{
  "id": 744309,
  "title": "Understanding LLM Hallucinations: Why AI Lies and How to fix it.",
  "url": "https://urgent.news/2026/08/13/understanding-llm-hallucinations-why-ai-lies-and-how-to-fix-it",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-13T07:35:37.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sangam_shrestha_07/understanding-llm-hallucinations-why-ai-lies-and-how-to-fix-it-bm7"
  },
  "original_language": "en",
  "account": "Artificial intelligence excels at generating code, applications, and answers to intricate queries in mere seconds. However, this capability comes with a troubling flaw: AI frequently lies with unwavering confidence. In the realm of technology, these convincing fictions are known as hallucinations.\n\nSo, why does AI generate falsehoods? Large language models (LLMs) do not possess inherent factual knowledge. Instead, they function based on probabilities.\n\nFirstly, advanced autocomplete is at play. AI predicts the next most probable word rather than presenting factual truths. Secondly, statistical guesswork comes into effect. When crucial data is missing, the model resorts to inventing plausible answers. Lastly, AI is optimized to deliver confident lies instead of admitting uncertainty.\n\nThe implications of AI's propensity for falsehoods in the real world are significant. Security vulnerabilities can arise as AI invents non-existent software packages, potentially inviting malicious exploits. Moreover, the credibility of systems is jeopardized when broken code or false data is shipped, leading to an instantaneous loss of user trust.\n\nTo mitigate these issues, there's no need to abandon AI entirely. Instead, the focus should be on securing its use. One effective strategy is to ground AI within specific source documentation, limiting its ability to provide answers outside of this defined boundary. Another approach is to reduce the temperature parameter of the AI API, effectively decreasing its creative output.\n\nLastly, adopting a human-in-the-loop approach can prove beneficial. AI outputs should be treated as unverified drafts, with a rigorous human review process before any production deployment.",
  "summary": "AI writes code, builds apps, and answers complex questions in seconds. But it has a dark side: it lies to your face with absolute confidence. In tech, these believable fabrications are called hallucinations. Why Does AI Make Things Up? LLMs do not actually \"know\" facts. They operate on probability: ▪︎ Advanced Autocomplete: AI predicts the next most likely word, not factual truth. ▪︎ Statistical…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}