{"id":196805,"date":"2026-05-07T10:33:30","date_gmt":"2026-05-07T15:33:30","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=196805"},"modified":"2026-05-07T10:33:30","modified_gmt":"2026-05-07T15:33:30","slug":"how-does-ai-get-its-information","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/how-does-ai-get-its-information\/","title":{"rendered":"How Does AI Get Its Information? Training Data, RAG, MCPs, and APIs Explained"},"content":{"rendered":"<div class=\"intro-txt\">AI gets its knowledge from three distinct layers: training data, retrieval systems, and live tool access like APIs and&nbsp;MCPs.<\/div>\n<p>Each data layer has its own pros and cons, so if you\u2019ve ever wondered why an AI confidently told you something wrong, why one tool seems to know about last week\u2019s news and another doesn\u2019t, or why your competitor\u2019s product gets mentioned tons while yours doesn\u2019t, the answer almost always traces back to which layer answered your question.[\/intro_text]\n<\/p><p>This article is a plain-English explanation of where AI knowledge actually comes from\u2014and why that matters for how much you should trust any given response.<\/p>\n<div class=\"intro-tok\" id=\"intro_tok\" style=\"display:none;\"><div class=\"intro-title\">Contents<\/div><a href=\"#\" class=\"expand-dots\"><span><\/span><span><\/span><span><\/span><\/a><\/div>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"Training data\" data-section=\"the-training-data-that-teaches-ai-what-it-knows\">\n<h2>Training data: the massive dataset that teaches AI what it&nbsp;knows<\/h2>\n<\/div><\/div>\n<p>Before an AI model ever answers a single question, it goes through a phase called training.<\/p>\n<p>During training, the model ingests billions of text, image, and code examples\u2014public web crawls, books, Wikipedia, code repositories, licensed databases \u2014and learns to predict patterns across all of it. By the time training ends, the model has effectively memorized a statistical snapshot of human knowledge up to that&nbsp;point.<\/p>\n<div id=\"attachment_196806\" style=\"width: 1410px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-196806\" class=\"wp-image-196806\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-1.jpg\" alt width=\"1400\" height=\"1074\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-1.jpg 1400w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-1-554x425.jpg 554w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-1-768x589.jpg 768w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\"><p id=\"caption-attachment-196806\" class=\"wp-caption-text\">A visualisation of common data sources used in training large language models.<\/p><\/div>\n<p>This is how AI models develop their \u201cunderstanding\u201d of the world. The occurrence of different entities in the training data (like your brand name, or your products: think \u201cPatagonia\u201d or \u201cNanopuff Hoody\u201d), and the words they commonly co-occur with (like \u201cenvironmentally-friendly\u201d or \u201chigh quality\u201d), shapes the model\u2019s understanding of your&nbsp;brand.<\/p>\n<p>As Gianluca Fiorelli explains:<\/p>\n<blockquote class=\"small\"><div class=\"quote-content\">\n<p>LLMs learn the relationships between your brand and concepts like \u2018gym\u2019 or \u2018noise-cancellation.\u2019 These semantic associations directly influence whether and how you\u2019re mentioned.<\/p>\n<\/div><div class=\"quote-info clearfix\"><div class=\"quote-photo\"><img decoding=\"async\" alt=\"Gianluca Fiorelli\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/Gianluca-Fiorelli.jpg\"><\/div><div class=\"extra-box\"><span class=\"quote-author\">Gianluca Fiorelli,<\/span> <span class=\"quote-author-job\">Strategic and International SEO Consultant<\/span><\/div><\/div><\/blockquote>\n<p>The scale involved in training is almost hard to picture. Training data for major models is measured in trillions of tokens (roughly, word-chunks). The costs give you a sense of what that requires: training GPT-4 cost an estimated <a href=\"https:\/\/juma.ai\/blog\/how-much-did-it-cost-to-train-gpt-4\">$78 million<\/a>; Google\u2019s Gemini Ultra cost around <a href=\"https:\/\/hai.stanford.edu\/ai-index\/2024-ai-index-report\">$191 million<\/a>.<\/p>\n<p>The global market for AI training datasets was <a href=\"https:\/\/www.grandviewresearch.com\/industry-analysis\/ai-training-dataset-market\">$3.2 billion in 2025, <\/a>and it\u2019s projected to hit $16.3 billion by 2033\u2014a 22.6% annual growth rate that reflects how central data has become to the whole enterprise.<\/p>\n<p>Here\u2019s the critical thing to understand: once training ends, the model\u2019s knowledge is frozen. It can\u2019t learn from new events. It has no idea what happened yesterday, or last month, or after whatever date its training data was cut&nbsp;off.<\/p>\n<p>Some providers periodically fine-tune their models on newer data, but that\u2019s still a discrete process\u2014more like issuing a software update than continuously reading the&nbsp;news.<\/p>\n<p>The other major failure mode is hallucination. When a model doesn\u2019t have reliable training data to draw on, it fills the gap with something plausible-sounding\u2014a fabricated citation, a made-up statistic, a confident non-answer (like Google\u2019s AI Overview citing an <a href=\"https:\/\/www.howtogeek.com\/ai-has-a-new-obstacle-april-fools-day\/\">April Fool\u2019s satire article as a factual source<\/a>).<\/p>\n<p>The model had no way to know the article was a joke; it just looked authoritative enough to fit the pattern.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"Grounding and RAG\" data-section=\"how-rag-and-grounding-give-ai-access-to-current-information\">\n<h2>Grounding: How RAG gives AI access to current information<\/h2>\n<\/div><\/div>\n<p>Retrieval-Augmented Generation (RAG) is the main technique used to work around the knowledge cutoff problem.<\/p>\n<p>Instead of relying purely on what the model learned during training, RAG lets the model pull in relevant documents at the moment a question is asked, then use those documents as context when generating a response.<\/p>\n<p>Think of it as the difference between a closed-book exam and an open-book one. A training-only model has to answer from memory. A RAG-enabled model can look things up first, then answer. The result is more current and, in principle, more verifiable, because the answer is grounded in actual retrieved content rather than statistical pattern-matching.<\/p>\n<div id=\"attachment_196807\" style=\"width: 1690px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-196807\" class=\"wp-image-196807\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-2.jpg\" alt width=\"1680\" height=\"920\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-2.jpg 1680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-2-680x372.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-2-768x421.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-2-1536x841.jpg 1536w\" sizes=\"auto, (max-width: 1680px) 100vw, 1680px\"><p id=\"caption-attachment-196807\" class=\"wp-caption-text\">Retrieval augmented generation visualised.<\/p><\/div>\n<p>\u201cGrounding\u201d is the broader term for this anchoring. When an AI answer is grounded, it\u2019s tethered to specific retrieved sources, which dramatically reduces the hallucination risk.<\/p>\n<p>As Britney Muller explains:<\/p>\n<blockquote class=\"small\"><div class=\"quote-content\">\n<p>Grounding comes from ground truth, rooted in statistics and originally cartography, where it literally meant going outside to verify that your map matched reality.<\/p>\n<\/div><div class=\"quote-info clearfix\"><div class=\"quote-photo\"><img decoding=\"async\" alt=\"Britney Muller\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2024\/02\/1651016231331.jpeg\"><\/div><div class=\"extra-box\"><span class=\"quote-author\">Britney Muller,<\/span> <span class=\"quote-author-job\">SEO + ML Consultant<\/span><\/div><\/div><\/blockquote>\n<p>AI search engines like ChatGPT and Gemini use traditional search indexes like Google and Bing for this grounding process. That\u2019s why good SEO, and ranking highly in traditional search, will also improve your AI visibility. The higher you appear in the search index for the term the AI searches for, the higher your chance of being retrieved and cite din the answer.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"2048\" height=\"1344\" class=\"wp-image-196808\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-3.png\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-3.png 2048w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-3-648x425.png 648w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-3-768x504.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-3-1536x1008.png 1536w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\"><\/p>\n<p>Not every AI product uses RAG. A base ChatGPT session with browsing disabled, for example, is purely training-based: it has no access to current information and no way to verify its answers against live sources.<\/p>\n<p>The tradeoff is speed and simplicity. Training-only responses are fast, but they\u2019re permanently dated. RAG adds latency and introduces a new failure mode (retrieval errors\u2014pulling in the wrong source, or a poor-quality one), but it makes recency possible.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"MCPs, APIs, and agents\" data-section=\"how-ai-agents-and-tools-extend-what-a-model-can-access-in-real-time\">\n<h2>MCPs and APIs: <a id=\"post-196805-bookmark=id.scyfh54t9tvo\"><\/a>How AI agents and tools extend what a model can access in real&nbsp;time<\/h2>\n<\/div><\/div>\n<p>RAG is one way to get fresh information into an AI response. But modern AI systems are increasingly going further, giving models the ability to call external tools mid-conversation. This is the territory of AI agents.<\/p>\n<p>An AI agent doesn\u2019t just retrieve documents; it can query APIs, run searches, execute code, and interact with live data sources as part of working through a&nbsp;task.<\/p>\n<div id=\"attachment_196809\" style=\"width: 1482px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-196809\" class=\"wp-image-196809\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-4.png\" alt width=\"1472\" height=\"796\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-4.png 1472w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-4-680x368.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-4-768x415.png 768w\" sizes=\"auto, (max-width: 1472px) 100vw, 1472px\"><p id=\"caption-attachment-196809\" class=\"wp-caption-text\">A comparison of using generative AI versus agentic AI.<\/p><\/div>\n<div class=\"further-reading\"><div class=\"reading-title\">Further reading<\/div><div class=\"reading-content\">\n<ul>\n<li><a href=\"http:\/\/r\">Agentic AI vs. Generative AI: What\u2019s the Difference?<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n<p>The emerging infrastructure for this is called <a href=\"https:\/\/ahrefs.com\/blog\/what-is-mcp-server\/\">Model Context Protocol (MCP)<\/a>, a standard that lets AI models connect to external data sources in a structured way.<\/p>\n<p>A concrete example: Ahrefs has an <a href=\"https:\/\/ahrefs.com\/mcp\/\">MCP integration<\/a> that lets AI agents query Ahrefs data directly during a task, pulling keyword metrics, backlink data, or competitive insights without the user leaving their workflow.<\/p>\n<div id=\"attachment_196810\" style=\"width: 1630px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-196810\" class=\"wp-image-196810\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5.png\" alt width=\"1620\" height=\"1632\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5.png 1620w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5-422x425.png 422w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5-768x774.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5-1525x1536.png 1525w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/word-image-196805-5-120x120.png 120w\" sizes=\"auto, (max-width: 1620px) 100vw, 1620px\"><p id=\"caption-attachment-196810\" class=\"wp-caption-text\">An example of getting keyword data using the Ahrefs MCP in Claude.<\/p><\/div>\n<div class=\"recommendation\"><div class=\"recommendation-title\">Try Agent A&nbsp;now<\/div><div class=\"recommendation-content\">\n<p>Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/agent-a\">Agent A<\/a> takes this further. It\u2019s a marketing AI with direct, unlimited access to Ahrefs\u2019 full internal dataset: keyword data, site metrics, competitive intelligence, the&nbsp;works.<\/p>\n<p>Rather than an AI that has to approximate SEO insights from training data (which goes stale) or retrieve them from public sources (which are incomplete), Agent A works from the actual data.<\/p>\n<p>For marketing and SEO tasks specifically, that\u2019s a huge difference: Agent A can tackle many SEO and marketing workflows, without any hand-holding.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-196795\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research.jpg\" alt width=\"4054\" height=\"2126\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research.jpg 4054w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research-680x357.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research-768x403.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research-1536x806.jpg 1536w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/agent-a-keyword-research-2048x1074.jpg 2048w\" sizes=\"auto, (max-width: 4054px) 100vw, 4054px\"><\/p>\n<\/div><\/div>\n<p>The broader principle is that tool-augmented AI is only as reliable as the tools it calls. If the API returns bad data, the AI produces a bad answer, confidently. The intelligence of the model doesn\u2019t save you from garbage inputs. What it does do is extend the model\u2019s reach far beyond what any training dataset could&nbsp;cover.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"What this means for brands that want AI to find them\" data-section=\"what-this-means-for-brands-that-want-ai-to-find-and-trust-them\">\n<h2>What this means for brands that want AI to find\u2014and trust\u2014them<\/h2>\n<\/div><\/div>\n<p>When you understand where AI gets its information from, you understand where your brand needs to show-up to stand the best chance of being&nbsp;cited:<\/p>\n<ul>\n<li><strong>Off-site mentions.<\/strong> If you want AI to accurately represent your brand, the starting point isn\u2019t your website\u2014it\u2019s <a href=\"https:\/\/ahrefs.com\/blog\/brand-mentions\/\">off-site mentions.<\/a> Models learn about brands from the sources they trained on: press coverage, third-party reviews, forum discussions, Wikipedia entries, and citations in authoritative publications. A brand that exists only on its own domain is largely invisible to the model\u2019s training data.<\/li>\n<li><strong>Query fan-out.<\/strong> Beyond brand recognition, you need to think about <a href=\"https:\/\/ahrefs.com\/blog\/query-fan-out\/\">query fan-out<\/a>, the adjacent questions AI systems generate around a core topic. A brand ranking for \u201cproject management software\u201d should also be targeting content like \u201chow to run a sprint review\u201d or \u201cagile vs. waterfall,\u201d because those are the questions an AI system will surface when a user follows up on the initial query. Creating content that covers the full semantic neighborhood around your core topics increases the chances you appear in that expansion.<\/li>\n<li><strong>AI accessibility.<\/strong> <a href=\"https:\/\/ahrefs.com\/blog\/search-engine-ai-seo-bot-crawling\/\">Technical accessibility still matters<\/a>, too. Clean HTML, fast load times, and a well-configured robots.txt file affect whether AI crawlers can read your content at all. <a href=\"https:\/\/ahrefs.com\/blog\/what-is-llms-txt\/\">llms.txt<\/a> is a proposed standard for helping LLMs navigate your site\u2019s structure, but as of 2026 no major LLM provider has confirmed they respect it (so don\u2019t waste your&nbsp;time).<\/li>\n<\/ul>\n<div class=\"recommendation\"><div class=\"recommendation-title\">Start tracking AI visibility with Brand&nbsp;Radar<\/div><div class=\"recommendation-content\">\n<p>To measure how this is working in practice, Ahrefs\u2019 <a href=\"https:\/\/ahrefs.com\/brand-radar\">Brand Radar<\/a> tracks AI share of voice across ChatGPT, Gemini, Perplexity, AI Overviews, AI Model Grok, and many more, showing how often your brand is mentioned in AI-generated responses relative to competitors. <a href=\"https:\/\/ahrefs.com\/blog\/brand-radar-use-cases\/\">Read this article to learn how it&nbsp;works.<\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-196819\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar.jpg\" alt width=\"3542\" height=\"1982\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar.jpg 3542w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar-680x381.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar-768x430.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar-1536x860.jpg 1536w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2026\/05\/Brand-Radar-2048x1146.jpg 2048w\" sizes=\"auto, (max-width: 3542px) 100vw, 3542px\"><\/p>\n<\/div><\/div>\n<h2>Final thoughts<\/h2>\n<p>AI knowledge comes from three layers: frozen training data, retrieved live documents, and connected external tools, like APIs and MCPs. Each has a different accuracy profile, a different relationship with recency, and a different way of failing.<\/p>\n<p>Training data is the foundation\u2014vast, expensive, and static. RAG and grounding add currency at the cost of retrieval reliability. Tool integrations like Ahrefs\u2019 MCP and purpose-built agents like Agent A extend that further, giving AI access to live, authoritative data at the moment it\u2019s needed.<\/p>\n<p>For a deeper look at how AI search engines stitch these layers together to generate answers, <a href=\"https:\/\/ahrefs.com\/seo\/how-ai-search-engines-work\">check out our guide to how AI search engines work<\/a>.<\/p>\n<div class=\"further-reading\"><div class=\"reading-title\">Further reading<\/div><div class=\"reading-content\">\n<ul>\n<li><a href=\"https:\/\/ahrefs.com\/seo\/how-ai-search-engines-work\/\">How AI Search Engines Work<\/a><\/li>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/agentic-ai-vs-generative-ai\/\">Agentic AI vs. Generative AI: What\u2019s the Difference, and Why Does It Matter?<\/a><\/li>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/brand-mentions\/\">How to Monitor and Win Brand Mentions in AI Answers<\/a><\/li>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/brand-radar-use-cases\/\">10 Ways to Use Ahrefs\u2019 Brand Radar to Grow AI Visibility<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Each data layer has its own pros and cons, so if you\u2019ve ever wondered why an AI confidently told you something wrong, why one tool seems to know about last week\u2019s news and another doesn\u2019t, or why your competitor\u2019s product<span class=\"ellipsis\">\u2026<\/span><\/p>\n<div class=\"read-more\">Read more \u203a<\/div>\n<p><!-- end of .read-more --><\/p>\n","protected":false},"author":194,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[469],"tags":[],"coauthors":[457],"class_list":["post-196805","post","type-post","status-publish","format-standard","hentry","category-ai-search","odd"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How Does AI Get Its Information? 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