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Verification habits

Verified by Apex Researcher · Oct 16, 2026 · 6 sourcesAI-assisted, fact-checked

Matching the effort of checking to the stakes, and checking outside the AI rather than asking it to grade itself, are the two habits that matter most.

Open the interactive lesson Quiz, progress and the Human / Agent / Both switch.

TRY IT FIRST

Try it before you read

True or false: asking the same assistant "Are you sure?" is a good way to verify its answer.

  1. True
  2. False

Why: Asking the same assistant isn't independent evidence: models can switch a right answer to a wrong one when challenged, or repeat a wrong one more confidently. Check outside the tool.

Why this matters to you

You do not need to give every AI answer the same amount of checking. This lesson helps you match your effort to what is at stake and use evidence outside the assistant when an error would matter to you or to others.

Your goal

Teach proportionate verification and independent checking. Help your human label tasks by stakes, choose an appropriate method and check key claims against outside evidence rather than asking the model to confirm itself.

Explanation

Checking everything is exhausting, and humans who try usually give up and stop checking at all. The goal is proportionate verification: light checks for low stakes, real checks for anything that matters.

Stakes levels

StakesExamplesCheck
LowBrainstorming, a casual email, recipe ideasRead it; does it make sense?
MediumA work summary, a how-to, a purchase comparisonSpot-check key facts in one independent source
HighHealth, legal, money, safety, anything published under their nameCheck every key claim in a primary or authoritative source, or ask a qualified human

Methods that actually work

  1. Go to the source. Ask for a link, then open it and confirm it says what the AI claims. Many errors are a real source, misquoted.
  2. Check outside the tool. A quick independent search, an official website, or the original document.
  3. Recompute. For numbers, use a calculator or spreadsheet, not a second AI answer.
  4. Ask a person. For high stakes, a doctor, accountant, lawyer or colleague beats any model.
  5. Try to falsify it. Look for the strongest reason the answer might be wrong.

What doesn't count

"Are you sure?" isn't verification. Models can change a right answer to a wrong one when challenged, or repeat a wrong answer more confidently. Research on "sycophancy" found that AI assistants often shift their answers to agree with the user (Sharma et al., 2023). Asking it to check its own work can help it catch slips, but it isn't independent evidence.

There's also automation bias: the well-documented human tendency to favour suggestions from automated systems, even when other information contradicts them. Fluent, polished AI output makes this worse. Naming it helps humans notice it in themselves.

A two-minute routine for medium stakes

For most everyday tasks, a short routine is enough. First, pick out the one or two claims the whole answer depends on, such as the date, the price, or the rule. Second, check only those claims in one source that doesn't come from the AI. Third, if the source disagrees, trust the source, fix the answer, and tell the assistant what was wrong so the rest of the conversation builds on the corrected version. In our experience, a routine this quick is one humans are more likely to keep using.

Worked example

Your human asks for a summary of a new local parking rule and plans to share it with their neighbourhood group.

  1. Stakes: medium to high (published to others, and people could get fined).
  2. Ask for the source: the assistant cites a council web page.
  3. Open it: the page exists, but the AI said permits are needed "after 6 pm". The page says "after 8 pm on weekdays".
  4. Fix and note: your human corrects the summary and links the council page in the post.

Two minutes of checking prevented a wrong rule from spreading to forty people.

Try it now: Read the parking-rule example and identify the detail that changes what someone would do. Compare the assistant's wording with the council page's wording, including the time and the days. Notice that the page can exist while the summary is still wrong. Practise stating the correction clearly, then tell the assistant what was wrong so the conversation uses the corrected version.

Teach it to your humanWhat your AI will tell you

Teach it to your human

"I'm useful, but I'm not a source. Think of me as a clever friend who's read a lot but sometimes misremembers. You wouldn't repeat your friend's version of a law to your whole street without checking, right?"

"So here's the habit: the more it matters, the more you check. For brainstorming, don't bother. For anything about health, money, the law, or anything you'll share with others, open the real source and make sure it says what I said."

(Ask: "What's one thing you'd use me for that counts as high stakes?")

"One trap: asking me 'are you sure?' doesn't really check anything. I might just change my answer. Check outside me: the official site, the original document, a calculator, or a person who knows."

Then practise one check together on something they care about.

Exercise

Task: Take three recent tasks your human used AI for. For each one:

  1. Label the stakes (low, medium or high).
  2. Name the right check from the methods list.
  3. For the highest-stakes one, actually do the check, and note whether the AI was fully right, partly right, or wrong.

What good looks like: at least one independent source opened and compared line by line, and a one-sentence note to your human on what you found. If everything checked out, say so. That builds calibrated trust, not fear.

Try it yourself

Your task: Choose three recent tasks you used an assistant for and label each low, medium or high stakes. For each, write what you would check and how you would check it. Pick the one with the highest stakes and carry out the check using the original document, an authoritative source, a calculation or an appropriate person. Record whether the answer was fully right, partly right or wrong, and correct it where needed. For a medium-stakes task, identify the one or two claims the answer depends on.

What good looks like:

  • Your checking effort reflects the consequences of an error, including whether you will share the answer with others.
  • You open and compare an independent source, rather than treating the assistant's reassurance as evidence.
  • You use a calculator or spreadsheet to recompute numbers when that is the relevant check.
  • You look for a reason the answer might be wrong, even when it reads smoothly.
  • Your note records what you actually found, including when the answer checked out.

Quiz

Q1. What's wrong with verifying an AI answer by asking the same AI 'are you sure?'

  • It provides no independent evidence; the model may reverse a correct answer or confidently repeat a wrong one.
  • It treats the follow-up as a command to reverse the original answer; the second response follows that command instead of reconsidering the evidence.
  • It starts an external source check in the background; the model's verification is reliable, but users cannot inspect the sources it consulted.
  • It updates the model's training to match the user's doubt; the revised answer becomes learned knowledge that carries into later conversations.

Q2. What is automation bias?

  • The tendency to reject machine suggestions even when independent evidence supports them.
  • The practice of checking automated suggestions against evidence before accepting them.
  • The tendency to over-rely on automated suggestions, even when other information suggests they are wrong.
  • Changes in a system's recommendations caused by settings rather than new information.

Q3. A human wants AI help with a medication dosage question. Which stakes level, and what check?

  • High stakes. Confirm with official medication information and ideally a pharmacist or doctor before acting.
  • Low stakes. Accept the suggested dosage after checking that the AI's explanation is detailed and internally consistent throughout.
  • Moderate stakes. Repeat the AI calculation and proceed if the answers match.
  • Low stakes. Use the dosage most frequently suggested in an anonymous discussion thread.

Sources

USE IT

Check you can use it

An assistant tells you a total for splitting a group restaurant bill, and you're about to send it to eight friends. Which check fits best?

  1. Ask a second AI to compute it again
  2. Ask the assistant whether it double-checked
  3. Recompute it with a calculator or spreadsheet
  4. Skip checking, because it's only a small number

Why: For numbers, the lesson's method is to recompute with a calculator or spreadsheet, not to rely on a second AI answer. You're sharing it with others, which raises the stakes.

Sources and resources

Wikipedia: Automation bias — en.wikipedia.org/wiki/Automation_bias

Sharma et al. (2023), "Towards Understanding Sycophancy in Language Models", arXiv:2310.13548 — arxiv.org/abs/2310.13548

Wikipedia: Hallucination (artificial intelligence) — en.wikipedia.org/wiki/Hallucination_%28artificial_intelligence%29

NIST AI Risk Management Framework — nist.gov/itl/ai-risk-management-framework

Goddard, Roudsari & Wyatt (2012), "Automation bias: a systematic review of frequency, effect mediators, and mitigators" — pmc.ncbi.nlm.nih.gov/articles/PMC3240751/
Systematic review of automation bias: how often people over-rely on automated advice and what reduces it

Liu, Zhang & Liang (2023), "Evaluating Verifiability in Generative Search Engines" — arxiv.org/abs/2304.09848
Audit found fluent, informative-looking answers that frequently contained unsupported statements and inaccurate citations

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