How to verify
AI-generated information

AI tools now draft emails, summarise documents, answer questions and generate images, code and data analyses. They're fast, but they can be confidently wrong. Verification is the habit of checking AI output before relying on it. It includes understanding why models make errors; identifying factual claims in AI text; tracing claims to original sources; confirming that citations exist and support the claim; recalculating numbers; checking quotes, names and dates; recognising when information may be outdated; spotting AI-generated images and audio; and matching the level of checking to the stakes.

The risks are well documented. An EBU-BBC study of more than 3,000 AI-generated news answers in 14 languages found 45% had at least one significant issue and 31% had serious sourcing problems.¹ People increasingly accept AI answers without checking: Pew Research Center found users clicked a search result on 8% of Google visits with an AI summary, versus 15% without.² Organisations are accountable for AI mistakes — a Canadian tribunal made Air Canada honour a refund policy its chatbot invented.³ Courts have seen a wave of filings with AI-fabricated material, tracked in Damien Charlotin's AI Hallucination Cases database.⁴ And trust in information is fragile: the Reuters Institute found 58% of people worried about telling true from false online.⁵

AI news answers with a significant issue (EBU-BBC, 2025)

45%

AI news answers with serious sourcing problems (EBU-BBC, 2025)

31%

Click rate with vs without an AI summary (Pew, 2025)

8% vs 15%

People worried about true vs false online (Reuters Institute, 2025)

58%

Table: How AI verification skills are assessed (editorial summary)

Table: Verifying claims in an AI-generated summary (illustrative)

Table: How AI verification developed (editorial summary)

• An AI-generated document you fact-checked, with corrections
• A citation check log
• A recalculated AI data analysis
• A personal AI verification checklist
• A note on what data you won't share with AI
• A verified AI information verification certificate

Show responsible AI use: "Used AI to draft research summaries; verified all citations and figures; caught fabricated sources before client delivery; wrote team AI-checking guidelines." Real examples only.

"How do you use AI at work?"

For drafts, summaries and ideas; I verify facts, citations and numbers before using output.

"An AI tool gives you a statistic — what do you do?"

Find the original source, check the context and date, and recalculate if possible.

"How would you know if a citation is fake?"

Search official databases and publisher sites; if it can't be found, treat it as fake.

"When is it okay not to verify?"

For low-stakes brainstorming or drafts that won't be shared; anything factual, external or high-stakes needs checking.

"What shouldn't you put into an AI tool?"

Confidential, personal or client data unless the tool is approved for it.

• "Which AI tools are approved?" — policy.
• "How is AI output reviewed?" — controls.
• "Who is accountable for AI-assisted work?" — responsibility.
• "Is there AI training?" — support.

Table: Roles where AI verification matters and typical pay in India (AmbitionBox, October 2026; indicative)

Table: Common AI verification tools (editorial guidance)

• Any knowledge role — trusted output.
• Research and analysis — reliable evidence.
• Legal and compliance — safe use of AI.
• Content and communications — accurate publishing.
• AI governance — policies and controls.

1. Week 1: Practise — Ask an AI tool five factual questions and verify every answer.
2. Week 2: Prove it — Build a personal checklist; take the AI Information Verification Certification.

Related: Media Literacy, Research Literacy and AI Research and Fact-Checking. Guides: media literacy, research literacy and AI research and fact-checking.

• Drafts — fact-checking before sending.
• Research — tracing sources.
• Data — re-running numbers.
• Code — testing AI-written code.
• Images — checking origins.
• Policy — choosing approved tools.

"What's an AI hallucination?"

A confident AI output that is false or unsupported, such as an invented fact or citation.

"What's retrieval-augmented generation?"

A method where AI retrieves documents before answering, which can improve grounding but doesn't guarantee accuracy.

Table: A first-90-days plan (editorial guidance)

Learn your organisation's AI policy and approved tools. Verify every factual claim you use. By the third month, share a simple checking routine with your team.

AI tools increasingly show sources, link to documents and flag uncertainty, which helps. Some can even check other AI output. But these features vary in quality and can still cite irrelevant or misread sources. Human verification remains essential for anything important, and the professionals who check carefully are the ones trusted with AI-assisted work.

Highlight each factual claim. For the two or three that matter most, search for an original source — official site, paper or reputable outlet. Confirm numbers and dates. Check that quotes are real. If anything can't be confirmed, remove it or mark it as unverified.

Consider stakes and audience. Brainstorming for yourself needs little checking. Internal drafts need key facts checked. Anything external, legal, financial, medical or affecting customers needs full verification and often a second reviewer. Match effort to risk.

Ask the AI to cite specific sources with links, to separate facts from assumptions, and to say when it's unsure. Provide your own trusted documents for it to work from. These steps don't guarantee accuracy, but they make checking faster.

Run AI-written code in a safe environment and test it with normal, edge and wrong inputs before using it. Check that libraries and functions it uses actually exist. For AI data analysis, inspect the data it used, recalculate key figures and confirm that charts match the numbers. AI can produce code and analyses that look right but quietly fail, so testing is part of verification.

"AI with sources is always right." Sources can be misread or irrelevant.

"Newer models don't hallucinate." They do, less often in some areas.

"Checking AI defeats the purpose." It's still much faster than starting from scratch.

• Pasting AI output unchecked.
• Trusting invented citations.
• Sharing confidential data.
• No checks on numbers.
• Blaming the tool for errors.

AI answer quality comes from the EBU-BBC study reported by Journalism.co.uk; search behaviour from Pew Research Center (2025); chatbot liability from MobileSyrup's report on the Air Canada case; court cases from Damien Charlotin's database; and trust data from the Reuters Institute Digital News Report 2025. The legal note scene, checking table and history are editorial. Science of Apps publishes the free test linked here. Last reviewed October 2026.

• Hallucination — confident but false AI output.
• Citation check — confirming a source exists and supports a claim.
• Grounding — tying AI answers to trusted documents.
• Synthetic media — AI-generated images, audio or video.
• Human-in-the-loop — people reviewing AI output.