{"id":615,"date":"2026-03-22T16:00:00","date_gmt":"2026-03-22T20:00:00","guid":{"rendered":"https:\/\/laeka.org\/blog\/archives\/615"},"modified":"2026-03-23T11:50:56","modified_gmt":"2026-03-23T15:50:56","slug":"why-ai-says-nonsense","status":"publish","type":"post","link":"https:\/\/laeka.org\/blog\/why-ai-says-nonsense\/","title":{"rendered":"Why AI Sometimes Says Nonsense (And That&#8217;s Normal)"},"content":{"rendered":"<p>You&#8217;ve probably seen it happen. You ask ChatGPT a question and it answers with total confidence&#8230; except the answer is completely wrong. It invents a book that doesn&#8217;t exist. It cites a fictional scientific paper. It throws statistics out of thin air.<\/p>\n<p>And the worst part? It looks <strong>absolutely sure of itself<\/strong>.<\/p>\n<p>This is called a hallucination. And it&#8217;s not a bug. It&#8217;s a direct consequence of how AI works.<\/p>\n<h2>AI is a sentence-completion machine<\/h2>\n<p>ChatGPT generates text one word at a time, choosing the most probable word after the previous one. It&#8217;s a giant autocomplete. It has no &#8220;fact database&#8221; to consult. There&#8217;s no little librarian inside checking sources.<\/p>\n<p>When you ask &#8220;What&#8217;s the tallest bridge in Quebec?&#8221;, it&#8217;s not looking up the answer in an encyclopedia. It generates the sequence of words that <strong>most resembles<\/strong> a good answer, based on everything it read during training.<\/p>\n<p>Most of the time, that gives the right answer. But when the data is fuzzy, contradictory, or simply absent&#8230; it makes things up. With confidence. Because it doesn&#8217;t have the concept of &#8220;I don&#8217;t know.&#8221;<\/p>\n<h2>It&#8217;s like a professional storyteller<\/h2>\n<p>Imagine a storyteller who&#8217;s read every book in the world. You ask them to tell you the story of the Battle of Ch\u00e2teauguay. If they&#8217;ve read about it often, they&#8217;ll tell it well. But if you ask for the story of the Battle of Saint-R\u00e9mi-de-Napierville (which doesn&#8217;t exist), they&#8217;ll still tell you a story. With dates, names, details. Because that&#8217;s what they do: they tell stories.<\/p>\n<p>AI can&#8217;t tell the difference between telling something true and telling something plausible. For AI, it&#8217;s the <strong>same process<\/strong>.<\/p>\n<h2>The most common hallucinations<\/h2>\n<p>Fake citations are a classic. Ask ChatGPT to find 5 studies on a niche topic and it&#8217;ll give you 5, with titles, authors, and dates. Except 2 or 3 will be completely made up. The authors exist, but the paper doesn&#8217;t.<\/p>\n<p>Fake historical facts are common too. AI will mix up events, invent dates, or combine two different stories into one. It&#8217;s like a student who crammed too hard the night before the exam and is mixing everything up.<\/p>\n<p>And math. AI is surprisingly bad at basic arithmetic. It&#8217;ll sometimes tell you that 37 x 14 = 498 (the right answer is 518) because it &#8220;predicts&#8221; the result instead of calculating it.<\/p>\n<h2>How to protect yourself<\/h2>\n<p>The golden rule: <strong>verify everything factual<\/strong>. If ChatGPT gives you a fact, a date, a statistic, a name \u2014 check it with a reliable source. Treat AI as a first draft, never as a final source.<\/p>\n<p>For creative tasks \u2014 brainstorming, writing, rephrasing \u2014 hallucinations are rarely a problem because you&#8217;re not looking for facts. You&#8217;re generating ideas.<\/p>\n<p>The trick is knowing <strong>when<\/strong> to trust and when to verify. That&#8217;s exactly what we teach in <a href='https:\/\/sherpa.live'>Sherpa<\/a>, our free AI guide. Because AI talking nonsense is normal. But you swallowing it without checking \u2014 that&#8217;s a problem we can fix.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>You&#8217;ve probably seen it happen. You ask ChatGPT a question and it answers with total confidence&#8230; except the answer is&#8230;<\/p>\n","protected":false},"author":1,"featured_media":15,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[190],"tags":[],"class_list":["post-615","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-understanding-ai"],"_links":{"self":[{"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/posts\/615","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/comments?post=615"}],"version-history":[{"count":1,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/posts\/615\/revisions"}],"predecessor-version":[{"id":699,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/posts\/615\/revisions\/699"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/media\/15"}],"wp:attachment":[{"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/media?parent=615"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/categories?post=615"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laeka.org\/blog\/wp-json\/wp\/v2\/tags?post=615"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}