If you have built anything withretrieval-augmented generation(RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top few that look similar to the question, and hand them to the model. For “What was our Q3 refund policy?” This works beautifully. For “What are the recurring themes across two years of customer complaints?”
If you have built anything withretrieval-augmented generation(RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top few that look similar to the question, and hand them to the model. For “What was our Q3 refund policy?” This works beautifully. For “What are the recurring themes across two years of customer complaints?”