How to chat with a PDF and get answers you can actually trust
Chatting with a PDF means asking questions in plain English and getting answers grounded in the document. The technology behind it is retrieval-augmented generation — we index the PDF, find the passages relevant to your question, and the model writes the answer using those passages as evidence. Done well, every answer comes with page citations so you can verify it. Done badly, you get plausible-sounding hallucinations.
How chat-with-PDF works
When you upload a PDF we extract its text (running OCR if needed), split it into semantically meaningful chunks (typically 500–1500 tokens each), and create vector embeddings for each chunk. The embeddings get stored in a per-document index. When you ask a question, we embed the question, retrieve the most-relevant chunks, and ask the model to answer using only those chunks. Every answer includes the page numbers where the supporting passage was found.
The 'only those chunks' constraint is the difference between a tool that hallucinates and a tool that doesn't. If the answer isn't in the document, our tool says so rather than guessing. You can verify any answer by clicking the page citation and reading the passage yourself.
When chat-with-PDF is the right tool
Specific cases where it pays for itself in minutes:
- Long contracts with specific clauses. Ask 'what's the termination notice period?' instead of skim-reading 80 pages.
- Research papers. 'What was the sample size?' or 'Did they control for income?' — fast extraction of methodology details.
- Manuals and product documentation. 'How do I configure SSO for Azure?' — pull the procedure without searching the index.
- Financial statements. 'What was the year-over-year revenue growth?' — pull the number from the management discussion.
- Long-form interviews and transcripts. 'Did the candidate mention prior leadership experience?' — surface specific quotes.
Common mistakes
Patterns that lead to disappointing chat sessions:
- Asking yes/no questions about counterfactuals. 'Could this clause apply to subsidiaries?' is interpretation, not retrieval. The model will give you an answer; you should verify it with a lawyer.
- Trusting summary questions. 'Summarize this document' is best done with the Summarize tool, not the Chat tool. Chat is for specific, locatable answers.
- Asking about content that was filtered. If your PDF is an image-heavy presentation, the chat may miss things only present in slide images. Run OCR first.
- Not reading the citations. Even with grounding, models sometimes paraphrase aggressively. Click the page reference and read the original passage for any answer that matters.
- Asking vague questions. 'Tell me about the contract' returns vague answers. 'What's the indemnification cap?' returns a precise one.
Tips for better answers
Habits that consistently produce better chat output:
- Be specific. Use the names, terms, and section headings from the document. 'In Section 8.2, what's the exception to the limitation of liability?' beats 'is liability limited?'.
- Ask one question at a time. Multi-part questions sometimes get partial answers. Ask question A, get the answer, then ask question B.
- Use the chat to find passages, not to interpret them. 'Where does the document discuss data retention?' is a great chat question. 'Is this GDPR-compliant?' is a question for a lawyer.
- Read the cited page yourself. Treat the chat as a first reader, not the final reader. Chat surfaces; you decide.
- Save useful Q&A as a digest. Use the 'export chat' option to save a Q&A summary alongside the PDF for future reference.
Limits and pricing
Free tier: 5 questions per document. Pro: unlimited questions per document. Documents up to 1,000 pages and 200 MB. The chat respects the document — your PDF is not used for model training. Files are deleted within 60 minutes by default.