How to Check AI Answers and Catch Made-Up Facts

A Confident Tone Proves Nothing

AI does not seek truth in the human sense. It generates a plausible continuation of text and sometimes fills gaps with invented dates, laws, quotes, and links. The more precisely a question is phrased, the more convincing the mistake may look.

Consider this prompt: “What VAT rate applies in Kazakhstan in 2025, and which article of the Tax Code specifies it?” An answer with a percentage and article number sounds authoritative. But the model may have mixed an old version of the law with another country’s rules and a random article number. For a tax decision, such text is only a starting point—not a basis for action.

There is a simple rule: the higher the cost of an error, the more independent checks you need. Medical advice, legal deadlines, loan calculations, and information about government services require consulting a primary source or a specialist. In these cases, AI is an assistant—not a doctor, lawyer, or financial adviser.

Cross-Checking in Four Steps

First, split the answer into separate claims. The statement “Parking on the sidewalk is allowed in Almaty after 22:00” contains several checkable elements: the city, the type of location, the time, and the permission itself. Checking everything with one question is inconvenient—the model may repeat its own mistake in a longer formulation.

Next, ask several models the same question without showing them the first answer. Keep the inputs and format consistent: “Name the rule, the date it changed, a link to an official source, and your confidence level.” This lets you compare knowledge rather than the quality of different prompts.

The third step is to ask each model to challenge its own answer. Try prompts such as: “Which parts of the answer may be outdated?”, “Which fact here is hardest to verify?”, and “Give the conditions under which this claim would be false.” This often surfaces caveats hidden behind smooth prose.

Finally, open the primary source. In Kazakhstan, this might be a government website, a regulatory database, a university page, an official bank tariff, or a company publication. Agreement among four answers increases confidence, but it does not replace the document: models are often trained on the same erroneous pages.

What Is a Consensus Map?

A consensus map is a short table showing, for each claim, how many models supported it, what caveats they added, and whether they provided a confirming link. It is more useful than an average “confidence rating”: AI systems can assign themselves 95% without any verifiable basis.

For example, you are finding out how long a trip from Almaty to Konaev takes. Some answers say 1 hour 10 minutes, while others say 1 hour 40 minutes. The difference may be explained by the route, traffic, and departure point. The map should record not simply “three models agree,” but the condition: “with clear roads and departure from the city center.”

For an everyday question, this structure is enough:

• Fact: a specific claim without unnecessary explanation.
• Agreement: how many of the 25 models gave the same answer.
• Disagreement: the dates, figures, and conditions where answers differ.
• Source: an official document or page that can be opened.

A map points you toward what to check manually. If every model repeats the same fact but none provides a usable link, that is a yellow flag. If two models give different dates for the adoption of a law, do not trust either answer until you consult the primary source.

Trick Questions for Plausible Fabrications

A good check does not ask a model to “answer confidently.” It makes the model reveal its verification process, the limits of its knowledge, and possible exceptions. Trick questions are useful for this.

Existence check. “Does this law, report, or organization exist? If so, give its official name and a link.” This works for questionable awards, studies, and local programs. AI systems like to complete nonexistent names based on familiar patterns.

Date check. “Was this fact true on January 1, 2023, or is it currently in effect? Give the date of the latest change.” Information about tariffs, visas, taxes, and border-crossing rules becomes outdated quickly. “Currently” without a date is too vague.

Exceptions check. “In what cases is this answer wrong? Which conditions change the result?” For example, you cannot verify a question about visa-free entry for Kazakhstani citizens without knowing their citizenship, travel purpose, length of stay, and document type.

Quote check. “Give the exact quote, page, or clause, and separate it from your paraphrase.” If a model reports a famous saying by Abai but gives neither the work nor the context, look up the quote separately. Attractive quotation marks are not proof.

Calculation check. “Show the formula, units of measurement, and intermediate values.” This is the bare minimum for converting tenge, calculating deposit interest, or estimating fuel consumption. Recalculate the result independently with a calculator.

Signs of an Unreliable Answer

The first warning sign is overly polished specificity without sources. An exact date, an official’s name, and a resolution number create an impression of expertise, but such details are often fabricated. Open the link: it may lead to a homepage, a document with different content, or nowhere at all.

The second sign is avoiding clarifying questions. If an answer depends on the city, date, age, currency, or a person’s status, a responsible response will ask for context. A model that immediately gives a universal rule may overlook an important exception.

The third is internal contradiction. The answer says the service is free at the start, then introduces a fee later. Or it gives one deadline but uses another in its calculation example. Read the entire answer: contradictions are often visible without an external search.

Another warning sign is citing “a study by scientists” without authors, a journal, or a publication year. The same applies to statistics without a sample or measurement period. “Most Kazakhstanis” means nothing without a source, participant count, and survey method.

Using Multiple Models Without the Hassle

In QueryWise, you can send one question to several strong models and compare their answers in one place. In the current live ranking, the leaders have scores of 9.8, and the top four are Claude Fable 5, GPT-5.6 Sol, Kimi K3, Grok 4.5. The ranking helps you choose participants for a check, but it does not turn their answers into an official document.

We added QueryWise on launch day and immediately tested it with an everyday scenario: we asked about the route between Almaty and Shymkent, separately requesting the assumptions about travel time and mode of transport. The models agreed on the general estimate but differed on the details—one assumed a flight, another a car route. This is a good example of why a consensus map should record conditions, not just the final number.

A single question usually costs 10–75 ₸, there is no subscription, and new users get three free questions. The interface is available in Russian and Kazakh. For a simple reference, you do not need to run everything at once: ask one model first, then send a disputed or important fact to several models and compare the differences.

A practical prompt template looks like this: “Check the claim: [text]. Separate facts from assumptions. For each fact, give the date it was valid, an official source, possible exceptions, and a confidence level. If there is no confirmation, say plainly: ‘I don’t know’.”

Then run an independent check: “Find the weak points in the previous reasoning and give possible counterarguments.” Do not tell the model which answer you believe is correct. Otherwise, it will start defending your version instead of looking for errors.

When You Can Stop

For choosing a film, explaining a term, or drafting a route, it is enough to compare several answers and check obvious inconsistencies. For entry rules, taxes, medication, contracts, and large payments, the bar is higher: you need a current primary source and sometimes advice from a qualified specialist.

Cross-checking does not make AI error-free. It reduces the chance of mistaking a polished fabrication for a fact and shows exactly where uncertainty remains. Look at dates, conditions, and evidence. Matching answers are a useful clue—not a stamp of truth.