A good document-AI system understands plain language, so you do not need to learn a query syntax. But how you phrase a question still shapes the answer you get back. A few habits make the difference between a vague summary and a precise, cited result - no technical background required.
Be specific about what you want
"Tell me about our contracts" invites a vague answer. "Which supplier contracts cap liability below €100,000?" gives the system a clear target to retrieve against. The more concrete the thing you are looking for - a clause type, a threshold, a date range - the sharper the result.
Ask one thing at a time
Bundling several questions into one sentence forces the system to split its attention. Ask for the liability caps first, then follow up about arbitration terms. Sequential questions also make the answers easier to verify against their sources.
Name the scope when it matters
If you only care about a particular client, period, or document type, say so. "In our 2024 EU client contracts, which include a force majeure clause?" narrows retrieval and avoids answers drawn from material you did not mean to include.
Use the citations
Every answer comes with sources - document, section, page. Treat them as the point, not a footnote. Open the cited passage to confirm the answer says what you think it says before you rely on it. This habit is what turns a fast answer into a defensible one.
- Prefer concrete targets over open-ended prompts.
- One question per query; follow up rather than bundle.
- State the scope - client, date, document type - when it matters.
- Always check the cited source before relying on the answer.
If the answer is "not found"
A good system will tell you when it cannot find a grounded answer. That is information, not a failure - it often means the document is not in the connected archive, or the question needs rephrasing. Try naming the scope differently, or confirm the source material is actually indexed.