AI document search with citations: 10 questions to ask before you buy

Most AI tools for searching documents now promise answers "with citations". For legal, finance and insurance teams, that phrase covers a wide range: from a link to a file somewhere in the library, to the exact sentence highlighted on the page. The difference decides whether anyone can rely on the answer.

Why "with citations" is not enough

A citation is only useful if a reviewer can confirm the answer from it in seconds. A link to a 90-page agreement does not do that. Neither does a highlighted chunk of text that does not actually contain the claimed figure. What reviewers need is the passage the answer relies on, on the original page, verified to exist.

The 10 questions

1. Does each citation open the exact passage on the original page? Ask to see it, not a description of it.
2. Is quoted text matched against the document? Or could the model produce a quotation that is not there?
3. What happens when the answer is not in the documents? It should say so, not answer from general knowledge.
4. How are calculations shown? The formula and each input, with the inputs cited.
5. How does it handle tables, footnotes and scanned pages? These are where answers break.
6. Does it understand which document governs? Amendments, later versions and order of precedence.
7. Can reviewers record decisions? Marking answers right or wrong, with names and dates.
8. Can the evidence be exported? Question, answer and source passages in a form an auditor can read.
9. Who can see which documents? Permissions should follow your existing access rules.
10. Where are documents stored, for how long, and can it run in your own cloud? And are documents used to train models?

A test you can run in a day

Demos use the vendor's documents. A short test on yours tells you more:

1. Pick 20 representative documents, including at least one scanned file and one with amendments.
2. Write 30 questions: 15 simple lookups, 5 table lookups, 5 calculations, and 5 whose answers are not in the documents.
3. For each answer, record whether it is correct, whether the citation shows the right passage, and how long it took to confirm.
4. Count the confident answers to the 5 unanswerable questions. That number matters more than any other.

How CiteLLM Ask answers these questions

CiteLLM Ask answers questions across your contracts, filings and policies, and every sentence carries its source: numbered citations open the exact PDF page with the passage highlighted. Quotations are matched against the document text, so a passage that is not in the document is never highlighted. Each answer can be exported as an audit report, and Ask runs in the CiteLLM cloud or on your own infrastructure.

Try it on sample filings, credit agreements, employment contracts, insurance policies and RFPs in the Ask demo, or book a demo on your own documents.

Frequently asked questions

What is the difference between AI search and RAG?

Retrieval-augmented generation (RAG) is the technique most AI search tools use: retrieve relevant passages, then have a model write an answer from them. The quality of the citations depends on how retrieval and answering are tied together.

Can the same tool extract structured data?

Extraction is a different job: pulling defined fields from each document into a system. CiteLLM does that with CiteLLM Extract, where every value carries its page and source passage.

How many questions should a pilot cover?

Enough to include the hard cases: tables, calculations, amendments and unanswerable questions. Thirty questions over twenty documents is usually enough to see the difference between tools.