I understand that transformer-based language models can generate highly fluent responses, and that retrieval-augmented generation (RAG) is often used to improve factual grounding by supplying relevant external context. However, in practice, I still see cases where the model is given the correct retrieved passage, yet the final answer is partially incorrect, logically inconsistent, or confidently mixes correct and incorrect details. For example,...
Originally asked by Avalon Brooks on Stack Exchange (ai) (CC BY-SA 4.0).
