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How are you handling AI hallucinations in document processing?

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Hey Product Hunt community! đź‘‹

We’ve all been through this pain: you feed a 50-page PDF, financial report, or contract into an LLM, ask for a quick summary, and it confidently hands you data that is completely made up.

If a creative writing bot hallucinates, it’s fine. But when it happens during document processing—like in your RAG pipelines or data extraction—it’s an absolute disaster.

Honestly, my team and I have been losing sleep over this exact problem. It’s why we are building Ethos by DocuShell (it's an open-source verifier for document citations, source evidence, and RAG). While building it, we realized just how easily LLMs get totally confused by simple tables or negative sentences (like missing a "not" or "except").

We have our own way of handling it, but we know there isn’t just one right answer. I really want to know how other builders and founders here are tackling this.

What kind of document hallucinations are driving you crazy the most?

  • Fake numbers: Making up metrics in financial statements?

  • Connecting wrong dots: Stitching two unrelated facts together to create a fake story?

  • Getting lost: Context drifting when dealing with huge, multi-page files?

And how are you guys fighting back? Are you fixing this at the infra level, playing around with prompts, or just relying on a human-in-the-loop to double-check everything?

For example, are you using:

  1. Advanced RAG: Better chunking, hybrid search, or re-ranking?

  2. Self-Correction: Making the LLM verify its own sources against a strict schema?

  3. UI Guardrails: Forcing the system to visually highlight exactly where the answer came from?

Drop your tech stack, your worst horror stories, or your current workarounds below.

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For document processing, I try not to trust the answer unless the source is visible next to it. Better chunking and re-ranking help, but the biggest improvement is usually forcing every claim to point back to the exact page, table, or sentence it came from. If the model can’t show the evidence, I’d rather have it say “not found” than give a confident summary.

 Exactly. That’s the part we care about too.

Showing the source next to the answer is a huge step, but we’ve also seen cases where the model points to the right page and still gets the meaning wrong. It may misread a table, miss a “not,” or stretch the evidence too far.

That’s why we’re building Ethos: first find the source, then check if the claim is actually supported by it. If it isn’t, the answer should say “not found” or “unsupported” instead of sounding confident.

We open-sourced it here if you want to take a look:

Curious, where do you see this most often: summaries, data extraction, or RAG Q&A?