Hallucinations

By the Syncfusion DevTutorials team · Last updated October 20, 2026

Why LLMs make up facts that sound right, how to spot low-confidence tokens, and how to reduce hallucinations with given context, permission to say I don't know, and checks.

Ask a small model "Who wrote the book The Silent Orchard?" There is no such book. But the model might still reply with a name, a year, and a short summary, all written in a calm and confident way.

That is called a hallucination: text that sounds right but is not true. This lesson will show you why this happens, how to notice it, and how to make it less likely.

The main idea: a model picks words that look likely, not facts that have been checked. When it does not know, it still writes the most likely-sounding answer.

A Real-World Example

Picture a student taking an exam who did not study one topic.

The question asks for a date. The student does not know it. Leaving it blank feels bad, so they write down a date that "sounds about right". They write it neatly and with confidence. The teacher cannot tell from the writing that it was just a guess.

A language model works in a very similar way.

• The exam question is your prompt.
• Not knowing the answer means the training text has no strong pattern for this question.
• Writing a date that sounds right means the model picks the most likely tokens.
• Neat handwriting is the model's smooth, confident style.

In simple words: a hallucination is a confident guess written as if it were a fact.

What You Will Learn

In this lesson, you will learn:

1. What a hallucination is.
2. Why LLMs hallucinate.
3. Which questions are most likely to cause one.
4. How to spot unsure tokens in a reply.
5. How to reduce hallucinations.
6. How to check answers before you trust them.

Why Models Hallucinate

You already know the reasons from earlier lessons.

• It predicts text, not truth. The model picks the next token that fits the pattern. A wrong fact that fits the pattern looks just as good to it.
• It must always write something. Every step gives some token the highest chance. There is no built-in "I don't know" step.
• It has no live lookup. A plain model cannot check a fact. It only has patterns from training.
• Its knowledge has a cutoff. Anything after the cutoff is unknown.
• Small models know less. A 0.5B model has far fewer patterns than a large one.

Which Questions Cause Hallucinations?

Some questions are much riskier than others.

Common, well-known facts are much safer, because the model saw them many times.

Spotting Unsure Tokens

A hallucination often shows up as tokens with low confidence: the model picked them, but many other tokens were almost as likely. You can flag these tokens.

Flag tokens the model was unsure about

const reply = [
  { token: "The", chance: 0.91 },
  { token: " book", chance: 0.88 },
  { token: " was", chance: 0.95 },
  { token: " written", chance: 0.83 },
  { token: " by", chance: 0.97 },
  { token: " Laura", chance: 0.09 },
  { token: " Fenwick", chance: 0.05 },
  { token: " in", chance: 0.71 },
  { token: " 1987", chance: 0.12 },
];

const flagged = reply.filter((t) => t.chance < 0.3).map((t) => t.token.trim());
console.log("Unsure tokens:", flagged.join(", "));

Expected output:

Unsure tokens: Laura, Fenwick, 1987

The grammar words are all sure. The name and the year, the actual facts, are the unsure ones. These example chances show the usual pattern: the model is sure about how to say it, and unsure about what to say.

In simple words: low chances on the key facts are a warning sign.

A Confidence Score for the Whole Reply

You can turn the chances into one number for the whole reply, such as the chance of the weakest key token.

The weakest token decides

function weakest(reply) {
  return reply.reduce((min, t) => (t.chance < min.chance ? t : min));
}

const known = [
  { token: " Paris", chance: 0.86 },
  { token: " is", chance: 0.97 },
  { token: " the", chance: 0.98 },
  { token: " capital", chance: 0.93 },
];
const guessed = [
  { token: " Laura", chance: 0.09 },
  { token: " Fenwick", chance: 0.05 },
  { token: " wrote", chance: 0.6 },
];

console.log("Known fact, weakest token:", weakest(known).token.trim(), weakest(known).chance);
console.log("Guess, weakest token:     ", weakest(guessed).token.trim(), weakest(guessed).chance);

Expected output:

Known fact, weakest token: Paris 0.86
Guess, weakest token:      Fenwick 0.05

A low score does not prove an answer is wrong, and a high score does not prove it is right. A model can be confidently wrong about a common mistake. But a low score tells you where to look first.

Reducing Hallucinations

1. Give the Model the Facts

The most powerful fix is to put the facts into the prompt. When the answer is in the text the model reads, it can copy it instead of guessing. This is called grounding. It is also the main idea behind RAG, retrieval augmented generation, which you will meet in a later course.

Answer only from the given facts

const facts = {
  "release date": "The new grid control ships on 12 March.",
  "price": "The license costs 995 dollars per developer.",
};

function answer(question) {
  for (const topic in facts) {
    if (question.toLowerCase().includes(topic)) return facts[topic];
  }
  return "I don't know. That is not in the documents I was given.";
}

console.log(answer("What is the release date?"));
console.log(answer("What is the price?"));
console.log(answer("Who is the lead developer?"));

Expected output:

The new grid control ships on 12 March.
The license costs 995 dollars per developer.
I don't know. That is not in the documents I was given.

This code is a simple stand-in for what a well-instructed model does: answer from the given text, and say "I don't know" when the answer is not there.

2. Allow "I Don't Know"

Tell the model, in the system message, that "I don't know" is a good answer. Without that, it tends to answer every question.

A system message that allows I don't know

const messages = [
  {
    role: "system",
    content: "Answer only from the facts below. If the answer is not in the facts, say: I don't know.\n\nFacts: The office opens at 9 am.",
  },
  { role: "user", content: "When does the office close?" },
];

console.log(messages[0].content);
console.log("---");
console.log("Question:", messages[1].content);
console.log("A good reply: I don't know.");

Expected output:

Answer only from the facts below. If the answer is not in the facts, say: I don't know.

Facts: The office opens at 9 am.
---
Question: When does the office close?
A good reply: I don't know.

3. Use a Low Temperature for Facts

High temperature makes unlikely tokens more likely, and that includes made-up names and numbers. For facts, use a temperature of 0 to 0.3.

4. Check the Answer

Check important claims against a trusted source, or with code. Even a simple check catches many errors.

Check numbers in a reply against the source

const source = "The report lists 3 offices: Chennai, Berlin, and Austin.";
const reply = "The company has 4 offices, in Chennai, Berlin, Austin, and Tokyo.";

const sourceNumbers = source.match(/\d+/g) ?? [];
const replyNumbers = reply.match(/\d+/g) ?? [];
const unsupported = replyNumbers.filter((n) => !sourceNumbers.includes(n));

console.log("Numbers in the reply that are not in the source:", unsupported.join(", ") || "none");

Expected output:

Numbers in the reply that are not in the source: 4

The reply says 4 offices, but the source only supports 3. A one-line check found the hallucination.

Try It in the AI Sandbox

Ask the model about something that does not exist. Then ask about a common fact, and compare the token colours.

Then try these in the Chat tab:

• Ask the same question with a system message that allows "I don't know". Does it help this small model?
• Paste a short paragraph with the answer into your message, and ask again.
• Try a bigger model from the model picker, and compare.

Common mistakes

Exercises

Each exercise ends with a value on the last line. Press Check to compare it with the expected answer.

What to Remember

• A hallucination is a confident answer that is not true.
• Models pick likely words, not checked facts, and they always write something.
• Risky questions: rare facts, exact numbers, made-up things, citations, recent events.
• Low token chances on key facts are a warning sign, but not proof.
• Grounding, putting the facts in the prompt, is the strongest fix.
• Allow "I don't know", use a low temperature, and check important claims.

Next: JSON Validation, how to get structured data from a model and check that it is valid.