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Asking questions about a document is one of the most useful applications of large language models. Instead of reading a 100-page contract cover-to-cover to find the termination clause, you ask 'What's the notice period for early termination?' and get a direct answer in 10 seconds. Instead of skimming a research paper for the methodology, you ask 'What's the sample size and what's the control?' and get specifics. The interaction is fundamentally different from reading — it's targeted extraction.

The grounding matters. A pure LLM asked 'What's the termination clause?' will make up a plausible-sounding answer; an LLM grounded in the PDF's text should answer based on what the document actually says. Our tool is designed to ground answers in the source text — when it works correctly, the AI tells you what's in the document, with the language of the document, not invented content. But grounding isn't perfect; always verify critical answers by reading the source.

Best practices for asking: be specific ('What's the termination notice period?' not 'Tell me about termination'), ask one question at a time (the AI handles one focused question better than a multi-part one), use follow-up questions to drill into specifics ('You mentioned a 30-day notice — is that calendar days or business days?'). Treat the conversation as a research session — you're exploring the document with an AI assistant, not getting definitive answers.