Why Do LLMs Hallucinate? A Beginner’s Guide to Safer, More Accurate AI
If you’ve ever asked an AI a simple question and got a confident, wrong answer, you’ve met an LLM hallucination. It feels smart. It sounds right. It still isn’t true. This guide explains why that happens in plain language, how to spot it, and what you can do to reduce it, today.
First, what an LLM actually is
A large language model (LLM) is a text generator trained on a huge library of text. It learns patterns in how words and ideas usually appear together, then writes by predicting the next bit of text (the “next token”) over and over.
A helpful mental model: imagine millions of tiny knobs inside the model. During training, those knobs are adjusted so the model gets better at guessing the next word from the words before it. That’s the whole trick. There’s no built‑in database of truth, just an ability to continue text in a way that usually matches what people write.
For a gentle, visual walk‑through of the underlying “Transformer” architecture many LLMs use, see The Illustrated Transformer.
Why “sounds right” isn’t “is right”
Because LLMs are trained to produce likely text, they sometimes produce things that are likely-sounding but false. Fluency and truth are different goals.
Likely sentence: “In 1895, Nikola Tesla unveiled the first personal radio broadcast to a crowd in New York.”
Reality: Well‑written, but wrong. Early radio milestones came later and involved other people.
If “a likely sentence” and “a true sentence” diverge, the model will favor the likely one. That’s a hallucination: text that is plausible and confident but not supported by facts or by the provided context.
Why this matters
Confidence plus inaccuracy is risky. In business, that might mean invented pricing for a competitor. In law, fake case citations. In healthcare, fabricated dosages. In 2023, a lawyer submitted a filing that cited non‑existent cases generated by an AI; the court sanctioned the attorneys (see Reuters coverage).
Trust is hard to win and easy to lose. If you use LLMs for work, you need techniques that make them safer and more factual.
The core reasons LLMs hallucinate
They predict plausible sequences, not facts. Training maximizes “likelihood of text,” not “truth about the world.” Even on perfect data, next‑token prediction can pick a smooth but wrong continuation.
They learn our incentives. Many benchmarks and casual uses reward delivering an answer over saying “I don’t know,” so models learn to guess unless explicitly told not to.
Small errors snowball. Generation is a loop: pick the next token, add it, repeat. A tiny early mistake can steer the rest of the answer off course.
Data issues make it worse
Garbage in, garbage out. The open internet contains rumors, spam, and old or biased information. Models can internalize it.
Long‑tail blind spots. Rare facts, niche topics, and events after a model’s training cutoff are empty spaces. Without a source, the model fills the gap with the most plausible guess.
Conflicting sources. When training data disagrees, the model may average conflicting narratives into a neat, wrong summary.
Decoding knobs: temperature and sampling, simply explained
When the model chooses the next word, it can play it safe or take risks.
Temperature: lower values make the model more predictable; higher values make it more creative. For factual tasks, keep temperature low.
Sampling (top‑k, top‑p): these methods limit how many candidate words are considered. Tighter limits reduce drift; looser limits increase variety.
If you control these settings, turn the creativity down for anything that must be accurate. For a short explainer, see OpenAI’s docs at Parameter Details.
Prompts shape behavior more than you think
Vague prompts invite invention. Clear prompts reduce it. Long, meandering chats can accumulate subtle drift and forget earlier instructions. Be explicit about goals, limits, and sources. Accuracy improves when you add a direct instruction such as “Don’t guess. If you’re not sure, say you don’t know.”
Two kinds of hallucinations you should know
Factuality errors
The output conflicts with real‑world facts.
Faithfulness errors
The output conflicts with the source you provided (for example, a summary flips “approved” to “rejected”).
When evaluating an answer, ask: is it true, and is it faithful to the input?
How to spot hallucinations fast
Watch for specific claims with no source. Named numbers, dates, or citations presented confidently but unsupported are red flags.
Look for phantom references. Phrases like “as the article says” when the article doesn’t actually say it.
Notice irrelevant confident details. Extra facts that feel oddly specific but aren’t verifiable from the provided context.
Quick context check. If an answer can’t be verified by a brief scan of the input or a trusted source, treat it skeptically. When stakes are high, trace every important claim to a trusted source: no source, no trust.
The single most effective fix: ground answers in real text
Grounding means giving the model the exact passages it should use before it answers. Retrieval‑Augmented Generation (RAG) automates this: it searches a knowledge base or the web, retrieves relevant snippets, and feeds them in as context. The model then writes “with receipts,” narrowing its search space to what you supplied.
Think of it like an open‑book exam: if the book is relevant and on the desk, the student is less likely to make stuff up. The original RAG paper by Facebook AI (now Meta) is a helpful reference: RAG paper.
How it works, simply:
First: You ask a question.
Second: The system searches your docs (or the web) and grabs the top few passages.
Third: The model answers using only those passages, and ideally quotes them.
Quality retrieval is everything. If retrieval is off‑topic, the model is still flying blind.
Should AI say “I don’t know”?
Yes, especially in medical, legal, or financial use. A clear, humble “I don’t know based on the provided information” is better than a fluent fiction. In your apps, treat abstention as a first‑class outcome: set confidence thresholds, reward safe non‑answers, and log guesses for review.
Practical ways to reduce hallucinations today
Ground answers with RAG and quote the lines used.
Use clear guardrails in prompts: scope, sources, and when to abstain.
Lower temperature for factual work.
Keep chats short and focused; reset context for new tasks.
Monitor and evaluate with both factuality and faithfulness in mind; red‑team before launch.
Encourage “I don’t know” when evidence is missing.
A quick exercise to build your instincts
Pick a narrow question with a known answer (for example, “Who approved the first Ebola vaccine and when?”).
Ask your model two ways. First, open‑ended: “Answer briefly.” Second, grounded: “Use only the text below. If unknown, say you don’t know.” Then paste a short article.
Compare the two results. Circle any extra claims, numbers, or names not in the source. Verify against a trusted site. Then re‑ask with: “Quote exact lines. No outside facts.” Lower temperature if you can. Watch the hallucinations drop.
Copy‑paste prompts you can ship
You are a precise research assistant.
Use only the provided context. Do not use outside knowledge.
If the context is insufficient, say: “I don’t know based on the provided information.”
Cite exact lines in brackets [quote].
No speculation. No unverifiable claims.
Keep answers under 120 words.
End with a 1-line confidence (High/Medium/Low).
Simple user prompt pattern:
Context: paste the exact text or bullet points you want the model to use.
Question: your question.
Required: Provide at most three short points, each with a citation to the exact line.
A five‑point grounding checklist
1. Is retrieval on, and are the top results obviously relevant?
2. Do you see verbatim quotes tying claims to text?
3. Are numbers, names, and dates all cited?
4. If a fact is missing, does the model abstain?
5. If drift appears, lower temperature and re‑run.
Learn more and go deeper
Retrieval‑Augmented Generation (original paper): https://arxiv.org/abs/2005.11401
TruthfulQA (how models mimic human falsehoods): https://arxiv.org/abs/2109.07958
SelfCheckGPT (consistency‑based hallucination detection): https://arxiv.org/abs/2303.08896
OWASP Top 10 for LLM Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
The Illustrated Transformer (beginner‑friendly visuals): https://jalammar.github.io/illustrated-transformer/
Key takeaways
Why hallucinations happen: LLMs predict plausible text, not truth; small generation errors compound; data can be wrong or missing; prompts and decoding settings matter.
What works to reduce them: strong grounding (RAG), explicit prompts with abstention rules, lower temperature for factual tasks, quotes and citations, short focused chats, and ongoing monitoring.
Golden rule: Always trace claims to a source you trust. No source, no trust.
Ready to reduce hallucinations this week?
Ship a safer prompt: drop the 7‑line system prompt into your assistant now.
Run a 15‑minute RAG sanity test: pick five real queries, force citations, and measure abstentions.
Close the loop: add a “Verify with sources” button and log uncited claims for review.
Frequently Asked Questions
What is an LLM hallucination?
It’s when an AI text sounds plausible but is untrue, irrelevant, or not supported by the input.
Why do LLMs often give believable but wrong answers?
Because they optimize for fluent, plausible text and predicting the next token, not for truth. Fluency isn’t the same as factual accuracy.
How can training data cause hallucinations?
Bad data, biases, and myths from the internet can be learned and repeated. Gaps, outdated info, or private data can also lead to invented responses.
What role do decoding settings play in hallucinations?
Higher temperature or more varied sampling increases creativity and risk of drift; lower temperature tends to improve factuality by narrowing choices.
What’s the difference between intrinsic and extrinsic hallucinations?
Intrinsic: the output contradicts the provided text. Extrinsic: it adds information not in the source and not verifiable from it.
How can prompts and chats trigger errors, and how can I reduce them?
Ambiguity and long, unconstrained chats invite invention. Be explicit about goals, limits, and definitions; use grounding or retrieved sources; keep conversations focused.
What is Retrieval-Augmented Generation (RAG) and how does it help?
RAG searches a knowledge base or the web to retrieve relevant passages and uses them to answer, which grounds the model and reduces hallucinations.
Should LLMs say “I don’t know”? When is abstaining better?
In high-stakes settings, saying “I don’t know based on the evidence” is safer. Some systems penalize abstaining, so approaches like confidence thresholds can help balance trust and usefulness.
What practical steps can I take to reduce hallucinations when prompting?
Be explicit about goals and sources; instruct the model to ground answers in provided information; require citations and exact lines; avoid guessing; consider lowering temperature.
Where can I learn more, and what quick-start actions can I try?
Resources include RAG papers, TruthfulQA, SelfCheckGPT, and guides on grounding. Quick-start ideas: use a system prompt that mandates sourcing, run a short RAG sanity test, and add a “Verify with sources” step for outputs.