In 2023, a US law firm was fined $5,000 for citing fake court cases in a legal brief.
Where did the fake cases come from? ChatGPT.
This is called AI hallucination—when an AI confidently makes things up. It’s not a rare glitch; it’s a critical flaw with real-world consequences. Since mid-2023, there have been over 120 documented cases of AI inventing legal facts. The risk is also huge in fields like healthcare and journalism.
This month’s V-Techtip breaks down what AI hallucination is and why it happens. We’ll give you a simple framework and a checklist of safeguards to protect your work from this growing problem, when doing your research with AI.
Table of Contents
Understanding AI Hallucinations
An AI hallucination is when a large language model gives you an answer that is false, misleading, or completely made up, but presents it as a confident fact. In September 2025, it’s crucial to understand that this isn’t a simple “bug” that can be fixed. It’s a fundamental characteristic of how these AI models work.
What is an AI Hallucination, Really?
First, “hallucination” is just a metaphor. The AI isn’t seeing things. It’s a sophisticated pattern-matching engine, not a thinking being. Its main job is to predict the next most likely word in a sentence based on the massive amount of data it was trained on.
When an AI doesn’t have enough data to answer a question accurately, it will still try to generate a coherent, plausible-sounding response. This is when a hallucination happens. It prioritizes sounding correct over being correct.
The most important advice: Never confuse fluency with accuracy. Always be skeptical.
Hallucinations happen on a spectrum, from subtle errors to complete fabrications:
- Subtle Errors (Amplifying Bias): This is when the AI reinforces stereotypes from its training data. For example, if you ask it to generate images of “doctors,” it might only show you men, which distorts a balanced representation of reality.
- Factual Inconsistencies (Getting it Wrong): This is when the AI gives you a specific, verifiably wrong fact. For example, an AI falsely reported that an Australian mayor was convicted of a crime, when in reality he was the whistleblower who exposed the crime.
- Pure Fabrications (Making Things Up): This is the most dangerous kind. The AI completely invents things that don’t exist. A famous example is when Meta’s science AI started citing fake scientific papers and attributing them to real, respected scientists. Lawyers have also been caught submitting fake, AI-generated legal cases to a court.
Real-World Impacts: When Hallucinations Cause Harm
These aren’t just funny mistakes; AI hallucinations are causing significant, real-world harm.
- Defamation and Misinformation: An AI falsely reported that an Australian mayor, who was a whistleblower in a bribery scandal, had been convicted of the crime, causing serious damage to his reputation.
- Dangerous Medical Advice: In one case, an AI drug checker made up fake adverse reactions between medicines, leading doctors to avoid safe and effective treatments. In another, a chatbot gave harmful dieting advice to a user with an eating disorder.
- Fake Academic and Legal Cases: Meta’s science-focused AI model had to be pulled after just two days because it was inventing fake scientific papers and citing them with the names of real, respected scientists. This is the same pattern seen in legal cases where lawyers have submitted fake, AI-generated legal precedents to a court.
III. The Hallucination Spectrum
Not all AI hallucinations are the same. In September 2025, understanding the “Hallucination Spectrum” is key to using these tools safely. This framework breaks down AI-generated falsehoods into four main categories, from subtle biases to complete fabrications. Let’s take a more detailed look at each one.
A. Bias-Driven Errors
This is the most subtle but common type of AI hallucination. The AI isn’t telling a direct lie, but it’s distorting reality by amplifying stereotypes it learned from its training data. This happens because the AI is trained on vast, uncurated data scraped from the internet. That data is a mirror of human society, and it contains all of our historical and cultural biases. The AI simply learns these statistical patterns—it doesn’t know the difference between a fact and a stereotype.
Example scenario. A clear and well-documented example of bias-driven error occurs in AI image generation. When a model like DALL-E or Midjourney is given a generic prompt such as “Generate an image of a ‘CEO’,” the output overwhelmingly consists of images depicting white males. Conversely, prompts for “nurse” or “flight attendant” disproportionately yield images of women, while a prompt for “housekeeper” often generates images of women from minority ethnic groups. This bias is so deeply embedded that it can resist explicit correction. One study tasked Midjourney with creating an image of a Black African doctor treating a White child. Despite the specific prompt, the AI consistently rendered the child patient as Black, while in some instances depicting the African doctor as White, thereby perpetuating the harmful “white savior” trope and failing to subvert the stereotype as intended.
B. Contextual Drift
This happens during a long conversation when the AI forgets the original topic. This is because the AI has a limited “memory” or “context window.” As your chat gets longer, the initial instructions can get pushed out of this window, and the AI will drift into a related but incorrect response.
Example prompt and drifted answer. Consider a professional workflow where a user is interacting with an AI assistant to manage a project.
- Initial Prompt: “We discussed the Q3 budget constraints for the ‘Project Titan’ marketing campaign yesterday. Can we review the proposed cost-saving solutions?”
- Drifted Answer (after several turns of discussing specific line items): “Sure, I can help you with budget planning. To create a new marketing budget, we first need to define your target audience. For ‘Project Titan,’ would you say the primary demographic is millennials or Gen Z?”
- Analysis: In this scenario, the AI has lost the specific, nuanced context of the initial request, which was to review existing solutions for known constraints. Over the course of the conversation, it has latched onto the general keywords “budget” and “marketing campaign” and has drifted into a related but incorrect task: creating a new budget from scratch. This demonstrates a classic failure in context management, where the model’s response is plausible on the surface but functionally useless because it ignores the established conversational history.
C. Fact-Conflicting Statements
This is when the AI confidently states something that is verifiably false. If the AI doesn’t have the right information in its training data, it will often “bluff” and make up a specific but wrong detail rather than just saying “I don’t know.”
Example with fabricated citation. A user asking an AI for a historical overview is particularly vulnerable to this type of hallucination, as the output can skillfully blend truth with falsehood.
- Prompt: “Provide a brief history of the development of AI, including key milestones and dates.”
- Hallucinated Output: “The field of AI was formally established in 1952 at the Dartmouth Conference, where John McCarthy coined the term. A major breakthrough came in 1961 when Joseph Weizenbaum created ELIZA, the first chatbot. This was followed by the development of the first expert system, SAINT, in 1958.”
- Analysis: This response is a tapestry of factual errors woven together with correct names and concepts, making it deceptively plausible. The Dartmouth Conference, where the term “artificial intelligence” was coined, took place in 1956, not 1952. The chatbot ELIZA was created in 1966, not 1961. The expert system SAINT was developed in 1961, not 1958. The AI has confidently misstated multiple key historical dates, demonstrating its capacity to generate precise but incorrect information that could easily mislead a user who is not an expert on the topic.
D. Pure Fabrications
This is the most extreme and dangerous type of hallucination. The AI will invent things from whole cloth because its main goal is to sound correct, even if it has to make up the substance. It can’t tell the difference between what a real citation looks like and what a real citation is.
High-impact example. In a famous example, a data scientist deliberately asked ChatGPT about a nonsense phrase she invented: “cycloidal inverted electromagnon.” Instead of saying the term didn’t exist, the AI generated a detailed, scientific-sounding definition, complete with citations to fake scientific papers. Another powerful example is when AI image generators are used to create photorealistic photos of fake historical events, like “The Great Cascadia Earthquake of 2001,” which can easily be mistaken for real history.
The following table provides a consolidated overview of the AI Hallucination Spectrum, summarizing the key characteristics of each category.
Table 1: The AI Hallucination Spectrum at a Glance
| Spectrum Category | Description | Primary Root Cause | High-Impact Example | Key Mitigation Strategy |
| Bias-Driven Errors | Outputs reinforcing societal stereotypes and prejudices. | Skewed and biased training data scraped from the internet. | Generating only male images for “CEO” or associating specific ethnicities with crime. | Data diversity auditing; Adversarial testing for bias. |
| Contextual Drift | Loss of topic focus and original intent in long conversations. | Context window limitations and semantic “pollution” from ambiguous turns. | A chatbot for technical support drifts into giving generic life advice. | Session summarization; Focused, single-intent prompts. |
| Fact-Conflicting Statements | Direct contradiction of known, verifiable facts (dates, stats, events). | Data voids, overfitting on incorrect data, or misinterpretation of patterns. | Stating the first moon landing occurred in 1968 instead of 1969. | Cross-verification tools; Grounding with reliable sources (RAG). |
| Pure Fabrications | Creation of entirely non-existent entities, sources, or narratives. | Model architecture optimizing for plausibility over factuality; Overconfidence. | Citing non-existent legal cases or academic papers to support an argument. | Human-in-the-loop verification; Demanding citations and provenance. |
Root Causes Behind Hallucinations
AI hallucinations aren’t random glitches. They are systemic failures that stem from three core areas: the flawed data the AI is trained on, the inherent limitations of the model’s architecture, and the imprecise prompts that users provide. In September 2025, understanding these three root causes is the first step to using AI safely and effectively.
1. The Data: Garbage In, Garbage Out
The foundation of any AI is the data it’s trained on. Most of this data is scraped from the internet, which means it’s full of inaccuracies, biases, and huge gaps in knowledge. When you ask an AI about a topic where it has a “data void,” it will often make up a plausible-sounding but false answer instead of just saying “I don’t know.” This is a primary cause of factual errors and biases. The model isn’t being malicious; it’s simply reflecting the flawed, skewed data it learned from.
2. The Model: Designed for Confidence, Not Truth
A second major cause is the AI’s architecture itself. Most models are not designed to know when they’re unsure. Their main job is to give you the single most probable answer, not necessarily the most truthful one.
This leads to “model overconfidence.” The AI will present a completely fabricated answer with the same authoritative, confident tone as a real fact. This is dangerous because it can trick you into trusting bad information. The burden of being skeptical is entirely on you, the user.
3. The Prompt: Vague Questions Get Vague (or Wrong) Answers
Finally, the quality of the AI’s output is directly tied to the quality of your input. Vague, ambiguous, or overly complex prompts force the model to guess what you really want. This dramatically increases the chance of getting an irrelevant or completely wrong answer. This is also the vulnerability that hackers can exploit with “prompt injection” attacks to try and bypass the AI’s safety filters.
A Shared Responsibility
Fixing the hallucination problem isn’t one person’s job. It’s a systemic challenge that requires everyone to play a part. Data scientists need to provide cleaner data, AI researchers need to build better models, and we, the users, need to write clearer prompts and always maintain a healthy dose of skepticism.
Actionable User-Centric Regulation Measures
AI hallucinations are a fundamental part of how the technology works, but you’re not powerless against them. In September 2025, you can use a suite of actionable, user-centric strategies to significantly reduce your risk. Think of it as a layered defense. Here are the key measures you can take.
1. Master Your Prompts: Garbage In, Garbage Out
Your first and best line of defense is a high-quality prompt. The quality of the AI’s output is directly tied to the quality of your input.
Be clear and specific. Tell the AI the exact format, length, and tone you want. For complex questions, use the “Self-Ask” technique: force the model to break your big question down into a series of smaller, logical sub-questions and answer each one. This makes its reasoning transparent and easy for you to check.
2. Demand Evidence and Always Cross-Verify
Make the AI show its work. Explicitly ask it to provide sources and citations for its claims. Prioritize using AI tools that have this feature built-in, especially those that use Retrieval-Augmented Generation (RAG) to ground their answers in real documents.
Never trust, always verify. For any critical piece of information, use a third-party tool to check it. This could be a browser extension like the Copyleaks AI Content Detector to see if text was AI-generated, or a scholarly search tool like Sourcely to find real academic papers.
3. Teach the AI to Say “I Don’t Know”
A major cause of hallucinations is that the AI will “bluff” instead of admitting it’s uncertain. You can fight this. As a user, you can add a simple instruction to your prompt: “If you are not certain of the answer, please state that you do not know.” This simple phrase can prime the model to avoid guessing and making up an answer.
4. Keep Records and Proactively Test for Weaknesses
Keep a log of your AI interactions. This audit trail is invaluable for finding patterns of failure and debugging problems. Use these logs to provide feedback and help refine your AI system over time.
Finally, be a “red teamer.” Proactively try to break your AI in a controlled way. Use adversarial prompts designed to trick it into giving biased or unsafe answers. When you find a vulnerability, report it to the AI provider (like OpenAI or Google). This helps make the entire ecosystem safer for everyone.
5. The Ultimate Safeguard: The Human in the Loop
For any high-stakes task, technology alone is not enough. The final and most important safeguard is meaningful human oversight.
The safest and most effective way to work with AI is to treat it as an assistant that generates a first draft. A qualified human expert must always make the final, accountable decision. This “human-in-the-loop” approach is the only way to balance the efficiency of AI with the irreplaceable judgment and ethical reasoning of a human.
Building Your Personal Mitigation Toolkit: How to Fact-Check AI Hallucinations
Knowing about AI hallucinations is one thing; actively fighting them is another. In September 2025, you can build a personal toolkit of simple habits and tools to protect yourself. This isn’t about buying expensive software; it’s about creating a smart, low-friction workflow for using AI safely and responsibly.
1. Build a Library of Reusable Prompt Templates
Stop writing every prompt from scratch. The best way to get consistent, high-quality results is to create and reuse prompt templates. This can be as simple as a shared document where you save your most effective prompts. Include templates for:
- The “Persona” Prompt: To set a consistent tone (e.g., “Act as a professional financial analyst…”).
- The “Structured Output” Prompt: To get data back in a specific format (e.g., “Return the answer as a Markdown table.”).
- The “Self-Ask” Prompt: For complex research, use a template that forces the AI to break the question down into smaller, logical steps.
2. Set Up a “Verification Dashboard”
This isn’t a piece of software; it’s a simple workspace setup. Arrange your screen with your AI chat window on one side and a browser window with your favorite fact-checking tools on the other. This makes it easy to follow a simple, 4-step workflow: Generate -> Isolate a claim -> Verify -> Compare. This side-by-side setup removes the friction of having to constantly switch between windows and makes fact-checking a natural part of your process.
3. Install an AI-Detecting Browser Extension
Browser extensions can act as an automated early-warning system. A tool like the Copyleaks AI Content Detector or GPTZero can scan the text on any webpage you’re reading and give you a score on how likely it is to be AI-generated. It’s a great first-pass check for the authenticity of online articles, reviews, or social media posts.
4. Create a “Fact-Check This” Bookmarklet
For a more lightweight solution, you can create a bookmarklet. This is a browser bookmark that runs a small piece of JavaScript when you click it. You can create one that, when you highlight any text on a page and click the bookmark, will automatically open a new tab and search your favorite fact-checking site with that text. This turns a slow, manual process into a single click, making you much more likely to actually do it.
We’ve seen that AI hallucinations are a fundamental part of how the technology works in September 2025. They range from subtle biases to complete fabrications. The most important lesson is simple: never mistake a confident-sounding answer for a correct one. But you’re not helpless. By adopting a few key practices, you can protect yourself and become an active participant in building a safer AI ecosystem.
Your Toolkit for Responsible AI Use
The strategies we’ve discussed are a layered defense against being misled by AI-generated falsehoods. The goal is to move from just knowing about the problem to actively fighting it. We strongly encourage you to build your own personal mitigation toolkit.
This means creating reusable prompt templates, setting up a verification dashboard for easy fact-checking, and using confidence-monitoring browser extensions. By making these tools a part of your daily workflow, you transform your interaction with AI from an act of blind faith into a process of structured, evidence-based inquiry.
Resources for AI Safety: Tools and Communities
Fighting AI hallucinations and promoting responsible AI requires the right tools and a strong community. In September 2025, a growing ecosystem of resources is available to help you verify information and connect with others who are working on these challenges. Here are some of the recommended tools and communities.
Recommended Fact-Checking and Audit-Logging Tools
For Fact-Checking and Verification
- Google Fact Check Tools: A suite of resources that lets you search a database of fact-checks from reputable publishers around the world.
- Sourcely: An AI-powered tool for academic research that lets you verify claims against a database of over 200 million peer-reviewed papers.
- GPTZero & Copyleaks: These are leading AI content detectors. Their browser extensions can scan the text on any webpage and tell you how likely it is to be AI-generated, giving you a real-time check on authenticity.
For Audit Logging and Observability
- Langfuse: An open-source tool specifically designed for logging, tracing, and analyzing the conversations you have with your LLM applications.
- New Relic & Zendesk: These are broad enterprise platforms that offer powerful tools for logging and analyzing system performance and user interactions at a large scale.
Community Forums and Policy Discussion Groups
If you’re interested in the bigger picture of AI safety and ethics, here are a few key groups to follow.
- AI Ethics Council (Operation HOPE): Focuses on the ethical impact of AI, especially on underserved communities, and promotes principles like transparency and human oversight.
- Future of Life Institute: A global community of AI researchers and professors dedicated to ensuring that advanced AI systems remain safe and beneficial for humanity.
- Effective Altruism Forum (AI Safety Section): An online public forum where researchers and practitioners share and discuss a wide range of ideas for improving AI safety.
- UNIDIR (RAISE): A United Nations-led forum that brings together global leaders from government, industry, and academia to foster dialogue on AI governance and international security.
Conclusion
The world of AI is moving incredibly fast, and our understanding of its problems has to keep up. This guide isn’t the final word; it’s a starting point.
We encourage you to share your own experiences with AI hallucinations. What have you seen? What tools and techniques have you found most effective in your work?
The responsibility for ensuring AI is deployed safely and ethically doesn’t just belong to the big tech companies. It’s a shared responsibility that includes developers, researchers, and a vigilant, educated community of users like you. By working together, we can build a more resilient and trustworthy information ecosystem.