Grounding
AI Agents
Grounding means basing an AI model’s answers on specific, checkable sources, like your documents or search results, not just its training data.
AI Fundamentals
What Is an AI Hallucination?
An AI hallucination is output from an AI model that sounds confident and plausible but is false, invented, or not supported by the information it was given or by any reliable source. Examples include made-up citations, wrong figures, and nonexistent policies. Hallucinations happen largely because models generate likely-sounding text from learned patterns instead of checking each claim, and training often rewards a confident guess over admitting uncertainty. They can be reduced, not eliminated.
Also called hallucination, confabulation
Example
A small bookkeeping firm uses an AI assistant to draft a reply to a client who asks whether they can deduct part of their home internet bill. The draft reads well. It names a specific section of the tax code and states a yearly deduction limit, both with complete confidence.
Before sending, a senior bookkeeper checks the citation. The section number covers an unrelated topic, and the limit doesn’t appear in any official guidance she can find. The assistant had produced details that sounded right but were invented.
The firm changes its process. The assistant now drafts from the firm’s approved reference documents, every citation gets checked against the original source, and a qualified staff member reviews any tax answer before it reaches a client.
Why AI hallucinations happen
A large language model works by predicting the next piece of text based on patterns it learned during training. It is built to produce responses that sound fluent and fitting, not to check each claim against a trusted record. Most of the time those patterns line up with the facts. When they don’t, the model can still produce an answer that sounds just as sure.
A few conditions make hallucinations more likely:
- Gaps in knowledge. The model may never have seen the information, or its training data may be out of date. Instead of saying “I don’t know,” it may fill the gap with something plausible, partly because training and common benchmarks tend to reward a confident guess over admitting uncertainty.
- Requests for precise details. Citations, case numbers, URLs, statistics, and exact quotes have a recognizable shape that a model can imitate even when it has no real source.
- Ambiguous or leading prompts. A vague question, or one built on a false premise, invites the model to guess or to go along with the premise.
- Long or complex tasks. Summarizing many documents or chaining several steps creates more places for small errors to slip in.
Where hallucinations show up at work
Hallucinations matter most when AI output is treated as finished work. Common examples include:
- A legal or compliance summary that cites a regulation, case, or contract clause that doesn’t exist.
- A report or slide with figures that don’t match the source spreadsheet.
- A customer service reply that describes a refund policy, warranty, or discount your company doesn’t offer.
- Marketing copy that attributes a quote to a real person or claims a product feature you don’t have.
- A research summary with links or article titles that lead nowhere.
The risk is not only that the information is wrong. Hallucinated content usually looks just as polished as accurate content, so it can pass a quick skim. Once it reaches a client, a regulator, or the public, it can create legal exposure, damage trust, or lead someone to act on bad information. The stakes depend on the task: an invented detail in a brainstorm is easy to fix, while a wrong answer in a contract or tax filing is not.
How to reduce hallucinations
You can lower the risk with a few practical habits:
- Ground answers in your own material. Give the model the documents it should use, or connect it to your company’s knowledge base, so it answers from them rather than from memory alone. This is called grounding, and retrieval-augmented generation (RAG) is a common way to do it.
- Ask for sources, then check them. Request citations or quoted passages, and check each one against the original source yourself. A citation the model provides can itself be invented.
- Narrow the task. Specific, well-scoped requests leave less room to guess. Clear prompting helps too: for example, tell the model to say when it doesn’t know or when the material you provided doesn’t answer the question.
- Match review to the stakes. For legal, financial, medical, or customer-facing output, build in a human-in-the-loop review step so a qualified person approves the result before anyone relies on it.
Why hallucinations can’t be fully eliminated
No current technique removes hallucinations entirely. Grounding a model in your documents helps, but it can still misread a passage, blend two sources, or add a detail the documents don’t contain. AI tools that search the web can cite a real page and still misstate what it says. Model improvements can lower error rates on some tasks, but no model is error-free.
A few points to keep in mind:
- Partial errors are the hardest to catch. A hallucination is often one invented name, date, or figure inside an otherwise accurate answer, so checking only the overall gist can miss it.
- Self-checks are limited. Asking “Are you sure?” can help a model catch some errors, but it can also lead it to abandon a correct answer or defend a wrong one.
- Reliability varies. Error rates differ by model, task, and topic, so a tool that drafts emails well may be unreliable on niche technical or legal questions.
The practical approach is to decide in advance which outputs need checking, who checks them, and against what source.
Further reading
- AI Training & SpeakingUseful if you’re deciding how your team should use and check AI output and want to explore SN3’s team training or custom workshops.
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