Have you ever encountered “plausible but wrong answers,” “nonexistent papers or statistics,” or “confidently incorrect responses” while using generative AI? This is neither a bug nor the AI lying to you.
This phenomenon is called Hallucination, and it is a fundamental problem rooted in the structural characteristics of generative AI. As of 2026, even state-of-the-art models like GPT-4o, Claude, and Gemini have not fully solved it.
This article systematically explains why AI gets things wrong, why it appears confident, and what countermeasures you should take — from the underlying technical principles to concrete mitigation strategies. This is essential reading especially if you think of AI as a “smart search engine.”
This article is a technical deep-dive into how AI works. For practical methods to improve AI accuracy through prompt writing, see our Prompt Design Guide. For the relationship between model size and performance, see Model Size Explained.
Key Points Quick Reference
| Topic | Key Point |
|---|---|
| What Is Hallucination | The phenomenon where AI generates false information as natural-sounding text |
| The True Nature of AI | Not knowledge search but a text-generation engine that “predicts the next word” |
| Why AI Gets It Wrong (4 Causes) | No knowledge retrieval, no fact-checking, probabilistic generation, always answers |
| Why AI Seems Confident | Not confidence — just generating “natural-sounding text” |
| Typical Hallucinations | Fake papers, fake APIs, fake statistics, fake laws, fake personas |
| Real-World Risk | Especially severe in legal, medical, and financial domains |
| Countermeasures (7 Methods) | Specific questions, constraints, source requests, RAG, step-by-step queries, etc. |
| Prompt Examples | Concrete prompt patterns that reduce hallucination |
| Future Outlook | Will decrease but not disappear — verification literacy is key |
| FAQ | Answers to 5 common questions |
What Is Hallucination?
Hallucination refers to the phenomenon where AI generates false information as natural-sounding text, as if it were factual. While sometimes loosely called “AI lying,” the technical reality is fundamentally different.
The most important thing to understand is that AI is not “lying.” A lie is the intentional act of stating something false while knowing the truth. AI has neither the ability to judge truth from falsehood nor the capacity for intent. What AI does is “generate the most natural-sounding text” — and the result sometimes doesn’t align with facts. That is the true nature of hallucination.
In other words:
| Human Lies | AI Hallucination | |
|---|---|---|
| Awareness of Truth | Knows the truth | Has no concept of truth |
| Intent | Deliberately deceives | No intent exists |
| Mechanism | Cognitive judgment | Probabilistic text generation |
| Result | Statement contradicting facts | Output contradicting facts |
Phrases like “AI lies” or “AI deceives” are commonly used in media, but they are technically inaccurate. AI has neither intent nor emotion. This misconception easily leads to both blind trust in AI and unwarranted fear of it, making accurate understanding crucial.
The True Nature of AI — Not Knowledge Search but Next-Token Prediction
To understand hallucination, you first need to know what AI is actually doing. Let’s clear up the common misconceptions.
The Biggest Misconception: “AI Searches Its Knowledge to Answer Questions”
This is fundamentally wrong. A search engine like Google “retrieves indexed information in response to a user query.” However, generative AI (LLM: Large Language Model) operates on an entirely different mechanism.
If we had to describe AI’s operating principle in one sentence:
“Given a context, predict and output the most natural next word, one at a time.”
This is the fundamental operation of LLMs, called Next Token Prediction. For example, given the input “The capital of Japan is,” AI calculates from its vast training data that the word “Tokyo” has the highest probability of coming next and outputs it.
| Search Engine (Google, etc.) | Generative AI (GPT, Claude, etc.) | |
|---|---|---|
| Operating Principle | Information retrieval from an index | Probability prediction of the next word |
| Information Source | Real-time web pages | Pre-trained parameters (weights) |
| Basis for Accuracy | Depends on the original information source | Depends on statistical patterns in training data |
| Freshness | Constantly crawled and updated | Fixed at training time (requires additional training) |
| Response Format | Links and snippets | Natural language text |
What Happens Technically
Inside an LLM, an architecture called the Transformer is at work. For a given input sequence (tokens), the Attention mechanism references the entire context to compute a probability distribution over “which word should come next.” One word is selected from this distribution, that word is appended to the input, and the next word is predicted — this cycle repeats to generate text.
In essence, AI is “a highly sophisticated text-completion engine” — not “a system that searches a knowledge database.” Understanding this fundamental difference is the first step to correctly grasping hallucination.
As a model’s “parameter count” increases, it can learn more complex patterns and is more likely to generate natural, accurate text. However, even with more parameters, “fact verification” is never performed, so hallucination never reaches zero. The relationship between parameter count and performance is explained in detail in this article.
Why AI Gets It Wrong — 4 Root Causes
The causes of hallucination can be classified into four main categories. Let’s examine each one from a technical perspective.
Cause 1: It Generates Patterns, Not Searches Knowledge
As explained in the previous section, AI does not retrieve facts from a knowledge database — it generates text from statistical patterns in its training data. When asked “How tall is Tokyo Tower?”, AI answers “333m” because the pattern of “Tokyo Tower,” “height,” and “333m” frequently co-occurred in its training data — not because it references Tokyo Tower’s official database.
As a result, for topics where the training data lacks sufficient information, or where multiple contradictory pieces of information exist, AI generates “plausible but inaccurate” information.
Cause 2: It Does Not Verify Truth
There is no mechanism inside AI that verifies “Is this statement factual?” What AI optimizes for is “text naturalness (likelihood),” not “alignment with facts.”
| What AI Optimizes For | What AI Does NOT Optimize For | |
|---|---|---|
| Objective Function | Next-word prediction accuracy (naturalness) | Alignment with facts |
| Evaluation Criterion | “Is this natural in context?” | “Is this true?” |
| Result | Fluent, natural text | Accuracy is not guaranteed |
In other words, “natural but wrong text” and “natural and correct text” are indistinguishable to AI. This is the most fundamental cause of hallucination.
Cause 3: Generation Is Based on Probability
AI output includes randomness (sampling). Because a word is sampled from the probability distribution each time, the same question can produce different answers. While this randomness enables “creative responses,” it also causes low-probability but inaccurate words to be selected.
A parameter called Temperature controls the degree of randomness. Setting Temperature closer to 0 produces deterministic output (always choosing the highest-probability word), which tends to reduce hallucination — but cannot completely prevent it.
Cause 4: It Is Not Designed to Say “I Don’t Know”
This is a combination of technical and design issues. LLMs are fundamentally trained to “generate some output for any given input.” While they can respond with “I don’t know,” this only happens when explicitly trained to do so.
Furthermore, from a user experience perspective, an AI that frequently responds with “I don’t know” feels unhelpful, so during RLHF (Reinforcement Learning from Human Feedback), “providing some answer” tends to be reinforced as a reward. As a result, AI speculates and answers even on topics it is uncertain about — this is the most significant practical cause of hallucination.
“The latest models don’t hallucinate” is a misconception. Even cutting-edge models in 2026 — GPT-4o, Claude 3.5, Gemini 1.5 Pro — still produce hallucinations on boundary-of-knowledge and specialized topics. While model evolution is reducing the risk, it has not reached zero.
Why AI Seems “Confident”
One of the most troublesome aspects of hallucination is that AI appears to output incorrect information “with full confidence.” Yet technically speaking, AI has no concept of “confidence.”
What exists inside AI is only probability scores (the probability of each word coming next). Both “Tokyo Tower is 333m tall” and “Tokyo Tower is 500m tall” are merely “output candidates with different probability values” to AI. The candidate with the higher probability is selected and output as natural-sounding text.
So why does it appear “confident”? Because:
・The text is fluent: Since AI optimizes for naturalness, its output is always “well-formed text”
・It uses assertive phrasing: Declarative phrasings like “It is…” and “This means…” are abundant in training data (textbooks, Wikipedia, etc.)
・It does not indicate uncertainty: Even when confidence is low, it often omits hedging phrases like “probably” or “it is possible that”
In other words, AI’s “confidence” is a result of its writing style, entirely unrelated to internal certainty. Fluency and accuracy are completely different metrics.
Adding “If you are uncertain, preface your answer with ‘I’m not fully certain, but…’” to your prompt can encourage AI to add hedging language when outputting uncertain information. It’s not perfect, but it helps prevent blind trust.
Typical Examples of AI Hallucination
Let’s catalog the patterns of hallucination that occur in real-world AI usage. Knowing “what can go wrong” in advance is the best defense against harm.
| Category | Specific Example | Danger Level |
|---|---|---|
| Fake Academic Papers | Cites papers with nonexistent author names, titles, and journal names | ★★★★★ |
| Fake APIs / Functions | Suggests nonexistent library or method names | ★★★★ |
| Fake Statistics | Generates fabricated numbers like “According to a study by X, Y%” | ★★★★★ |
| Fake Laws / Regulations | Cites nonexistent law names or article numbers | ★★★★★ |
| Fake Personas | Names fictional researchers or experts | ★★★★ |
| Fake URLs | Presents URLs to web pages that do not exist | ★★★ |
| Fact Conflation | Mixes multiple facts together, resulting in inaccurate information | ★★★★ |
| Timeline Errors | Speculates on events after the training data cutoff | ★★★★ |
Particularly dangerous is “partially correct” hallucination. Completely wrong information is easy to spot, but a response that is 80% accurate and 20% wrong can be impossible to detect without verification.
Be cautious with code examples suggested by AI as well. It can generate code that “naturally” uses nonexistent function names, parameters, or deprecated APIs. Always execute code and verify that it works.
Real-World Risk by Field
The impact of hallucination varies greatly by field. To properly assess the risk of “using AI output as-is,” let’s review the danger level by domain.
| Field | Risk Level | Specific Risk | Required Countermeasure |
|---|---|---|---|
| Medical / Health | Extremely High | Incorrect dosages, nonexistent treatment recommendations | Never use AI output directly. Expert verification is mandatory |
| Legal / Regulatory | Extremely High | Citations of nonexistent laws, incorrect case law references | Verification by legal professionals is mandatory |
| Finance / Investment | High | Incorrect financial data, citations of nonexistent regulations | Cross-checking with official data sources is mandatory |
| Academic / Research | High | Fake paper citations, incorrect experimental data | Verification of all citations against original sources is mandatory |
| Programming | Moderate | Nonexistent APIs, deprecated code | Execution testing and documentation verification |
| General Questions | Low–Moderate | Inaccurate facts, incorrect dates | Cross-checking important facts is recommended |
| Creative / Brainstorming | Low | Low impact since creativity matters more than factual accuracy | Verify only the factual claims |
The key takeaway is to treat AI output as “reference material” rather than “the final answer.” Especially in fields involving human life or legal liability, AI responses are merely a starting point — verification by experts or official sources is always required.
7 Countermeasures to Reduce Hallucination
While hallucination cannot be completely prevented, there are ways to significantly reduce it. Here are 7 countermeasures, listed in order of effectiveness.
| Countermeasure | Principle | Effect |
|---|---|---|
| ① Ask specific questions | Reduces room for AI to speculate | ★★★★★ |
| ② State constraints explicitly | Limits output scope and prevents deviation | ★★★★★ |
| ③ Allow “I don’t know” | Removes the pressure to speculate | ★★★★ |
| ④ Require sources and evidence | Suppresses unsupported claims | ★★★★ |
| ⑤ Ask step by step | Maintains accuracy by not demanding too much at once | ★★★ |
| ⑥ Use RAG (external knowledge retrieval) | Lets AI reference information beyond its training data | ★★★★★ |
| ⑦ Cross-check with multiple AIs | Compares outputs from different models | ★★★★ |
① Ask Specific Questions
Vague questions widen AI’s range of speculation and increase the probability of hallucination. “Explain the role of the Attention mechanism in GPT-4’s Transformer architecture” yields far more accurate results than “Tell me about AI.” This is also a fundamental principle of prompt design.
② State Constraints Explicitly
Including constraints like “No speculation,” “Only answer with verifiable facts,” and “Do not include information without a clear source” in your prompt encourages AI to follow them. While not foolproof, hallucination is significantly reduced compared to unconstrained queries.
③ Allow “I Don’t Know”
Explicitly stating “If you are unsure, respond with ‘This information needs to be verified’” reduces AI’s motivation to speculate. Since AI is designed to follow instructions, giving it “permission to say it doesn’t know” works effectively.
④ Require Sources and Evidence
Requesting “Please cite your sources” prompts AI to try to include references. However, be aware that the sources AI provides may themselves be hallucinations. Always verify the URLs and paper titles that AI cites.
⑤ Ask Step by Step
Requesting a large volume of information at once degrades AI’s response quality and increases hallucination. Breaking complex questions into parts and drilling down incrementally improves accuracy.
⑥ Use RAG (Retrieval Augmented Generation)
RAG is a technique where, before generating an answer, AI searches an external knowledge base (documents, databases, etc.) for relevant information and uses it as context for generation. This enables accurate responses even for up-to-date information or organization-specific knowledge not contained in AI’s training data. As of 2026, RAG is widely adopted as one of the most effective technical approaches to combating hallucination.
⑦ Cross-Check with Multiple AIs
This method involves asking the same question to different models — GPT-4o, Claude, Gemini, etc. — and checking whether their answers agree. When multiple models return the same answer, confidence is high; when answers diverge, one of them may be hallucinating.
In practice, combining these countermeasures is most effective. For example, combining just “specific questions + explicit constraints + source requests” dramatically lowers the risk of hallucination.
Prompt Examples to Reduce Hallucination
Here are concrete examples of how to apply hallucination countermeasures in actual prompts.
Bad Prompt vs. Good Prompt
| Comparison | Bad Prompt | Good Prompt |
|---|---|---|
| Question | Tell me about AI security | Explain the specific techniques and countermeasures for Prompt Injection attacks against LLMs |
| Constraints | None | No speculation. Only answer with verifiable facts |
| When Uncertain | Not specified | If uncertain, label as “Unverified information” |
| Output | Not specified | Bullet points with supporting evidence for each item |
| Hallucination Rate | High | Significantly lower |
Ready-to-Use Prompt Template
Simply adding the following rules to the beginning of your prompt can effectively reduce hallucination:
・“If you are unsure, respond with ‘This point needs to be verified.’”
・“Speculation and guessing are prohibited. Only answer with verifiable facts.”
・“Do not use statistical data without a clear source.”
・“Indicate the confidence level of each piece of information as ‘High / Medium / Low.’”
Since AI is designed to follow instructions, simply stating these constraints can dramatically improve output quality. For more advanced prompt design techniques, see our Prompt Design Guide.
Will Hallucination Disappear in the Future?
The short answer: Hallucination will decrease but will not completely disappear.
Why It Will Decrease
・Model evolution: Increasing parameter counts and improved training methods are enhancing factual reproduction accuracy
・Advances in RLHF / DPO: Fine-tuning through human feedback is becoming more refined, improving the “hedge when uncertain” behavior
・Adoption of RAG: Integration with external knowledge bases enables accurate reference to information beyond training data
・Built-in fact-checking mechanisms: Systems for automatically post-verifying outputs are being researched and implemented
Why It Will Not Disappear
・Fundamental limitation: As long as LLMs remain probabilistic text-generation models, “100% factual guarantee” is theoretically impossible
・Knowledge boundaries: Continuously covering all human knowledge is impossible — knowledge gaps will always exist
・Ambiguous problems: For questions with no single correct answer (value judgments, predictions, interpretations), defining what is “accurate” is itself difficult
・Training data bias: Internet data contains misinformation and biases that cannot be completely eliminated
The right approach to hallucination is not “waiting for AI’s flaws to be fixed” but rather “developing the literacy to properly evaluate and verify AI output.” This will become one of the most important skills in the AI era.
The expectation that “verification will become unnecessary as AI improves” is dangerous. Even a 99% accurate AI producing an error 1 in 100 times would cause serious problems in medical or legal contexts. The habit of verification should never be abandoned, no matter how advanced AI becomes.
Frequently Asked Questions (FAQ)
Q: Is AI lying?
No. AI has neither the intent nor the ability to lie. A lie is the deliberate act of stating something false while knowing the truth, but AI lacks the very function of judging truth from falsehood. AI’s goal is to “generate the most natural text,” and the fact that the result sometimes contradicts reality — that is hallucination.
Q: Does AI “understand” the question?
Not in the way humans understand. AI has statistically learned patterns and relationships in text and does not experientially grasp “meaning” the way humans do. However, it is true that through highly advanced pattern recognition, it can exhibit behavior that appears as if it “understands.”
Q: Can I trust AI’s answers?
AI is extremely useful as reference material, but it is not recommended to use it as-is for final decision-making. Especially in situations where factual accuracy is critical (medical, legal, financial, academic), always verify AI output against primary sources. On the other hand, for brainstorming, drafting text, and programming assistance, AI can be used very effectively when you understand the hallucination risk.
Q: Can hallucination be completely prevented?
Complete prevention is currently impossible. However, by combining the 7 countermeasures introduced in this article (specific questions, explicit constraints, allowing “I don’t know,” requiring sources, step-by-step queries, RAG, cross-checking), you can dramatically reduce the risk.
Q: Are there topics where hallucination is more likely?
Yes. Hallucination tends to occur more frequently on the following topics: recent events (after the training data cutoff), niche specialized knowledge (scarce in training data), numerical data (statistics, dates, quantities), detailed information about people (biographies, achievements), and URLs / references (specific links and paper titles). Particularly careful verification is required for these topics.
Conclusion
AI hallucination is not a “bug” — it is a structural characteristic inherent to the mechanism of probabilistic text generation. Here is a summary of this article’s key points.
・AI is not “searching knowledge” but “predicting the next word”
・AI output optimizes for “naturalness,” not “correctness”
・AI’s “confidence” is a result of its writing style, unrelated to internal certainty
・Hallucination arises from 4 root causes (no knowledge retrieval, no fact-checking, probabilistic generation, always answers)
・Combining the 7 countermeasures dramatically reduces the risk
・Since complete elimination is unlikely even in the future, verification literacy is paramount
AI output is “reference material,” not “the final answer” — maintaining this mindset is the most effective way to leverage AI in the AI era.
Related articles: Prompt Design to Improve AI Accuracy / What Is Generative AI Model Size? / How to Detect AI-Generated Videos and Deepfakes
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