S.s

I've been building an AI document reader (DocMind upload a PDF/DOCX, get a summary, key points, plain-language explanation, or chat with it directly).

The hardest problem wasn't extraction or summarization it was getting the model to admit when something isn't actually in the document instead of confidently making something up. Early on, I had a small model invent an entire fake financial report for a document that was actually about fintech hardware fluent, convincing, completely wrong.

What ended up working: switching to a stronger primary model, adding a "groundedness check" that verifies the output actually shares vocabulary with the source text before accepting it, and falling back to a retry (or a different provider entirely) if it fails that check.

Curious how others building with LLMs are handling this are you doing something similar (grounding checks, RAG-style citations, confidence scoring), or found a different approach that works better? Launching my product Wednesday, but genuinely more interested in how other people are solving this specific problem right now.