Chat with PDF without hallucinations

Most "chat with PDF" tools you can try this afternoon have the same failure mode: they sound confident, the answer is plausible, and a third of it is invented. That works on a 5-page test PDF. It collapses on the messy 600-page packet you actually wanted to ask.

Why naive PDF chat hallucinates

Three failure points compound. One, the PDF gets blindly chopped — page boundaries become semantic boundaries, which is wrong. Two, retrieval is "vector-only," which catches gist but misses exact terms (case numbers, SKU codes, drug names). Three, the model is asked to answer even when nothing relevant came back, so it fills the gap with fluent-sounding text.

What grounded retrieval actually looks like

• Overlapping chunks, not pages. Each chunk carries enough surrounding context to stay coherent on its own.
• Hybrid retrieval: vector embeddings for meaning, BM25 for exact terms, fused with reciprocal rank fusion. Either alone is brittle; together they're robust.
• A hallucination guard. If the top retrieved chunks fall below a similarity floor, the model is instructed to refuse rather than invent.
• Citations as a first-class output, not a post-hoc feature. Every claim in the answer maps to a chunk, with file, page, and similarity score.

Why citations matter more than people admit

A great-sounding answer pulled from the wrong chunk is worse than no answer — it's confident misinformation, and you'll never know unless you check. Citations make the model honest by construction, because the answer can be audited in two clicks.

What to look for in a chat-with-PDF tool

• Does it show the exact source chunks behind every answer?
• Does it OCR scans and image-only PDFs?
• Does it keep working when your question's wording doesn't appear in the doc?
• Does it refuse, when it should?

That's the bar SeekFiles AI builds to.