How to Avoid AI Hallucinations When Chatting With Documents

AI hallucinations happen when a language model states something false as if it were fact. It isn't lying on purpose — a model only predicts plausible-sounding text, and when it doesn't actually know an answer, it fills the gap with something that reads correctly but isn't grounded in any real source. Ask a general chatbot about a report it has never seen, and it may invent a number, a date, or a quote rather than say it doesn't know.

The fix isn't a smarter model — it's giving the model something real to answer from. That's the idea behind retrieval-augmented generation (RAG): instead of answering from memory, the model is handed the actual passages from your documents at the moment you ask, and writes its answer from those passages instead of guessing.

A document-chat tool built on RAG never answers purely from what the underlying model memorized during training. Each file you upload is split into chunks and embedded, so when you ask a question, the system retrieves the chunks that are actually relevant and hands only those to the model. Doxy works this way whether you chat with a PDF or run Q&A across a whole projectof files — the answer is built from passages that exist in your documents, not recalled from the model's general training data.

Grounding an answer in your documents helps, but the part that actually lets you catch a mistake is the citation. See how document Q&A with citations works for the full workflow: every answer points back to the exact passage it came from, so instead of trusting the model's word, you click through and check it yourself. A tool that can't show its source is asking you to trust it blindly — a tool that cites its source is asking to be checked.

Pure semantic retrieval can still miss the one chunk that contains an exact order number, a product code, or a specific clause, simply because the wording doesn't match closely in meaning — and a model asked to answer anyway may fill that gap with a guess. That's why Doxy pairs semantic search with keyword matching, a technique covered in our hybrid search explainer, so an exact term in your documents gets found even when it isn't semantically close to how you phrased the question. The fewer gaps retrieval leaves, the fewer gaps the model has to fill with a hallucination.

Doxy is built so every answer is grounded and checkable: upload your files or paste a link, ask a question, and read the citation before you trust the answer. Use it as a NotebookLM alternative for everyday documents or as an AI research assistant for work where a wrong answer actually costs you something. Upload a document you already know well and ask it something specific — then check the citation against what you know is true. If the answer still comes back wrong, vague, or incomplete, the fix is often the question or the scope, not the tool.