Document AI

Picture this: You’re asking an AI about cancer treatments. It sounds super confident and gives you detailed answers. But here’s the problem — it just made up a medical study that doesn’t exist. TL;DR RAG fixes LLM hallucinations by grounding answers in retrieved documents. Pipeline: chunk documents → embed → store in vector index → retrieve at query time → generate. Use RAG for knowledge-intensive tasks (legal, medical, finance) where accuracy is non-negotiable. Evaluate with RAGAS or custom metrics: faithfulness, answer relevancy, context recall. That’s not just embarrassing. When we’re talking about healthcare, finance, or legal advice, these AI “hallucinations” can be downright dangerous.

Document summarization is a critical NLP task that helps users quickly grasp key information from long documents. But how do you know if your model is actually working? This guide shows a workflow that starts with evaluation and acceptance criteria before touching models — the approach that got a finance report summarizer from prototype to production in three weeks.