Short answer. Giving an AI your document does not make its answer faithful to it. Even when models summarise a text they were handed, public tests find some that add information that is not there. To verify an answer against an approved source, force a quote for every claim, check that each quote exists word for word, check that it supports the claim, then check that the document is the current version. Compare more than one model on the same document to find the passages worth reading yourself. No tool replaces a human sign-off on a decision that matters.
You have a policy, a contract, a handbook or a set of approved facts. An assistant answers a question about it. The question is whether the answer is what the document says. This guide gives a method you can run by hand today, says what each step catches and misses, and is clear about what Satcove does and does not do here.
Why is "it read my document" not enough?
Because summarising a supplied text is itself a source of errors. The Vectara Hallucination Leaderboard measures exactly this: how often a model adds information that is not in a document it was asked to summarise. On its latest update (2026-10-22, 111 models), rates run from 1.8% for the best model to 24.2% for the worst, and OpenAI's own listed models range from 3.1% to 23.3%. These are rates on that benchmark's news-style documents, not on your policy, but the lesson carries: grounding lowers the risk without removing it.
Four failure types matter when you check against a document:
- Invented content. A figure, a deadline or a clause that is not in the source.
- Real quote, wrong meaning. The sentence exists, but it does not support the claim, or the model dropped an exception.
- Omission. The answer is true but leaves out the condition that changes the decision.
- Wrong or stale source. The model used an old version, a different policy, or its own memory instead of your text.
How do you verify an AI answer against an official source?
1. Fix the source and its version
Decide which document is authoritative and write down its name, version and date. Put the text in the prompt, or attach it, so the model is not answering from memory. If the policy changed last quarter, the old PDF in someone's folder is the most likely cause of a wrong answer.
2. Make the answer quote its evidence
Ask for a short, rigid format: the answer in one or two sentences, then for each claim the exact sentence from the document in quotation marks, with the section or page. Add the rule that matters most: "If the document does not say it, write NOT IN THE DOCUMENT. Do not guess." A claim with no quote is a claim you do not trust.
3. Check every quote exists, word for word
This step is mechanical and catches invented content. Search the source text for each quoted string. A quote that is not found, even slightly reworded, is a red flag. A short script that tests whether each quoted string is a substring of the document does it in seconds, and a plain find-in-document does it by hand.
4. Check that the quote supports the claim
This is the step machines do worst, so a person should do it on anything important. Read the quote and the claim side by side and ask three things. Does the quote say what the claim says? Does an exception, a condition or a date in the surrounding text change it? Is a number the same number? A second model can act as a first screen, but it shares blind spots with the first, so use it to rank which claims to read, not to approve them.
5. Compare models and read where they split
Ask two or three different models the same question on the same document, with the same quoting rules. Where they agree, the odds of an error are lower but not zero: in our own test of 2,500 questions, answers on which every model agreed were right 94.0% of the time, and wrong in 73 of 1,221 cases. Where they differ, you have found the exact passage to read. That is the cheapest use of several models.
What does each check catch?
Which tools can help?
Three kinds exist, and they do different jobs.
- Retrieval and citation features in assistants and document tools show which passage an answer came from. They help step 2, but you still need steps 3 and 4.
- Faithfulness-scoring models estimate whether a summary is supported by its source. Vectara's HHEM, the evaluator behind the leaderboard above, is one example. Treat a score as a screen, not a verdict.
- Human review workflows for regulated or high-impact decisions, where a named person signs off.
For a manual method that works on any answer, see our guide to how to cross-check AI answers and the AI answer reliability page.
What does Satcove do, and not do, here?
Satcove does not check an AI answer against your private policies, and it is better to say so plainly.
- Consensus. Ask the same question to six models and see where they agree and where they split, with an agreement score. In Cove chat you can attach a document and run the question across the models. Use it for step 5. Agreement is not proof.
- Verify. Satcove Verify checks the values of a CSV row by row against live web sources, with a confirmed, divergent or unverified status per field. It does not read PDFs or free text, so it is not a tool for checking answers against a private policy.
- Privacy. Before uploading sensitive documents, read what the Privacy Shield can and cannot cover. Automated detection can miss contextual identifiers.
For teams that need AI answers checked against internal sources at scale, see Satcove for enterprise. The five steps above work without any tool.
FAQ
How do you verify an AI answer against an official source?
Fix the authoritative document and its version, make the answer quote its evidence, check each quote exists word for word, read each quote next to its claim, and keep a person responsible for the final call. Compare several models to find the passages most worth reading.
What tools help verify AI answers against approved policies and facts?
Retrieval and citation features show the source passage. Faithfulness-scoring models, such as Vectara's HHEM, estimate whether a summary is supported by its source. Neither replaces reading the passage. Satcove can compare six models on the same question, but it does not check answers against private policies.
Can AI check whether an answer matches a document?
Partly. A model can flag likely mismatches, and a dedicated faithfulness model can score support. Both make mistakes, and a second model shares blind spots with the first. Use them to decide what to read, not to approve.
Does Satcove verify answers against my own documents?
No. Satcove Verify checks CSV values against live web sources and does not read PDFs or free text. Satcove consensus compares six models' answers and can run a question over an attached document, but agreement among models is a signal, not a check against your policy.
What if the model says "NOT IN THE DOCUMENT" too often?
That is the safe failure. It usually means the question needs the document's exact wording or that the document does not cover the case, which is useful to know. Loosen the rule only if you will verify the extra answers.
How often should answers be audited?
On a sample at first, then whenever the document, the model or the use changes. Keep a log of questions, answers, quotes and who approved them.
Figures: Vectara leaderboard read 2026-10-26 (updated 2026-10-22); Satcove benchmark, 2,500 questions, run 2026-10-22. Product descriptions as of 2026-10-26.