Artificial Intelligence
Checking an AI Answer: The Verification Habits That Catch Most Errors
AI answers fail in predictable ways: invented sources, numbers stated with confidence, rules that were true last year or in another country. The fixes are habits, not tools, and they take a minute each.
By Maya Iyer · Updated · 10 min read
Short answer
How do I know if an AI answer is wrong?
AI assistants are wrong in a small number of recognisable ways. They invent citations, case numbers and product specifications that look real. They state numbers - prices, rates, dates, dosages - with the same confidence whether or not they know them. They apply a rule from the wrong jurisdiction, which in India usually means American advice, or from the wrong year, which matters for anything touching GST, RBI rules or FSSAI regulations that have changed recently. They make arithmetic slips inside otherwise sound reasoning. The habits that catch most of this are simple: ask for the source and open it; check the date the answer applies to; take anything with a number or a rule to the primary document; ask the model to argue against its own answer; put the same question to a second model and treat disagreement as a flag; and, for maths, ask for the working and check one line by hand. Nothing touching money, health or the law should be acted on from an AI answer alone.
Our practical verdict
AI assistants are useful in proportion to how well you can tell when they are wrong, and they are wrong in a small number of ways that repeat. Once you know the patterns, verification is a set of habits rather than a project.
The patterns first. Models invent sources - citations, case numbers, report titles - that are formatted exactly like real ones, because the format is what they learned. They state numbers with the same confidence whether the number is known or guessed. They apply rules from the wrong place, which for an Indian reader usually means American tax, employment or consumer law offered as if it were local, or from the wrong time, which matters for anything the government has changed recently. They assemble product specifications from similar products and present the composite as fact. And they make arithmetic slips inside reasoning that is otherwise sound, usually in a single line.
The habits follow from the patterns. Ask where a claim comes from, and open what it names; a source you cannot open is not a source, and a source that does not say what the model said it says is the single most common serious error. Ask what date an answer applies to, and treat anything a year old as needing a check; India's GST slabs changed in September 2025 and a large share of answers still describe the old ones. For anything with a number or a rule in it - a rate, a deadline, a threshold, a dosage, a legal provision - go to the primary document: the RBI circular, the gazette notification, the vendor's own pricing page. Ask the model what is wrong with its own answer, which reliably surfaces the caveats it omitted. Put the same question to a second model, not because agreement proves anything but because disagreement is a cheap and dependable flag. And for arithmetic, ask for the working and check one line yourself, or use a calculator built for the purpose; the ones on this site exist so that the maths is not the model's.
Shortlist
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Pick 1
The failure modes are predictable
Fabricated references that look exactly like real ones, including citation formats and case numbers. Numbers stated confidently whether known or guessed. Rules from another country or an earlier year. Product specifications assembled from similar products. Arithmetic slips inside sound reasoning. Knowing these five patterns is most of the skill, because you know where to look.
Pick 2
Ask for the source, then open it
Any claim that matters gets the question: where does that come from? A real answer names a document you can open. Then open it, because a confident-sounding citation to a document that does not exist, or does not say that, is the most common serious error. If the model cannot name a source, treat the claim as its opinion.
Pick 3
Check the date the answer belongs to
Models are trained to a cutoff and may not know it has passed. For anything that changes - tax rates, RBI rules, product prices, FSSAI regulations - ask what date the answer applies to, and assume anything older than a year needs checking against the current position. India's GST slabs changed in September 2025 and a great many answers still describe the old ones.
Pick 4
Take numbers and rules to the primary
A rate, a threshold, a deadline, a dosage, a legal provision: go to the document it came from. RBI circulars, gazette notifications, a vendor's own pricing page. The AI answer is the map; the primary is the territory, and the two differ often enough that this habit alone would have caught most of the costly mistakes people report.
Pick 5
Ask it to argue the other side
Asking the model what is wrong with its own answer, or what a critic would say, surfaces caveats it had left out. It is not a proof of anything, but it reliably turns an over-confident answer into a more honest one, and it costs one follow-up question.
Pick 6
Use a second model as a flag, not a judge
The same question to a second assistant. Agreement is not proof - both can share a training error - but disagreement is a reliable signal that something needs checking. This is the cheapest cross-check available and the one people skip because it feels redundant.
Pick 7
For maths, show the working
Ask for the steps and check one of them by hand, or paste the numbers into a calculator. Models make arithmetic errors inside otherwise correct reasoning, and the error is usually in one line that a human can verify in seconds. For anything financial, the calculators on this site exist so that the arithmetic is not the model's.
Which option should you choose?
The failure modes are predictable
Fabricated references that look exactly like real ones, including citation formats and case numbers. Numbers stated confidently whether known or guessed. Rules from another country or an earlier year. Product specifications assembled from similar products. Arithmetic slips inside sound reasoning. Knowing these five patterns is most of the skill, because you know where to look.
Ask for the source, then open it
Any claim that matters gets the question: where does that come from? A real answer names a document you can open. Then open it, because a confident-sounding citation to a document that does not exist, or does not say that, is the most common serious error. If the model cannot name a source, treat the claim as its opinion.
Check the date the answer belongs to
Models are trained to a cutoff and may not know it has passed. For anything that changes - tax rates, RBI rules, product prices, FSSAI regulations - ask what date the answer applies to, and assume anything older than a year needs checking against the current position. India's GST slabs changed in September 2025 and a great many answers still describe the old ones.
Take numbers and rules to the primary
A rate, a threshold, a deadline, a dosage, a legal provision: go to the document it came from. RBI circulars, gazette notifications, a vendor's own pricing page. The AI answer is the map; the primary is the territory, and the two differ often enough that this habit alone would have caught most of the costly mistakes people report.
Ask it to argue the other side
Asking the model what is wrong with its own answer, or what a critic would say, surfaces caveats it had left out. It is not a proof of anything, but it reliably turns an over-confident answer into a more honest one, and it costs one follow-up question.
Use a second model as a flag, not a judge
The same question to a second assistant. Agreement is not proof - both can share a training error - but disagreement is a reliable signal that something needs checking. This is the cheapest cross-check available and the one people skip because it feels redundant.
For maths, show the working
Ask for the steps and check one of them by hand, or paste the numbers into a calculator. Models make arithmetic errors inside otherwise correct reasoning, and the error is usually in one line that a human can verify in seconds. For anything financial, the calculators on this site exist so that the arithmetic is not the model's.
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| Situation | Best starting point | Final check |
|---|---|---|
| The failure modes are predictable | Fabricated references that look exactly like real ones, including citation formats and case numbers. Numbers stated confidently whether known or guessed. Rules from another country or an earlier year. Product specifications assembled from similar products. Arithmetic slips inside sound reasoning. Knowing these five patterns is most of the skill, because you know where to look. | Use this as a shortlist, then verify the final details before committing. |
| Ask for the source, then open it | Any claim that matters gets the question: where does that come from? A real answer names a document you can open. Then open it, because a confident-sounding citation to a document that does not exist, or does not say that, is the most common serious error. If the model cannot name a source, treat the claim as its opinion. | Use this as a shortlist, then verify the final details before committing. |
| Check the date the answer belongs to | Models are trained to a cutoff and may not know it has passed. For anything that changes - tax rates, RBI rules, product prices, FSSAI regulations - ask what date the answer applies to, and assume anything older than a year needs checking against the current position. India's GST slabs changed in September 2025 and a great many answers still describe the old ones. | Use this as a shortlist, then verify the final details before committing. |
| Take numbers and rules to the primary | A rate, a threshold, a deadline, a dosage, a legal provision: go to the document it came from. RBI circulars, gazette notifications, a vendor's own pricing page. The AI answer is the map; the primary is the territory, and the two differ often enough that this habit alone would have caught most of the costly mistakes people report. | Use this as a shortlist, then verify the final details before committing. |
Read the editorial notes
Two boundaries hold regardless of how careful the habits are. Nothing touching money, health or the law should be acted on from an AI answer alone; the answer is where you start reading, and the primary document is where you decide. And the model's confidence carries no information about its accuracy, which is the hardest habit of all to keep, because the writing is fluent whether or not the content is true.
The cost of these habits is a few minutes per answer that matters, and none at all for the answers that do not. What you are paying for, in the AI subscription cost guide's terms, is speed of understanding; the privacy guide covers what you give up in return. Verification is what keeps the speed from becoming a cost of its own. How many seats a team should be doing this across is the subject of the small team subscriptions guide.
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FAQ
How do I know if an AI answer is wrong?
Look for the five patterns: invented sources, confident numbers, rules from the wrong country or year, specifications that do not match the product, and arithmetic slips. Then ask for the source and open it, and take any number or rule to the primary document.
Do AI assistants make up sources?
Yes, and the fabricated ones are formatted exactly like real ones. Always open a citation before relying on it. A source the model cannot name is an opinion.
Is asking a second AI a good way to check?
As a flag, yes. Agreement between two models is not proof, since they can share an error, but disagreement reliably signals that the claim needs checking against a primary source.
Can I rely on AI for financial or legal questions in India?
For understanding, yes. For acting, no - not without the primary document. Rules and rates in India change often, and models frequently apply American rules or last year's Indian ones.
Why does an AI get Indian rules wrong?
Two reasons: training data is weighted towards other countries, so it defaults to their rules, and it is trained to a cutoff, so recent Indian changes - GST slabs, RBI directions, FSSAI regulations - may not be in it. The same weighting is why it is weaker in Indian languages, which the Indian languages guide maps.
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How Choice IQ evaluated this guide
Choice IQ evaluates AI answer verification by the recurring failure modes - fabricated sources, confident numbers, wrong-jurisdiction and out-of-date rules, composite specifications and arithmetic slips - and the low-cost habits that catch them: sourcing, dating, primary-document checks, self-critique, second-model comparison and shown working.
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