What Are Generative AI Hallucinations and Why Do They Happen? Posted on May 19, 2025May 19, 2025 Generative AI can create high-quality content, but what if GenAI gets biased and skewed? This can be referred to as ‘generative AI hallucinations’, when the AI tool discerns patterns and objects in a way that makes the output generated inaccurate or grotesque for the user. How exactly AI hallucinations take place, why they occur, and how users address them– let’s break it down for you in this article. What Does ‘Hallucination’ Mean in the Context of Generative AI? Hallucination here does not particularly mean seeing imaginary things, but it can be referred to as a situation where generative AI chatbots like ChatGPT or Gemini fabricate information that is incorrect or imaginary. It includes made-up data, historical events, or citing non-existent research. Even though resources may look correct and confident to the human eye, they can still be factually wrong. That’s it; it is suggested to users to always fact-check the output produced by AI tools. These generative AI hallucinations are not intentional. AI can’t perform a fact check or identify if the information is wrong. AI tools are designed to generate content based on the pattern in the training dataset. These hallucinations are a result of overfitting, biased training data, or accuracy and high model complexity. Why Do Generative AI Models Produce Hallucinations? The reason for generative AI hallucinations depends on the training of these AI models. Chatbots like ChatGPT contain a large language model (LLM) that reads massive amounts of data in the form of text or images scraped from the internet. This training helps them to predict the next words in the sentence and understand the sequence and meaning of the sentences, but not to fact-check or provide logical reasoning for the data or input. There can be multiple causes for these hallucinations. Inaccurate Training Data Source – AI models are trained on data that is full of information that can be correct or incorrect as well. It can include cultural or societal biases, and since the models are trained to mimic the patterns of training data, it can create output with the same biases and inaccuracies. Prompt ambiguity – Prompts that are not written correctly can also lead the model to predict blanks with guesses that can often be inaccurate. Overconfidence in generation – Models are trained to produce content with confidence and fluency, even when they are unsure regarding what kind of output they should produce. Lack of senses – Generative AI does not have a built-in truth or reality checker; it just mimics training data. You can notice that what causes AI hallucinations is mostly due to inaccurate training data and AI tools’ technical limitations, which do not give it the ability to perform logical reasoning and sense. When Are AI Hallucinations Most Likely to Occur? Multiple reasons can trigger these generative AI hallucinations. It can be frequent under some conditions, which include: Ambiguous topic – It occurs when the users try to get output on little-known facts or highly specific content which is difficult to understand for the AI tool. Then the model might fabricate pieces of information to generate output. Lack of context – If the prompt does not provide the AI model with enough context, it will try to create plausible output based on limited information. Creative tasks – Inputs that often demand poetry, storytelling, or hypothetical situations can lead to more hallucinations due to their open-ended nature. Long-form generation – When the output takes too long to generate, it can create more possibilities for inaccuracies to seep in. Poorly crafted prompts – This refers to niche or obscure prompts that can include less or overly complex instructions, which can confuse the AI model, increasing the chances of hallucinations in the output. Also Read: In Prompt Engineering, What Are Format, Length, a Audience Examples Of? One ChatGPT hallucination example includes when the users demand a research report with citations, and then the AI model fabricates some citations from a scientific paper that does not even exist. Despite the tone and structure being plausible, the content can still be entirely incorrect. What Are the Risks of AI Hallucinations in Real-World Applications? These hallucinations can sometimes be misleading or inappropriate, depending on the application. Some of the real-world cases include Legal – AI-generated cases or examples regarding legal processes or laws can be fabricated, which can result in misconduct in professional cases. Academic – Students rely on AI to write research reports, but there is a chance that the AI citation can be fake, resulting in false information. Journalism – Using AI-generated news without fact-checking can mislead the public. You can even see some funny AI hallucination examples, like when a chatbot says elephants can fly, which is false information. This shows the misconception and confusion caused by an AI model; that’s why it is always recommended to check before taking these outputs seriously. How Can Developers and Users Minimise Hallucinations? Some strategies can reduce the frequent occurrence of these hallucinations, like – For Developers: Developers can develop a system that takes human feedback during training to improve the system’s accuracy. Since the AI tool is based on training data, the developer must use verified databases or sources. Developers can try to update regularly to reduce outdated information in the AI tools. For Users: Users must provide the AI tools with specific and clear prompts to reduce niche output. After content generation, users can cross-verify the information with other trusted sources. Understanding the need for responsible AI is necessary for users to avoid overreliance on these tools. You can check out our guidelines on prompt engineering tools to learn how to write the perfect prompt to reduce hallucinations. What Are Companies Doing to Reduce Hallucinations in AI? All the major developers and companies that run AI systems and LLMs are working on this problem right now. Here are some updates. Google’s DeepMind is trying to find a way to cite the sources from which the AI takes data while generating output to increase its reliability. Meta is researching a way for AI models to learn from training datasets with both text and visuals to improve their accuracy. OpenAI is also working on creating a model in which users can fact-check the sources from which the AI takes the content for output generation. FAQs What is an example of hallucination in generative AI? Suppose you asked AI to write a research paper on a cosmic event, and AI generated an output with fictional events that never took place or cited some sources that do not exist. These are the most common AI hallucinations. How to avoid hallucinations in generative AI? Provide clear and specific prompts as input, cross-verify the output generated, and developers should regularly update the training data and use verified resources. What is the problem with generative AI? The biggest issue is the deficiency of a system that can check and verify the facts and information generated by AI. No logical reasoning behind the output leads to generative AI hallucinations. Can hallucinations be eliminated with AI? AI hallucinations are one of the biggest limitations of AI models right now, which can be reduced by some precautionary steps, but they can’t be eliminated for now. Are some models less prone to hallucinations than others? Models that are trained under human supervision and provided with a verified dataset for training are more reliable. These AI systems can reduce the occurrence of AI Hallucinations in the output.If you want to learn more about the output generation process, check out our guidelines on how generative AI works. How can I verify if AI-generated content is accurate? When you get the output from the AI, cross-check the content with trusted sources that can provide you with some ground for fact-checking. Specifically, when you are working for academic or professional use, to avoid misleading or inaccurate information. Should AI hallucinations stop us from using generative tools? No, since hallucinations can sometimes be used as an advantage as well. Some developers use hallucinations to remove creative blocks; they use hallucinations to generate creative images or video games that are odd and unique. AI Awareness
AI Awareness What is AGI vs ASI in Artificial Intelligence Posted on April 10, 2025April 20, 2025 Introduction Imagine a machine being your doctor that diagnoses your disease within a few seconds and provides a cure that heals you instantly. Doesn’t it sound futuristic or similar to some of the movies of Christopher Nolan? Not anymore. Artificial intelligence has made this possible, and the developed version of… Read More
AI Technology & Trends What Are the 7 Cs of Artificial Intelligence and Why Are They Crucial for AI Success? Posted on May 24, 2025May 23, 2025 Artificial Intelligence is a widely utilized concept; every individual and every industry has applied it to fulfill their needs. As AI has spread worldwide, it has become an integral part of different communities. It has gained trust and reliance because of the automation provided at such a low cost. Although… Read More
AI Awareness Foundation Models: The Cornerstone of AI Advancements Posted on January 15, 2025January 23, 2025 Knowing formation models, use of formation models in practical life, and how AI foundation models, large language models (LLMs), and pre-trained models are transforming industries. Read More