AI Hallucination – Don’t Trust Everything ChatGPT Says
- Ankita Tiwari
- 4 minutes ago
- 7 min read
Imagine asking someone a question, and they respond with an answer that sounds confident and detailed—but it's entirely made up.
This is what sometimes happens when using AI tools like ChatGPT. These instances, where AI generates false or misleading information, are known as "AI hallucinations."
As AI becomes more integrated into our daily lives—helping us write emails, summarize reports, or even plan vacations—it’s important to understand that it’s not infallible.
One significant concern is the phenomenon of AI hallucinations, where models like ChatGPT produce information that appears accurate but is factually incorrect.
What Is an AI Hallucination?
In simple terms, an "AI hallucination" happens when an artificial intelligence tool makes something up. It might create facts, events, or even full articles that sound real — but aren’t actually true or based on real data.
For example, ChatGPT might confidently mention a news story or a statistic that was never published anywhere.
This isn’t because it’s lying, but because it’s guessing what should come next in a sentence based on patterns from the data it was trained on.
For instance, when asked for the latest news updates, the AI can sometimes return outdated articles, even after multiple attempts with different prompts. In one case, it kept providing links from 2023, most of them incorrectly labeled as 2025.
This shows how AI can produce seemingly relevant content, but based on older or inaccurate data, leading to misleading results.
Why Does AI Hallucinate?
AI models like ChatGPT don’t actually “know” facts — they predict the next word based on patterns in massive datasets. They’re not search engines or fact-checkers; they generate responses that sound right, not ones that are verified.
When there are gaps in training data or the prompt is vague, the model guesses based on statistical likelihood. This leads to hallucinations — confident but incorrect outputs.
Even advanced models like GPT-4.5 can do this because they are not inherently connected to real-time databases unless explicitly integrated (e.g., with web browsing).
AI Hallucinations also spike with high temperature settings, which increase creativity (and randomness), or in tasks that require specificity (legal, medical, tech, etc).
In AI models, like ChatGPT, temperature is a value that usually ranges between 0 and 1 though it can go a bit beyond 1 too. It doesn’t refer to actual heat, it’s a metaphor borrowed from probability and randomness.
Here’s what those numbers mean-
Temperature- 0
The AI becomes very strict and predictable. It always picks the most likely word every time. So the output will be safe, accurate, and repetitive.
Temperature- 1
AI becomes creative and diverse, picking from a wide range of possible next words. This makes responses more varied but also more likely to include errors or “hallucinations”.
Temperature > 1 (like 1.2 or 1.5)
The randomness increases further - often producing more surprising, imaginative thighs up.
So, can a regular user control the temperature?
In tools like the ChatGPT app or website, you don’t see or change the temperature setting directly. However, you can influence it indirectly by:
Being very specific in your prompts
Asking the model to only provide verified information or citations
Using follow-ups challenge or verify what it said
Also, there's no internal mechanism to flag falsehoods unless the system is fine-tuned with human feedback or linked to fact-checking tools.
In short, ChatGPT’s superpower is language fluency — not truth verification. Without careful prompt design and external validation, it’s easy for it to make things up.
Real Life Examples
AI hallucinations can have serious consequences in real-world situations, sometimes leading to unexpected outcomes. Here are two such cases-
Indian Tax Tribunal Cites Non-Existent Cases
In early 2025, a tax tribunal in Bengaluru issued an order that referenced court judgments which, upon closer inspection, didn't exist.
This raised concerns that generative AI tools, like ChatGPT, might have been used in drafting the order, leading to the inclusion of fabricated legal precedents.
The order was quickly withdrawn, but the incident highlighted the risks of relying on AI-generated content without thorough verification.
ChatGPT’s Hallucination Leads to Defamation of Innocent Father
In another alarming case, ChatGPT falsely generated a story accusing Norwegian man Arve Hjalmar Holmen of murdering his children—an entirely fabricated event.
The AI-generated story even included real details about his life, like his hometown and number of kids, making it feel disturbingly believable.
Digital rights group NOYB filed a GDPR complaint, saying such AI “hallucinations” violate the right to accurate personal data. They argued that disclaimers aren’t enough when false information can seriously damage someone’s reputation.
This case raises critical concerns about how language models can confidently produce harmful, made-up content.
The Compliance and Regulatory Risks of AI Hallucinations
AI hallucinations aren't just technical glitches—they can pose serious compliance and regulatory risks. In sectors like finance, banking, healthcare, and law, where accuracy is paramount, an AI-generated falsehood can lead to significant consequences.
For instance, if an AI tool fabricates a legal precedent or misinterprets a financial regulation, it could result in non-compliance, legal penalties, or reputational damage.
Regulatory bodies are increasingly scrutinizing the use of AI, emphasizing the need for organizations to ensure the reliability and accuracy of AI outputs. Implementing robust AI governance frameworks, conducting thorough risk assessments, and maintaining human oversight are essential steps to mitigate these risks.
How Companies Are Tackling AI Hallucinations?
While AI hallucinations can pose serious risks, companies are not sitting idle. They’re adopting smart, layered approaches to ensure AI stays grounded in reality.
Here’s a simplified breakdown of the three most common strategies being used today:
Retrieval-Augmented Generation (RAG)
RAG combines the generative capabilities of AI with real-time data retrieval from trusted sources. This means that instead of relying solely on pre-existing training data, the AI can fetch up-to-date information to generate more accurate and contextually relevant responses.
In 2024, the adoption of RAG saw significant growth across various industries. For instance, the global RAG market was valued at approximately $1.85 billion in 2025 and is projected to reach around $67.42 billion by 2034, indicating a compound annual growth rate (CAGR) of 49.12% . This surge reflects the increasing demand for AI systems that can provide accurate, context-aware, and scalable solutions.
Why Does It Matter?
By integrating RAG, companies can significantly reduce the risk of AI hallucinations, ensuring that the information provided is both current and accurate. This is particularly crucial in sectors like healthcare, finance, and legal services, where misinformation can have serious consequences.
Human-in-the-Loop (HITL)
HITL involves human oversight in the AI decision-making process. Humans review, interpret, and, if necessary, correct AI outputs, ensuring they meet ethical standards and align with organizational goals.
Organizations are increasingly recognizing the importance of HITL in maintaining AI accuracy and trustworthiness. For example, in legal and compliance sectors, human reviewers assess AI-generated documents to ensure they adhere to regulatory standards .
Why Does It Matter?
Incorporating HITL processes helps prevent the dissemination of incorrect or unethical information, thereby reducing the risk of compliance violations and maintaining public trust in AI systems.
Model Fine-Tuning
Model fine-tuning involves adjusting a pre-trained AI model using domain-specific data to improve its performance in particular tasks or industries.
Companies like OpenAI have introduced fine-tuning methods such as Reinforcement Fine-Tuning (RFT) to enhance AI models for complex domains like law and medicine . Additionally, platforms like Azure AI offer tools for customers to fine-tune models across various services, enabling more accurate and customized AI applications.
Why Does It Matter?
Fine-tuning allows AI models to better understand and respond to the nuances of specific fields, reducing the likelihood of errors and increasing the relevance and reliability of their outputs.
Tips to Mitigate AI Hallucinations
AI tools like ChatGPT can do a lot — write emails, explain quantum physics, even draft birthday wishes, but do not trust them blindly. That’s like asking your dog for stock market advice.
To strike the right balance between usefulness and accuracy, these tips can help-
Cross-Verify Information
Never take AI responses at face value, especially when it comes to important facts or data.
If you’re using AI for research, content creation, or decision-making, always double-check the information from trusted sources like news websites, official government pages, or published studies.
Be Specific with your Prompts
Ask for Sources — and Check Them
Don’t Use AI for Critical Decisions
Stay Updated and Aware
Closing Remarks
AI is powerful—but it’s not perfect. From confidently sharing outdated information to making up sources, AI hallucinations remind us that technology still needs human oversight.
While tools like ChatGPT can be incredible assistants, they’re not a replacement for real-world expertise or judgment.
At Riskinfo.ai, we believe that understanding the limitations of AI is just as important as exploring its potential. That’s why we break down complex AI concepts into digestible, real-world insights—so you stay informed, not misled.
Don't just use AI—understand it.
Follow riskinfo.ai for trusted, easy-to-digest insights that help you stay ahead in an AI-driven world. We’re here to make sure you get the real story behind the algorithms—minus the confusion, minus the hype.
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(Written by Ankita Tiwari. Special thanks to Khushi Tripathi for the valuable suggestions. Views expressed are personal.)
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