University of Kwazulu-Natal

Ai Hallucinations Why They Happen And How To Stop Them

Bonisiwe Shabane
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ai hallucinations why they happen and how to stop them

Stravito CTO Jonas Martinsson breaks down why AI hallucinations happen and how you can stop them in this in-depth Q&A. LLMs hallucinate because they're pattern-matching machines, not knowledge repositories. They predict text based on statistical patterns rather than retrieving verified facts. When uncertain, they can generate coherent completions instead of admitting ignorance. Their training data compounds this problem. Fiction mixes with fact, and contradictory sources offer different truths.

Without fact-checking mechanisms, models blend these contradictions into convincing but potentially false compromises. In longer contexts, attention mechanism limitations mean they can 'lose track' of earlier information, fabricating details that seem consistent but weren't actually present. Context and consequence define the line. Creative outputs are valuable when imagination is the goal: brainstorming campaign themes, generating product names, or exploring "what if" scenarios. Harmful hallucinations occur when AI fabricates information in contexts requiring truth, such as market sizing, customer feedback, or competitive intelligence. In the age of intelligent machines, artificial intelligence is transforming everything—from how we write and research to how we diagnose disease, navigate cities, and interact with the digital world.

These systems, trained on oceans of data, seem to possess an almost magical ability to understand language, recognize images, and solve complex problems. But lurking behind the polished facade of modern AI is a strange and sometimes unsettling phenomenon: hallucinations. No, AI doesn’t dream in the human sense. But it can fabricate. It can make things up—confidently and convincingly. In the world of artificial intelligence, a hallucination refers to when an AI model generates information that is not true, not supported by any data, or entirely fictional.

These “hallucinations” may take the form of fake facts, invented quotes, incorrect citations, or completely fabricated people, places, or events. Sometimes they’re harmless. Sometimes they’re dangerous. Always, they raise important questions about how much we can—or should—trust intelligent machines. In this expansive exploration, we’ll journey deep into the fascinating world of AI hallucinations. What exactly are they?

Why do they happen? Can they be controlled—or even eliminated? And what do they reveal about the limits of artificial intelligence and the nature of intelligence itself? To understand AI hallucinations, we must first appreciate how modern AI works—especially large language models (LLMs) like ChatGPT, GPT-4, Claude, or Google Gemini. These models don’t “know” things in the way humans do. They don’t have beliefs, awareness, or access to a concrete database of verified facts.

Instead, they are trained to predict the next word or token in a sentence based on statistical patterns in vast amounts of text data. An AI hallucination occurs when the model produces a response that sounds plausible but is factually incorrect, logically flawed, or completely invented. This could be something as simple as inventing a fake academic paper title or something more complex like citing a legal case that never existed. AI hallucinations are outputs generated by large language models (LLMs) that appear factual and coherent but contain false or fabricated information. Unlike human hallucinations, these aren't perceptual errors—they're confidence failures where models generate plausible-sounding responses without factual grounding. The term was popularized in the AI research community around 2019-2020, but became a critical concern with the deployment of large models like GPT-3 and GPT-4.

Studies show that even state-of-the-art models like GPT-4 hallucinate in 15-20% of factual queries (Ji et al., 2023), making this the primary reliability challenge in production AI systems. What makes hallucinations particularly dangerous is their convincing nature. Models don't typically say 'I don't know'—instead, they confidently generate false facts, fake citations, or entirely fabricated events. This is why understanding AI safety and alignment has become crucial for enterprise deployments. Source: Ji et al., 2023 - Survey of Hallucination in Natural Language Generation Understanding why hallucinations occur requires examining how transformer architectures fundamentally work.

Unlike traditional databases that return 'no result found', neural networks always generate the most probable next token based on training patterns, even when they lack relevant knowledge. AI tools like ChatGPT, Gemini, and Claude are transforming how we work, learn, and create. But sometimes, they get things very wrong—confidently stating false facts, making up events, or even inventing entirely fake sources. This phenomenon is called AI hallucination, and it’s one of the biggest challenges in artificial intelligence today. This article is part of a series demystifying Gen AI and its applications for the world of work. You can read the earlier articles – a basic guide to LLMs, prompt engineering vs context engineering, AI bots vs agents, intro to RLHF and how Gen AI models are trained and about how...

Imagine asking a friend for a restaurant recommendation, and instead of suggesting a real place, they invent one—complete with a fake menu and glowing (but imaginary) reviews. That’s essentially what AI hallucination is: Artificial Intelligence (AI), particularly Generative AI (GenAI), has redefined how organizations process information, automate workflows, and deliver personalized experiences. Large Language Models (LLMs) like OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude are now capable of writing articles, generating code, assisting in decision-making, and even acting as autonomous AI agents. However, despite their intelligence, AI systems are prone to “hallucinations”—instances where the AI produces inaccurate, nonsensical, or entirely fabricated information with high confidence. These errors can range from minor factual mistakes to major misinformation, leading to business risks, legal challenges, and loss of user trust.

This article explains what AI hallucinations are, why they occur, their real-world consequences, and proven strategies enterprises can implement to minimize or prevent them. Read more: Future Trends in Generative AI An AI hallucination occurs when a generative AI model produces output that appears plausible but is factually incorrect, misleading, or fabricated. Hallucinations can happen in text, images, audio, or even code generated by AI systems. Agentic AI systems are rapidly moving from experiments to real operational tools, powering healthcare triage assistants, legal intake agents, industrial diagnostics, customer support automation, and more. As these systems become more autonomous and start executing multi-step workflows, one concern keeps surfacing across online discussions and expert forums: hallucinations.

Hallucinations occur when an AI system produces information that is incorrect, invented, or not supported by its underlying data or tools. In traditional “single-answer” AI models, hallucinations are inconvenient. But in agentic AI, where the system can take actions, maintain memory, and trigger downstream workflows, they can cascade into operational, financial, or regulatory risks. The good news is that hallucinations are not mysterious or unpredictable. They arise from identifiable failure points: poor grounding, missing data, unclear autonomy boundaries, or weak architectural controls. With the right safeguards, AI engineers can design agentic systems that systematically prevent, detect, and contain hallucinations before they reach the real world.This checklist gives teams a practical way to understand where hallucinations originate...

Hallucinations in AI occur when a model generates information that is factually incorrect, fabricated, unsupported by data, or inconsistent with the real-world context in which the system is operating. In other words, the AI appears confident, but the answer is wrong. In traditional generative AI (e.g., chatbots or summarisation models), hallucinations typically show up as incorrect facts, invented citations, or plausible-sounding but false explanations. These mistakes usually stem from how large language models predict text: they generate the most statistically likely sequence of words, not the most verified or truth-checked output. You don't need to use generative AI for long before encountering one of its major weaknesses: hallucination. Hallucinations occur when a large language model generates false or nonsensical information.

With the current state of LLM technology, it doesn't appear possible to eliminate hallucinations entirely. However, certain strategies can reduce the risk of hallucinations and minimize their effects when they do occur. To address the hallucination problem, start by understanding what causes LLMs to hallucinate, then learn practical techniques to mitigate those issues. An LLM hallucination is any output from an LLM that is false, misleading or contextually inappropriate. Most LLMs can produce different types of hallucinations. Common examples include the following:

AI is transforming how the firms automate processes, assist the customers and derive insights. Yet even the most advanced AI systems occasionally generate information that is incorrect, fabricated, or not grounded in real data. These errors are known as AI hallucinations that represent one of the most critical challenges in enterprise AI adoption. This article provides a technical yet accessible overview of AI hallucinations: what they are, why they occur, and how organisations can systematically reduce them. AI hallucinations occur when a model produces factually incorrect or invented content while presenting it confidently and coherently. Basically the data which is not based on facts.

Hallucinations arise because LLMs generate probabilistic text and not verified truth. They predict the next likely word based on patterns and not factual accuracy. AI hallucinations stem from the inherent design of large language models and the data they are trained on. Below are the most validated reasons behind hallucinations: Artificial intelligence fuels diverse applications like automated conversational agents, along with tools designed for generating content. Even now, the year 2025 artificial intelligence-generated errors pose a significant challenge for these generative systems.

A phenomenon termed AI hallucinations presents itself when a model produces information presented with strong assurance yet devoid of truth. This includes invented details, inaccurate citations, or illogical assertions easily accepted by individuals relying on the artificial intelligence’s output. For the mature individual dependent upon artificial intelligence in areas like investigation, client assistance, or strategic planning, vigilance concerning AI-generated inaccuracies becomes paramount. Protecting one’s professional reputation and circumventing financial missteps requires diligent attention. The ability to identify and mitigate these computational errors is therefore important. Avoiding untrustworthy data ensures sound judgment.

Artificial intelligence models such as GPT4, Claude 3, and Bard create text. They achieve this by anticipating the subsequent word within a given series. This process relies upon extensive datasets procured from the internet. However, these datasets present challenges. They incorporate inaccuracies, biases, and omissions. A model generates a hallucination when it embellishes information or extrapolates beyond its established parameters.

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