated. If (when) you\u2019re setting up an AI that uses RAG, it\u2019s imperative to make sure you\u2019re also setting yourself up with reliable sources that are regularly updated.\u00a0<\/p>\r\n\r\n\r\n\r\n<p>But, given its impact, and how low of a lift it is to integrate into existing products, we can see why RAG is all the RAGe\u2014and, as always, we look forward to more to come in the AI landscape.\u00a0For now, we can already see the impact it&#8217;s having on the market, with <a href=\"https:\/\/www.databricks.com\/blog\/data-intelligence-and-ai-trends-top-products-rag-and-more\" target=\"_blank\" rel=\"noreferrer noopener\">SaaS companies<\/a> and <a href=\"https:\/\/www.founderoo.co\/resources\/the-top-ai-companies-to-follow-the-companies-to-watch-in-the-world-of-artificial-intelligence\" target=\"_blank\" rel=\"noreferrer noopener\">startups<\/a> alike exploring the possibilities.<\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>Retrieval-augmented generation (RAG). What is it, and why is it such a big deal for generative AI? <\/p>\n","protected":false},"author":182,"featured_media":111375,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[7,434,438],"tags":[489,468],"class_list":["post-111372","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-storage","category-featured-1","category-featured-cloud-storage","tag-ai-ml","tag-b2cloud","entry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is RAG in AI? Understanding Retrieval-Augmented Generation<\/title>\n<meta name=\"description\" content=\"Explore retrieval-augmented generation (RAG) in AI\/ML! 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Learn how it enhances data accuracy, improves responses, and addresses AI limitations.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.backblaze.com\/blog\/ai-101-why-rag-is-all-the-rage\/\" \/>\n<meta property=\"og:site_name\" content=\"Backblaze Blog | Cloud Storage &amp; Cloud Backup\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/backblaze\" \/>\n<meta property=\"article:published_time\" content=\"2024-06-27T14:58:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-11T20:25:27+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/backblazeprod.wpenginepowered.com\/wp-content\/uploads\/2024\/06\/bb-bh-AI-RAG.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1440\" \/>\n\t<meta property=\"og:image:height\" content=\"820\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Stephanie Doyle\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@backblaze\" \/>\n<meta name=\"twitter:site\" content=\"@backblaze\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Stephanie Doyle\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"What is RAG in AI? Understanding Retrieval-Augmented Generation","description":"Explore retrieval-augmented generation (RAG) in AI\/ML! Learn how it enhances data accuracy, improves responses, and addresses AI limitations.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.backblaze.com\/blog\/ai-101-why-rag-is-all-the-rage\/","og_locale":"en_US","og_type":"article","og_title":"What is RAG in AI? Understanding Retrieval-Augmented Generation","og_description":"Explore retrieval-augmented generation (RAG) in AI\/ML! 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