Short answer. LLMs hallucinate because they predict the most likely next words, not the most truthful ones, so a model that does not know an answer confidently invents one — as Air Canada learned when a tribunal held it liable for its chatbot's invented refund policy. Enterprises cut hallucinations by grounding answers in trusted data (RAG), showing sources, adding automated guardrails, and keeping a human in the loop for high-stakes decisions.
Key takeaways
- Grounding an AI's answers in trusted, current data through Retrieval-Augmented Generation (RAG) reduces hallucinations compared with letting a model answer from memory alone.
- Showing the source behind every answer and running automated groundedness checks lets a team catch and rewrite unsupported claims before a customer sees them.
- A human in the loop for high-stakes financial, legal, medical, or customer-facing decisions remains the most reliable safeguard against a wrong answer reaching a customer.
- Hallucination is fundamentally a data-quality problem: a model can only cite facts it has actually been given, so messy or missing data produces bad answers.
- A 2024 Stanford study of legal AI found general-purpose models hallucinated on 58% to 88% of specific legal queries, and even purpose-built legal AI tools were wrong on 17% to 33% of queries.
What is an AI hallucination, and why do chatbots make things up?
An AI hallucination is a confident, fluent answer that is factually wrong or entirely invented, produced without the system flagging any doubt. In November 2022, Jake Moffatt booked a last-minute flight after his grandmother died and asked Air Canada's chatbot about bereavement discounts. It told him he could claim the reduced fare within 90 days of travelling. That was false: the airline had no such policy, refused the refund, and argued it was not responsible for its own chatbot. A tribunal disagreed, and Air Canada was ordered to pay.
The chatbot had not been hacked. It had simply produced a confident, well-written, completely invented answer.
An LLM, or Large Language Model, is the engine behind ChatGPT and most AI assistants. A Large Language Model is a system trained on enormous amounts of text that predicts the next few words in a sequence rather than retrieving a verified fact. Chain those predictions together and you get fluent answers, summaries, and emails — but the model produces the most likely-sounding continuation, not the most truthful one. When it does not know, it does not pause and say "I'm not sure." It fills the gap with something plausible that has never been true.
IBM defines a hallucination as an AI confidently presenting incorrect or invented information as fact. OpenAI's researchers add a memorable reason: it is like a student in an exam that never rewards "I don't know." The smart move is to guess, so the model learns to guess.
Why do hallucinations matter for business?
A wrong answer that is merely an annoyance for a casual user becomes legal and financial exposure for an enterprise. The Air Canada case showed a company can be held legally responsible for what its AI tells a customer, and courts have separately sanctioned lawyers who filed documents citing cases an AI invented.
The research is blunt about scale. A 2024 Stanford study of over 800,000 legal questions found general-purpose models hallucinated on 69% to 88% of specific legal queries. On questions about a court's core holding, the rate was at least 75%. Even GPT-4 hallucinated 58% of the time.
Purpose-built tools do better, but not by enough. A follow-up Stanford study in the Journal of Empirical Legal Studies found leading legal AI products still hallucinate on 17% to 33% of queries, with accuracy ranging from 65% for the best, to 41% and then 19% for the others. These are tools sold specifically for a high-stakes profession, and buyers comparing alternatives often start from a list such as LLM training data annotation companies rather than take a single vendor's accuracy claim on trust.
In McKinsey's 2025 global survey, 51% of organizations using AI reported at least one negative consequence, and inaccuracy was the risk most often tied to real harm. Trust is fragile too: in a study of over 48,000 people across 47 countries by KPMG and the University of Melbourne, only 46% said they were willing to trust AI. Yet 66% use it regularly, and 70% want stronger regulation.
Why is reducing hallucinations harder than it looks?
You cannot fully delete the behaviour, because guessing is built into how language models work, so no single setting removes it. Even the providers describe hallucination as a stubborn, ongoing challenge. The realistic goal is to keep it rare, catch it, and never let it reach a customer unchecked.
The model rarely knows your business, because a general AI has read the public internet but never your latest pricing, policy, or contract, so it improvises. IBM, Google, and AWS all note that this gap must be closed with your own trusted data. AI-ready data is also rarer than buyers assume, and many enterprises still describe their own data as not ready for AI use. Messy data in means bad answers out, so accuracy is a data problem long before it is a model problem.
How do you stop LLM hallucinations?
The most effective approach is not a secret algorithm but a discipline the largest tech companies now agree on, called grounding. Grounding means never letting a model answer from memory alone — instead handing it the relevant, trusted facts at the moment of the question and telling it to answer only from those facts.
The common method is Retrieval-Augmented Generation, or RAG. RAG is a technique where a system retrieves the right documents from a knowledge base before the model answers, then asks it to generate a reply grounded in those documents. AWS calls RAG a pragmatic way to give an enterprise model accurate context. Google frames it as supplying the facts and grounding the answer on them. NVIDIA notes it reduces the chance of a plausible-but-wrong answer. IBM is candid that RAG lowers the risk of hallucination without making a model error-proof.
A grounded enterprise system has a recognisable shape. A knowledge base holds your trusted documents. An orchestrator fetches facts, then combines question and facts. The language model writes a draft answer. A guardrail checks whether the draft is backed by the facts. A grounded answer is delivered with a source you can verify. A failed check is fixed, flagged, or routed to a human.
The question never goes straight to the model. First the system pulls the right facts from your documents, then hands them to the model with the question, and finally checks the draft against those facts before anyone sees it. Grounding is the foundation, and the strongest systems add two more layers. Guardrails are automated checks that flag or rewrite any part of a model's answer that is not supported by the retrieved sources. Microsoft's Azure groundedness detection flags any part of an answer not supported by the sources, and can rewrite it before the user sees it. NVIDIA's open-source NeMo Guardrails adds fact-checking rails to RAG systems. The second layer is the most reliable safeguard of all: a human in the loop for high-stakes answers, the discipline covered in a complete guide to human-in-the-loop machine learning.
What belongs in an enterprise accuracy playbook?
An enterprise accuracy playbook has four parts that reinforce each other: ground the answer, show the source, add a human check, and start with clean data. Teams building this out often pair it with enterprise evaluation benchmarks that measure whether the guardrails are actually catching errors.
- Ground every answer. Connect the AI to trusted, current data with RAG so it answers from facts, not memory.
- Show the source and add guardrails. Make the AI cite where each answer came from, and use automated checks to flag or rewrite unsupported claims.
- Keep a human in the loop. For money, legal, medical, or customer-facing decisions, a person reviews before it ships.
- Start with the data. Clean, well-labeled, AI-ready data is the input that makes everything above work.
How does Lifewood turn data-driven accuracy into reality?
Trustworthy AI is built on trustworthy data, and that is where Lifewood works. Founded in 2004 and refocused as an AI-data specialist, the company now operates across 40+ delivery centres in 30+ countries. It combines a worldwide human workforce with an industrialized methodology and its proprietary LiFT platform.
Lifewood supports AI-generated content at both ends: preparing the high-quality data that makes generated output accurate, and applying full-time human-in-the-loop quality control so the output holds up.
- Data collection. Multilingual, multi-modal gathering across text, audio, image, and video in 50+ languages.
- Annotation and labeling. Labeling, tagging, transcription, and sentiment analysis — structured truth checked through gold sets, audit sampling, and consensus QA.
- LLM training data. Supervised fine-tuning sets, human-preference (RLHF) data, and model-evaluation datasets built through enterprise LLM training data programs.
- AIGC and QA. Enterprise AI-generated content, including video at scale, backed by full-time human review and data validation.
Hallucinations are usually a data-quality problem, not only a model problem. Lifewood's specialists attack the problem at its source: structuring and validating the documents a RAG system retrieves from; building industry- and language-specific datasets plus evaluation sets that teach models to ground answers and admit uncertainty, an approach the wider industry increasingly benchmarks against lists like top LLM training data providers; and providing a global workforce as the verification layer that catches errors automated checks miss, in 50+ languages.
The pipeline runs from collection, to annotation, to training and evaluation, to human QA. Those stages feed a grounded knowledge base, which supports an assistant answering from verified facts. When the AI does slip, errors flow back into the data process, so the next version is more accurate.
What comes next for enterprise AI accuracy?
Two shifts are shaping the next phase of enterprise accuracy work: AI supervising AI, and a growing focus on the data itself rather than the model.
A fast-emerging idea is guardian agents — AI that supervises other AI, reviewing, monitoring, and blocking another model's risky outputs before they reach a person. Alongside that, attention is shifting from flashy models to the unglamorous foundation of clean, well-governed data, since many organizations still describe their own data as not AI-ready. The organizations that invest there will quietly pull ahead.
What is the bottom line?
Hallucinations are not a sign that AI is broken, but a predictable and manageable feature of how language models work. The strategy is broadly agreed by McKinsey, KPMG, OpenAI, Google, AWS, IBM, NVIDIA, and Microsoft alike: ground the model in trusted data, show the source, add guardrails, keep a human in the loop, and govern the whole thing.
Underneath every step sits the same requirement: high-quality, well-prepared, AI-ready data. With more than half of organizations reporting at least one negative AI consequence in McKinsey's 2025 survey, that is where the work begins. The breakthrough is not a cleverer model, but better data, handled with care. You cannot prompt your way out of a data problem — accuracy is built upstream, in the data, long before the model ever speaks.