AI Hallucinations: How to Get More Reliable Answers

Generative AI can produce clear reports, emails, research summaries and recommendations in seconds. The difficulty is that a fluent answer can contain an invented fact, a false quotation, an incorrect legal reference or a source that does not support the claim being made. This is commonly described as an AI hallucination.

Better prompts can help, but prompting alone does not make an answer reliable. The strongest approach combines a clearly defined task, trustworthy source material, explicit permission for the AI to say it is uncertain, claim-by-claim verification and human approval proportionate to the risk. AI should accelerate the work while evidence and accountability remain with the business.

Last checked: 28 July 2026. AI product features, browsing tools and reliability behaviour change frequently — always confirm current capabilities against the provider’s own documentation.

What is an AI hallucination?

An AI hallucination occurs when a generative AI system produces content that is incorrect, fabricated or unsupported while presenting it in a convincing form. Examples may include:

• An invented statistic
• A quotation that was never said
• A fictional court case
• An incorrect regulation
• A non-existent product feature
• An invented software command
• A source that does not exist
• A real source that does not support the stated claim
• A fabricated event or meeting
• A false summary of an uploaded document
• An incorrect answer built around a false assumption in the question

A confident tone is a writing characteristic, not evidence of correctness. The US National Institute of Standards and Technology, in its Generative AI risk profile, describes this behaviour as “confabulation” — confidently stated but erroneous or false content that can mislead or deceive users.

Why can AI sound certain when it is wrong?

Generative AI creates responses by predicting and constructing suitable language from its training, the user’s instructions, supplied documents, retrieved information, available tools and the conversation context. It is designed to provide a useful response. When information is missing, ambiguous, contradictory, obscure, very recent, highly specialised or based on a false premise, the system may still produce a plausible continuation rather than stop.

Newer models and better tools can improve reliability, but no general-purpose AI system should be treated as an infallible authority. This is not a mysterious malfunction — it is a predictable consequence of how these systems generate language. They do not automatically verify every sentence against an authoritative database before responding.

Error, hallucination or outdated information?

Not every mistake is technically a hallucination, and the remedy depends on the type of error:

The remedy depends on the type of error. A better prompt cannot repair an unreliable source, and a citation does not help if the cited page does not support the claim.

Where is the business risk highest?

The level of verification should rise with potential harm, financial value, legal significance, public visibility, irreversibility, sensitivity of the information and the effect on customers, staff or third parties.

Lower-risk uses

• Brainstorming
• Rewriting supplied text
• Creating meeting-agenda templates
• Changing tone
• Formatting
• Generating draft headings
• Summarising non-critical internal notes that will be checked
• Producing ideas for human review

Medium-risk uses

• Customer emails
• Marketing claims
• Competitor research
• Product comparisons
• Internal procedures
• Management reports
• Tender drafts
• Website copy
• Training materials
• Meeting summaries

Higher-risk uses

• Legal interpretation
• Regulatory compliance
• Financial decisions
• Tax
• Medical or health and safety advice
• Employment and HR decisions
• Cyber-security configuration
• Incident response
• Contractual commitments
• Public accusations
• Formal evidence
• Safeguarding
• Decisions affecting an individual’s rights
• Technical instructions that could cause downtime or data loss

Warning signs that an answer needs checking

• Very precise statistics without a direct source
• Quotations without a verifiable original
• Links that do not open
• Citations with plausible but unfamiliar titles
• Sources that exist but do not support the claim
• Confident claims about very recent events
• Definitive legal, tax, medical or regulatory statements
• An answer that perfectly matches the user’s assumption
• Product features not shown in manufacturer documentation
• Technical commands with no explanation of impact or rollback
• Claims that use words such as always, guaranteed, completely or impossible
• A suspiciously neat answer to a disputed or complex issue
• An unexplained change in names, dates or figures
• Contradictory statements in different parts of the response
• References to documents the AI has not actually been given
• A summary that includes material absent from the original document

Ten practical ways to reduce the risk

1. Define the task precisely

State the objective, the audience, the required output, the relevant date, the jurisdiction, the permitted sources and what the AI should do when evidence is missing. For example:

Use only the documents I provide. If the answer is not contained in them, state “not found in the supplied material” rather than filling the gap.

2. Separate large tasks

Break work into stages: identify the questions, locate evidence, extract relevant passages, draft the answer, verify every claim, then approve publication. Combining research, analysis, drafting and verification in one instruction makes it harder to see where an error entered.

3. Provide trustworthy source material

Use official guidance, current policies, contracts, approved internal documentation, manufacturer information, original research and verified datasets. Uploading evidence reduces the need to rely on general memory, but the AI can still misread or omit parts of a document.

4. Require evidence for factual claims

Do not ask merely for “some sources”. Ask for a table containing the claim, the supporting source, the relevant section or quotation, the date of the source, any uncertainty or limitation, and the verification status.

5. Give permission to say “I do not know”

Use instructions such as: do not guess; state when evidence is insufficient; separate facts from assumptions; identify unresolved questions; flag any claim that cannot be verified. This reduces the pressure on the system to produce a complete-looking answer.

6. Check the premise of the question

A detailed answer built on a false premise can still look internally coherent. Ask the AI to identify assumptions before answering:

Before answering, list the factual assumptions in my question and identify which ones require verification.

7. Ask for alternatives and limitations

Request competing interpretations, exceptions, missing information, contrary evidence and reasons the conclusion may be wrong. This does not guarantee accuracy, but it surfaces weaknesses that a single confident answer conceals.

8. Verify important claims outside the AI response

Open the source. Check the author, the date, the original wording, the jurisdiction, the context, whether it actually supports the claim and whether a newer source supersedes it.

9. Use human approval

Assign a named person to approve external communications, formal advice, legal or regulatory statements, financial claims, technical changes, customer-specific recommendations, public statistics and high-impact decisions.

10. Retain an auditable record where the risk warrants it

Keep the original request, source documents, generated draft, changes made, sources checked, approver, publication or decision date and final version. Not every casual AI interaction requires formal record-keeping, but important business outputs may.

A reliable AI workflow

1. 1Define — what decision or output is required?
2. 2Ground — which authoritative material should the AI use?
3. 3Generate — ask for a draft, not an unquestionable final answer.
4. 4Separate — distinguish facts, assumptions, suggestions and unknowns.
5. 5Verify — check important claims against original sources.
6. 6Challenge — look for exceptions, conflicting evidence and missing context.
7. 7Approve — a suitable human accepts responsibility.
8. 8Record — retain evidence where the business risk warrants it.
9. 9Review — correct or withdraw the output if new evidence emerges.

A practical verification table

For factual work, a simple table keeps verification visible. This fictional example checks claims in a draft article about a software feature:

Use labels such as verified, partly supported, unsupported, outdated or requires specialist review. Confidence percentages generated by the model are not calibrated guarantees, so do not treat a model-generated numerical confidence score as objective truth.

How to check sources and citations

AI-generated citations can fail in several ways: the source does not exist; the URL is wrong; the title is slightly altered; the source is real but irrelevant; the source contradicts the answer; the source is outdated; a secondary article is presented as the original authority; or multiple claims are attached to one source that supports only one of them.

1. 1Open the link.
2. 2Confirm the publisher and author.
3. 3Check the publication or update date.
4. 4Find the exact supporting passage.
5. 5Read enough surrounding context to understand limitations.
6. 6Check whether a newer authoritative source exists.
7. 7Confirm the jurisdiction and intended audience.
8. 8Record the verification where appropriate.

Using your own documents

Supplying internal or approved documents — policies, contracts, meeting notes, product manuals, procedures, project plans, service records, approved price lists or knowledge-base articles — can improve relevance. However, risks remain:

• The wrong document may be selected
• The document may be outdated
• The AI may overlook a clause
• Tables or appendices may be misread
• Conflicting documents may exist
• Access permissions may expose inappropriate information
• A summary may remove an important qualification

Ask the AI to provide the document name, section, page number where available, the relevant excerpt, the date or version and any conflicting evidence.

What do browsing, search and deep research change?

Tools with search or browsing may access newer information, provide links, compare multiple sources, reduce reliance on training memory and make verification easier. But they can still select a weak source, miss the authoritative source, misread the page, combine contradictory information, cite a source that does not support the sentence, use an outdated page, fail to access content behind a login, or summarise a search snippet rather than the underlying material.

“Deep research” features, which run longer multi-source investigations and produce cited reports, are a useful research aid — not automatic professional assurance. The citations still need opening and checking.

Why a second AI is not independent proof

Asking another AI to check the answer may identify inconsistencies, unclear reasoning, missing questions, alternative interpretations and possible errors. That can be genuinely useful. However, both systems may rely on similar source material; both may repeat the same widely circulated error; the second system may invent different evidence; agreement between models does not establish truth; and consensus-like wording is easy to mistake for independent confirmation.

Does asking the AI to review its own answer help?

Self-review may help identify contradictions, omitted requirements, unsupported claims, weak wording, arithmetic worth recalculating and areas of uncertainty. But it cannot turn the model into an independent source. Useful review prompts include:

Review the draft and produce a list of every factual claim that requires external verification. Do not confirm the claims yourself unless you can cite an accessible authoritative source.

Separate the response into verified facts, assumptions, recommendations and unresolved questions.

Ask for a concise rationale, assumptions, evidence, calculation steps, limitations, checks performed and unresolved issues. Do not ask the AI to reveal hidden internal reasoning — request the useful outputs above instead.

When professional review is required

AI may help organise questions or prepare a draft, but suitable professional review may be required for legal advice, tax, regulated financial advice, medical decisions, health and safety, employment law, safeguarding, data protection, contractual interpretation, formal cyber-security design, regulatory submissions, structural or engineering decisions, insurance coverage, and court or tribunal material.

Practical business implications

AI output needs an owner

Every material output should have a person responsible for deciding whether it is suitable.

Different tasks need different rules

A social-media idea does not need the same approval as a contract clause or a cyber-security change.

Sources should be part of the workflow

Verification should not be an optional final thought. Build it into how AI-assisted work is requested, drafted and approved.

Staff need permission to challenge the AI

Employees should not feel obliged to accept an answer because it appears sophisticated or was produced by an expensive tool.

Reliability depends on information governance

Outdated, duplicated and poorly controlled internal documents will produce weaker grounded answers.

Questions to ask before using AI output

1. 1What decision or publication will this influence?
2. 2What harm could result if the answer is wrong?
3. 3Which claims are factual rather than creative?
4. 4What sources were used?
5. 5Have those sources been opened and checked?
6. 6Are they current and relevant to the UK?
7. 7Did the AI rely on information outside the supplied material?
8. 8Which assumptions did it make?
9. 9What information is missing?
10. 10Does the answer require specialist review?
11. 11Who is approving the final output?
12. 12Is confidential or personal information involved?
13. 13Should the prompt, evidence and approval be retained?
14. 14How will an error be corrected after publication?
15. 15Is AI the right tool for this task at all?

The IT Club View

The most important lesson is not that AI sometimes makes mistakes. People, search engines, websites and internal documents make mistakes too. The difference is that generative AI can package an error into an unusually fluent and convincing answer within seconds. That creates a temptation to treat presentation quality as evidence quality.

Businesses will not solve this by endlessly searching for a magic prompt or a model that never hallucinates. They need a proportionate working method: use AI freely for low-risk drafting and exploration; ground factual work in appropriate evidence; verify important claims; obtain specialist review when necessary; and retain human accountability.

Insisting on human review does not mean manually repeating all the work. AI can still structure the task, extract candidate claims, identify evidence, create comparison tables, flag uncertainty, produce a draft and prepare a verification checklist.

Reliable AI use is not blind trust or blanket prohibition. It is fast assistance combined with visible evidence and accountable approval.

Plain-English Takeaway

AI can produce convincing information that is incomplete, outdated or entirely wrong. Reduce the risk by giving it reliable source material, asking focused questions, allowing it to admit uncertainty and checking important claims against the original evidence. The AI may prepare the answer, but the business remains responsible for approving and using it.

The Operational Heartbeat

AI reliability is not a one-off achievement. It changes as models are updated, search features change, source documents age, staff add or remove files, permissions change, new use cases emerge, prompts are modified, regulations change, trusted websites move or disappear and employees adopt new AI products.

A recurring review should check approved AI services, active licences, documented use cases, source repositories, outdated documents, AI-generated incidents, incorrect published claims, high-risk workflows, review and approval compliance, user training, prompt templates, retrieval quality, citation failures, access permissions, data-sharing settings, model or feature changes and corrective actions.

This article provides general technology information. It is not legal, financial, medical or professional advice, and AI product capabilities may change.