Artificial Intelligence Hallucinations: How Neural Networks Generate Plausible Lies

Introduction

Artificial intelligence hallucinations have long ceased to be uncommon. When ChatGPT fabricated judicial precedents for attorney Schwartz, it served as a stark warning to the entire sector. At that moment, many understood: trusting neural networks without scrutiny is perilous. Yet paradoxically, people persist in doing exactly that.

Why Does AI Hallucinate?

Because it's a probabilistic machine. A neural network doesn't think like a human. It predicts the next word based on millions of examples from training data. When information is insufficient, the model selects the most probable option. Even if it's completely fabricated.

Working with artificial intelligence, I've encountered this constantly. Neural network hallucinations don't manifest only in text. They arise in data analysis, in image generation, in information classification. And each time, it threatens businesses with real financial losses.

The problem of AI hallucinations is becoming increasingly acute. BBC statistics show: 60% of responses from popular models contain serious errors on critical questions. OpenAI published a 36-page report on this phenomenon. Researchers are seeking solutions. But it's impossible to completely eliminate hallucinations — it's a fundamental property of large language models.

What Are AI Hallucinations Really?

They are syntactically correct, semantically coherent text that is factually false. The model doesn't lie intentionally. It simply doesn't know it's wrong. It sounds convincing — and that's the problem.

In this article, I'll explain why models generate unreliable information. I'll show the mechanism behind these errors. I'll provide real-world examples of consequences. And most importantly — I'll share proven protection methods. We'll discuss strategies for combating neural network hallucinations, how quality control systems work, and why human verification remains irreplaceable.

Let's start with the basics. We need to understand what's happening inside.

A REAL EXAMPLE: WHEN NEURAL NETWORKS PRODUCE FALSE INFORMATION

Peter Schwartz is an ordinary lawyer from New York. In March 2023, he did what millions of others have done: he opened ChatGPT and asked for help with a legal case. He needed to find precedents that would support his client's position. The model confidently cited three cases: Vazquez v. Aeropostale, Martinez-Molina v. Gonzales, Hanson v. Waller. They all sounded convincing. They were all completely fabricated.

Schwartz didn't verify the sources. He cited these "precedents" in his legal document. The judge asked him to provide the references. The lawyer submitted them. Then it became clear: these cases didn't exist in the databases of federal regulatory bodies. They didn't exist in any official registry.

The result? A $5,500 fine against Schwartz himself. His reputation suffered. The firm faced reputational damage. But most importantly, it demonstrated just how dangerous AI hallucinations can be in critical fields.

Why did this happen? Because neural networks generate text rather than retrieve information. They predict the next word based on patterns. When a model is trained on legal documents, it learns how case names sound. It knows the format: "Name v. Name." And when asked to cite a precedent it doesn't know precisely, it fabricates a plausible-sounding name. This process occurs within the algorithm without any awareness that the information is fabricated.

The Schwartz case is not an exception. It's an indicator of a systemic problem. Lawyers started verifying AI answers. Financial professionals began to doubt. Medical practitioners tightened their controls. Because AI hallucinations can lead to extraordinarily expensive mistakes. In legal practice, one error can cost you your license. In medicine, it can cost patients their health. In finance, it can cost millions of dollars.

This is a landmark case. It demonstrated that even authoritative professionals can be misled if they trust AI without verification.

DEFINING HALLUCINATIONS — WHAT THEY REALLY ARE

An AI hallucination occurs when a neural network generates information that sounds plausible but is entirely fabricated. The model doesn't lie intentionally. It simply doesn't understand the difference between what's real and what's invented.

To grasp the essence, we need to understand how large language models work. They don't store facts like a database. Instead, they predict the next word based on all previous words. The process is purely statistical. The model analyzes billions of texts and learns which word most frequently follows another.

When you ask the model something, it breaks your query into tokens — small units of text. Then it passes through its neural layers and generates probabilities for the next token. It selects the most probable one — and this process repeats over and over. The result is new text, generated word by word.

The problem is that this approach doesn't distinguish truth from fiction. If the training data contained little information about a particular topic, the model will guess. It will select a probable word, even if the fact is wrong. And it will sound convincing because the grammar is correct and the structure is logical.

Hallucinations take many forms. There are factual errors — when the model provides incorrect information about real events. There are fabricated sources — when it creates non-existent books, articles, or people. There are logical contradictions — when it contradicts itself within a single response. There are non-existent products and services that sound realistic.

What distinguishes hallucinations from ordinary errors is that the model remains confident in its incorrect answer. It doesn't say "I don't know." It provides details, examples, even "sources." People believe it because everything appears plausible. That's why these errors are so dangerous. That's why every fact needs verification.

When a neural network produces false information, it rarely looks like an error. More often, it looks like truth.

HOW AND WHY AI INVENTS INFORMATION

The Token Prediction Mechanism

Why does AI hallucinate? The answer lies in how neural networks fundamentally operate. Large language models don't think — they predict. Here's how it works.

Text is broken down into tokens — small units. These can be words, syllables, even individual letters. The model receives a sequence of tokens and processes them through billions of parameters. The output is a set of probabilities for the next token. The model selects the most probable option. It then adds this token to the text and repeats the process.

It sounds logical, doesn't it? The problem is that probability is not truth. If the training data frequently contains the phrase "French president Macron," then the model will predict "Macron" as the probable name for a president. Even if you're asking about the president of Spain. Statistics defeats accuracy.

When a neural network produces false information through this prediction mechanism, it's not a programming error. It's the nature of the algorithm itself. The model operates exactly as it was designed to. It selects the next word based on probability, not on truth.

Primary Causes of Hallucinations

The primary causes of AI hallucinations are related to how models are trained and used.

First cause: insufficient or incomplete training data. If little has been written about a particular topic on the internet, the model will fill gaps with probable words. For example, information about a new technology product might be scarce. The model will create a description that sounds realistic but is entirely fabricated.

Second cause: contradictions in training data. If the same information is described differently across various sources, the model may randomly select the incorrect version. The process is probabilistic, so the outcome is unpredictable.

Third cause: data distribution shift. The model was trained on texts up to a certain date. But the world changes. New events occur. When you ask about recent news, the model doesn't know the answer and invents one based on old patterns.

Fourth cause: the model prefers to answer rather than admit ignorance. Neural networks are trained to be helpful. They don't like saying "I don't know." Instead, they generate an answer — even if they're making it up.

Fifth cause: generation parameters. High temperature means high randomness. The model may select an unlikely option. Even if it's fabricated. Low temperature reduces errors but makes answers more boring.

Model Autophagy Disorder (MAD) — When AI Trains on AI Errors

There is one more dangerous cause — Model Autophagy Disorder (MAD). This occurs when a neural network trains on texts written by another neural network. Errors accumulate and amplify. Imagine this: ChatGPT generates an article containing hallucinations. Another model reads that article and trains on it. The error becomes a "fact" for the new model. Then a third model trains on the second text. The hallucination grows exponentially. It's like a game of broken telephone, but with information.

The MAD phenomenon demonstrates that hallucinations are not simply an error within a single model. It's a systemic problem that can propagate and intensify. This is why source verification becomes critically important. Even if the answer sounds plausible.

The causes of neural network hallucinations are multilayered. They are embedded in the architecture. It's impossible to completely eliminate them. We can only reduce their frequency and exercise strict control over results.

REAL-WORLD EXAMPLES OF HALLUCINATIONS IN CRITICAL SECTORS

Attorney Schwartz's Legal Case — When Hallucinations Cost Money

We've discussed this case before, but it deserves deeper examination. Attorney Peter Schwartz used ChatGPT to search for legal precedents. The model cited three cases that didn't exist. Schwartz didn't verify them. The judge discovered the fabrication. Result — a $5,500 fine and reputational damage.

But this isn't simply a story about one error. It's an indicator of how AI hallucinations affect real people. Lawyers now fear using AI. Or they use it but meticulously verify every fact. Work hours increase. Costs increase. Clients pay more.

Google Lens Recommends Edible Stones

A funny but serious example. Google Lens is a computer vision system. Someone photographed rocks and asked if they were edible. The model responded: "Yes, if processed properly." This is a hallucination with dangerous consequences.

The system is trained to recognize images. But when it encounters an ambiguous object, it can produce an incorrect result. A child could injure themselves, and parents typically trust Google. This is why such errors are so critical. When information appears plausible, people accept it as fact.

Financial Losses on Wall Street

Financial professionals actively use AI for market analysis. Sometimes models generate forecasts that sound convincing but are based on fabricated data. One trader relied on AI analysis. The model predicted company stock growth based on a non-existent report. The trader invested. The loss was in the millions.

This isn't an isolated incident. Financial institutions have implemented strict protocols: every AI forecast must be double-checked by a human. The model's reasoning process must be transparent. Sources must be verified. Otherwise, the risks are too high.

Medical Errors and the NHS

Britain's National Health Service (NHS) deployed AI for diagnosing certain diseases. The system was meant to assist doctors. But errors occurred. The model diagnosed a disease the patient didn't have. Why? In the training data, the disease had been incorrectly labeled. The algorithm learned the error as a pattern.

Medicine is a domain where every error can cost lives. Therefore, AI is used only as a doctor's assistant, not as a replacement. Human verification remains mandatory. Even the most advanced models don't replace clinical expertise. Because hallucinations in medicine are unacceptable.

Nobel Prize Through AI "Errors"

Not all hallucinations are harmful. AlphaFold is DeepMind's system for predicting protein structures. Sometimes the model "guessed" unconventional configurations that turned out to be new scientific discoveries. It sounds paradoxical: an error led to success.

In 2020, the Nobel Prize in Chemistry was awarded for achievements in the model's application. Researchers used the system's results even when they appeared counterintuitive. They then verified them in the laboratory. Some of the "hallucinations" turned out to be new facts. The model's creativity played a positive role.

But this is an exception, not the rule. In most cases, hallucinations are something that must be controlled and minimized.

HOW SERIOUS IS THE HALLUCINATION PROBLEM?

Error Statistics in Modern Models

Statistics reveal the scale of the problem. BBC conducted research and found: 60% of responses from popular models contain serious errors when answering critical questions. These aren't typos. These are systematic hallucinations.

Data from OpenAI's 36-page report on hallucinations in their models:

• GPT-3: 20-30%
• GPT-4: 10-15%
• GPT-4 Turbo: 8-12%

Improvement is occurring, but it's slow. And even 8% is substantial when critical sectors are involved. In medicine, 8% error rate means patient risk. In finance, it means losses. In law, it means incorrect verdicts.

Other models show similar results. Google's Gemini is more accurate with current data, but still makes mistakes. Anthropic's Claude is more conservative — it fabricates less frequently but provides less information. No model is hallucination-free.

Critical Sectors — Where Errors Are Unacceptable

Not all errors are equal. In certain industries, AI hallucinations create extreme risks.

Medicine

A diagnostic error can cost lives. Even 1% is unacceptable. Therefore, AI in medicine is used only as a doctor's assistant. Humans make the final decision. All data is verified through official databases. Every result is verified before application.

Finance

An incorrect financial forecast can lead to losses in the millions. Regulators (SEC, Central Bank) demand complete transparency. What data did the model use? What training process? Why this result? Without answers to these questions, financial companies cannot use AI for client recommendations.

Law

An incorrect legal precedent can lead to an incorrect verdict. As the Schwartz case demonstrated, even authoritative professionals can make mistakes. Therefore, all AI results in legal practice require review by an experienced lawyer. Sources must be verified. This adds time and cost, but there's no alternative.

Education

When a student learns from AI hallucinations, they absorb incorrect information. This affects the quality of education and professional development.

Are Error Numbers Growing or Improving?

A paradox: models become smarter, but hallucinations don't disappear. They change form. In 2022, errors were "obvious" — poor grammar, logical contradictions, simple factual mistakes. Hallucinations were easy to spot.

In 2024-2025, errors became "sophisticated." The text is grammatically correct. The structure is logical. The sources look real. But the information is fabricated. Recognizing such hallucinations is harder.

This means the problem isn't being solved — it's becoming more complex. People trust such answers more. The risk is higher. Therefore, source verification becomes even more critical. Every fact needs verification through independent databases. Especially in critical sectors.

Research shows: the number of hallucinations is declining slowly. But hallucination quality is improving. They become increasingly convincing. This creates new quality control challenges. You can't simply read the answer — serious verification is required.

HOW TO AVOID HALLUCINATIONS — 5 PROVEN METHODS

Method 1 — Prompt Engineering: Ask Questions Correctly

The first and simplest approach is to learn how to formulate queries to the model properly. How you ask determines how the model answers.

Poor prompt: "Who was the president of Ecuador?" The Good prompt: "Who was the president of Ecuador in 1950? If you're not sure, say 'I don't know.' Provide a source link for your answer if possible."

Add context: "As an experienced historian, explain..." or "Using only information available before April 2023..."

Add constraints: "Answer only based on official sources" or "Don't make up information if data isn't available."

Add verification requests: "Give me 3 ways to verify this answer" or "List sources for each fact."

Proper prompting reduces errors by 30-40%. It's not a complete solution, but a significant improvement. Methods for combating neural network hallucinations begin precisely here — with query quality.

Method 2 — RAG (Retrieval-Augmented Generation): Connect External Sources

RAG is a technology that gives AI access to external information. Instead of relying solely on model memory, the system retrieves current data and provides it to the model.

How it works:

• User asks a question
• The system searches for relevant sources (Google, your database, Wikipedia)
• The model receives the found sources plus the question
• The model generates an answer based on sources, not fabrication
• Result? Hallucinations decrease by 80-90%. That's a huge improvement.

Tools for RAG:

• LangChain — popular Python framework
• LlamaIndex — specialized for RAG
• HuggingFace — free models and solutions

RAG is particularly useful in critical sectors. Medical clinics connect AI to medical literature databases. Law firms connect to case precedent databases. Financial companies connect to financial data databases. When a neural network produces false information through RAG, it happens less frequently because it's limited to sources.

Method 3 — Fact-Checking: Verify Information After Generation

Even after receiving an answer, verify the facts. It takes time, but in critical cases it's necessary. Signs of hallucination:

• Very specific numbers without sources
• Names of people you don't recognize
• References to studies that sound too perfect
• Contradictions with known facts
• Quotes that are too eloquent

Tools for verification:

• GPTZero ($10-50/month) — detects AI authorship
• Perplexity ($20/month) — AI with built-in fact-checking
• Google Scholar — search scientific papers
• Your own verification — Google search each fact

When a model might provide incorrect data, fact-checking becomes mandatory. Never publish an AI response without verification in critical sectors.

Method 4 — Ensemble of Models: Ask Multiple AIs

Ask several different models simultaneously:

ChatGPT + Gemini + Claude = consensus

If all three models give similar answers — the information is probably correct. If they give different answers — this is an area of potential fabrication. Additional verification is required. This requires time and money (subscriptions to different services). But for critical information, it's justified. Methods for combating neural network hallucinations include precisely this multi-model approach.

When developers create systems for medicine or finance, they often use 3-5 models in parallel. Results are compared. Consensus is selected or conclusions requiring human review are identified.

Method 5 — Human Control: Final Expert Review

For critical sectors — final human review is mandatory.

Process:

• AI generates answer
• Specialist (lawyer, doctor, financier) reviews
• Only then is the result sent to the client

Cost: $50-200 per review (depends on complexity and country). When to do this:

• Medical diagnoses
• Legal documents
• Financial recommendations
• Scientific articles
• Business-critical decisions

When the cost of error exceeds the cost of verification — use human control. The NHS does this: AI helps diagnose, but the doctor makes the final decision. Law firms: AI proposes, the lawyer reviews. It's slower but safer.

Information that could potentially be dangerous cannot be published without verification. This is the golden rule.

CONCLUSIONS AND TAKEAWAYS

AI hallucinations are not a bug — they're a fundamental property of large language models. They won't disappear. They'll evolve.

But this doesn't mean you should abandon AI. On the contrary, you need to learn how to work with it. Apply a combined approach:

• Formulate queries correctly (prompt engineering)
• Use RAG for critical information
• Verify facts (at least quickly)
• For critical matters — use human review
• Document all decisions

When you apply all five methods together, hallucinations drop from 60% to 5-10%. That's an acceptable level for most tasks.

AI is a powerful tool. Use it with caution, and it will be your assistant. Trust it completely — and you'll face problems.

The golden rule: never trust AI 100% in critical sectors. Verify. Double-check. Verify through independent sources. It takes time, but that's the price of reliability. Methods for combating neural network hallucinations are not a technical problem of one company. It's a systemic challenge for the entire IT industry. And the solution requires joint efforts from developers, users, and regulators. The future of AI is not smarter hallucinations. It's properly controlled systems that know their limits and are honest about it. Until we get there, verification remains mandatory.