How to verify AI answers — and avoid AI hallucinations

General AI models generate text by predicting likely word sequences from patterns in their training data, not by looking up facts in a verified record. When the most plausible-sounding continuation is wrong, the model still states it with full confidence — this is what people call an AI hallucination.

The model has no built-in way to tell you which source a claim came from, because for most claims there is no specific source; the answer is reconstructed from opaque training data you cannot inspect. This is why a fluent, confident answer is not the same as a correct one, and why verification has to be your job, not the model's promise.

Verifying an AI answer comes down to tracing every factual claim back to a real, checkable source. First, ask the AI to cite the exact passage it used, not a vague reference to a whole document. Second, read that passage and confirm it actually supports the claim — citations can be real documents that still do not say what the answer implies. Third, open the original document at the cited location to make sure the passage exists and is in context. If a claim has no source, treat it as unverified until you find one.

A trustworthy AI answer is one you can check, not one you are asked to believe. The two properties that make checking possible are grounding and traceability: the answer should be built from a known body of documents rather than opaque training data, and each claim should link back to the specific passage it came from.

Tatsulok is built around this. It answers from the documents you provide — your library — and cites every answer to the exact source passage, with a highlighted preview of that passage and a direct link to the original document. Because answers are grounded in your own sources, you can confirm them rather than take them on faith.

Tatsulok puts the evidence next to the answer. Every claim is cited to the exact passage in your source document, and you see a highlighted preview of that passage plus a direct link to the original file, so confirming an answer takes a glance rather than a separate search.

Answers come only from the documents you upload — PDFs, Word files, spreadsheets, and slides — so there is a real source behind each one, and a built-in Google Scholar search lets you bring in published research when you need it. Your documents and prompts are private by default, encrypted at rest and in transit, never used to train AI, and covered by zero data retention with the model providers — so verifying answers never means giving up control of your sources.