The Core Truth
AI language models predict the most statistically likely next word based on patterns in training data. They do not "know" anything, cannot verify truth, and have no mechanism to distinguish fact from plausible fiction.
What Is an AI Hallucination?
An AI hallucination occurs when a language model generates content that is factually incorrect, nonsensical, or entirely fabricated, while presenting it as if it were true. The term "hallucination" is used because the AI is essentially "seeing" patterns that lead it to generate content that does not correspond to reality.
Common types of hallucinations include:
Fabricated Citations: Generating academic papers, court cases, or news articles that do not exist. Over 600 legal cases nationwide have documented this problem.
False Facts: Stating incorrect information about real people, places, events, or concepts with apparent confidence.
Invented Quotes: Creating quotes attributed to real people that they never said.
Fictional Expertise: Providing detailed technical, medical, or legal advice that sounds authoritative but is incorrect.
How Language Models Actually Work
To understand why hallucinations happen, you need to understand what ChatGPT actually is: a statistical prediction engine.
The Autocomplete Analogy
Think of your phone's autocomplete feature. When you type "I'm going to the...", your phone suggests "store" or "gym" based on patterns it has learned. It does not know where you are actually going. ChatGPT works the same way, just at a much larger scale and with much more sophisticated pattern recognition.
The Training Process
Language models like ChatGPT are trained on massive amounts of text from the internet, books, and other sources. During training, the model learns statistical patterns: which words tend to follow other words, how sentences are typically structured, and what kinds of content usually appear in different contexts.
Critically, the model does not learn "facts" in the way humans understand them. It learns patterns of language. When it generates text about Abraham Lincoln, it is not accessing a database of Lincoln facts. It is predicting what words typically appear in text about Lincoln.
Why This Causes Hallucinations
Because the model predicts based on patterns rather than truth, it can generate text that follows correct patterns but contains false content. A fabricated court case name might follow all the patterns of real case names, making it statistically plausible even though it is entirely made up.
Human Knowledge vs. AI Pattern Matching
Human Understanding
When humans learn that "Paris is the capital of France," we store this as a fact that we can verify, question, and connect to other knowledge. We know that we know it, and we know how we learned it.
AI Pattern Recognition
When an AI "learns" this, it simply records that the words "Paris," "capital," and "France" frequently appear together in certain patterns. It has no concept of what a capital city actually is or why this information matters.
Real-World Examples
Legal Hallucination
In the Noland v. Land of the Free case (California, September 2025), an attorney's brief contained 21 fake case citations generated by ChatGPT. The AI created case names like "Smith v. California Department of Corrections" that followed all the patterns of real legal citations but referred to cases that do not exist. The attorney was sanctioned $10,000.
Government Report Fabrication
Deloitte used GPT to help prepare a 237-page Australian government report on safety standards. Reviewers discovered fabricated references, incorrect standards citations, and sources that did not exist. The AI generated plausible-sounding citations that followed correct formatting patterns but pointed to nonexistent documents.
Medical Misinformation
AI chatbots have been documented providing incorrect medical information with high confidence, including wrong drug dosages, contraindicated treatments, and fabricated drug interaction warnings. The patterns of medical text are replicated, but the underlying facts may be wrong.
Why Training Does Not Solve This
Many people assume that better training data would eliminate hallucinations. This is a fundamental misunderstanding of how these systems work.
The Pattern Problem: No amount of training changes the fact that the model is predicting patterns, not verifying truth. Even a model trained on 100% accurate data would still be predicting what words come next, not checking whether those words are true.
The Novelty Problem: When users ask questions that were not well-represented in training data, the model must extrapolate from patterns. This extrapolation often produces plausible-sounding but incorrect content.
The Confidence Problem: The model has no mechanism to express uncertainty proportional to its actual reliability. It generates text with the same confident tone whether it is correct or completely wrong.
What This Means for Users
Understanding why hallucinations happen leads to clear guidelines for AI use:
Never Trust Without Verification: Any factual claim from an AI should be verified against primary sources before being used or shared.
Avoid High-Stakes Reliance: For legal, medical, financial, or safety-critical applications, AI output is inherently unreliable and should not be trusted without expert human review.
Use for Appropriate Tasks: AI is useful for brainstorming, drafting, and exploring ideas where factual accuracy is not critical and human review will follow.
Recognize the Limits: AI cannot know when it is wrong, cannot assess its own reliability, and cannot distinguish between patterns that reflect truth and patterns that merely sound true.