AI Hallucinations Explained: Why LLMs Make Mistakes
AI hallucinations are confident but false outputs from large language models. Learn why they happen, real-world risks, and strategies like RAG and fact-checking to prevent them.
In May 2023, a New York lawyer found himself in hot water after using ChatGPT to draft a legal brief. The AI confidently cited six federal court cases to support his arguments—except none of them existed. The fabricated citations included realistic case names, dockets, and even fake judicial opinions. The lawyer, Steven Schwartz, and his colleague faced $5,000 in sanctions from Judge P. Kevin Castel, and the incident became a cautionary tale about trusting AI-generated content without verification.1
This wasn’t a glitch or a bug. It was an AI hallucination—a phenomenon where large language models (LLMs) generate information that sounds plausible but is completely fabricated. As these models power healthcare diagnostics, legal research, customer service, and education, the risks are real. A hallucinated medical recommendation could endanger lives. A fabricated financial report could mislead investors.
Understanding why AI hallucinates—and how to prevent it—is no longer an academic curiosity. It’s a technological imperative.
What Are AI Hallucinations?
AI hallucinations occur when a language model generates text that appears fluent and confident but contains fabricated facts, nonsensical claims, or invented references.
Unlike humans who can admit “I don’t know,” LLMs are designed to predict the next most likely word. Without a mechanism to express uncertainty, they often fill gaps with plausible-sounding fiction.
How Hallucinations Differ from Other Errors
It’s important to separate hallucinations from other AI limitations:
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Bias → Prejudices reflected from training data
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Outdated Info → Lack of knowledge past training cutoff
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Misinterpretation → Misunderstanding ambiguous prompts
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Hallucination → Inventing entirely new “facts”
Real-World Consequences
The Legal Brief Disaster
The New York lawyer case shows systemic risk in professional services. Schwartz testified he wasn’t aware ChatGPT could fabricate cases.2 The citations included proper formatting, realistic judge names, and coherent—but fictional—reasoning. Judge Castel noted the tool wasn’t the issue; the failure to verify AI output was.3
Medical Misinformation
The stakes are even higher in healthcare. Studies show LLMs can:
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Fabricate medical studies
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Invent drug interactions
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Misattribute quotes to professionals
The Med-HALT benchmark found that models often generate dangerous but plausible-sounding medical claims.4 A 2024 Nature study confirmed hallucinations in radiology outputs could risk human life.5
Why Do LLMs Hallucinate?
Hallucinations aren’t random “bugs.” They come directly from how large language models (LLMs) are built and trained. Let’s break it down:
1. Next-Token Prediction Without Understanding
LLMs (like GPT, Claude, or Gemini) don’t “think” in the way humans do. At their core, they’re giant probability machines trained to predict the next word (or token) in a sentence.
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If you type: “The capital of France is …”, the model knows “Paris” is the statistically most likely next word based on training data.
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But if you type something more obscure, like “The capital of Wakanda is …”, it will still give you an answer—even though Wakanda is fictional. Why? Because its job isn’t to verify facts, but to output something fluent and plausible.
2. Lack of Grounding in Verifiable Sources
Traditional search engines (Google, Bing) retrieve information directly from indexed documents. If you ask, “Who is the CEO of Tesla?”, they’ll pull from a current webpage or database.
LLMs don’t work that way. They generate answers from memory, which is the compressed representation of all the text they’ve trained on.
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They don’t look up facts in real time.
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They can’t check a database or confirm citations.
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They don’t distinguish between “I’ve seen this fact many times (likely true)” vs. “This looks statistically plausible but I’ve never seen it (likely false).”
3. Training Data Gaps and Contradictions
LLMs learn from internet text, books, research papers, and more. But that training data isn’t perfect:
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Errors in the source data → If a blog post contains a wrong fact, the LLM might learn and repeat it.
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Contradictions → If one source says “Event X happened in 1995” and another says “Event X happened in 1997”, the model doesn’t weigh credibility—it just absorbs both.
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Sparse data → For rare or new information (e.g., a medical study published after training cutoff), the model has no exposure, so it “fills the gap” with plausible fiction.
This leads to blended or hybrid facts—half-true, half-invented statements. For example:
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Asked about a real but obscure scientific paper, the model might invent an author’s name that “sounds right.”
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Or it might merge details from two different studies into a non-existent third study.
Fighting Back: Strategies to Reduce Hallucinations
Retrieval-Augmented Generation (RAG)
Instead of recalling facts from memory, RAG systems first retrieve verified documents from a knowledge base, then generate text conditioned on them.
👉 Think of it as an open-book exam instead of a memory test.
Fact-Checking Pipelines
Multi-stage verification systems can:
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Require source attribution
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Cross-check against trusted databases
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Assign confidence scores
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Use a second AI to detect hallucinations
Fine-Tuning & Reinforcement Learning
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RLHF trains models to reduce hallucinations by penalizing wrong outputs.
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Newer methods encourage models to say “I don’t know” when uncertain.
Human-in-the-Loop Systems
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For high-stakes industries (law, medicine, finance), AI should assist—not replace—humans. Experts can review drafts and ensure outputs are correct before use.
The Path Forward: Toward Trustworthy AI
AI hallucinations remind us that making AI sound human-like was easier than making it reliable.
The next generation of solutions—RAG pipelines, fact-checking tools, fine-tuning methods, and hybrid workflows—show real promise. But total elimination of hallucinations may be impossible with current architectures.
The realistic goal is trustworthy AI that:
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Grounds outputs in verified sources
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Signals uncertainty clearly
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Provides citations for claims
The New York lawyer who trusted ChatGPT learned a costly lesson. As society embraces AI, we must build systems that balance innovation with reliability.
Call to Action
👉 If you’re building AI apps, don’t ignore hallucinations.
Ground your models with RAG, add fact-checking, and verify outputs before deploying.
Confidence ≠ correctness.
References
CNBC. (2023). "Judge sanctions lawyers for brief written by A.I. with fake citations." ↩
Legal Dive. (2023). "Lawyer cites fake cases generated by ChatGPT in legal brief." ↩
Goldberg Segalla. (2023). "Fake Cases, Real Consequences: Misuse of ChatGPT." ↩
arXiv. (2023). "Med-HALT: Medical Domain Hallucination Test for LLMs." ↩
Nature. (2024). "Detecting hallucinations in LLMs using semantic entropy." ↩
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