RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production
中文摘要
RAG系统常忽视时间维度导致信息过时。作者为此构建了一个时间层，以确保系统能检索并提供最新的准确答案。
English Summary
To prevent RAG systems from providing outdated answers, the author built a temporal layer to prioritize and filter for the most current information.
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer. Not obviously wrong — just outdated enough to mislead. That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw. The fix wasn’t in the retriever or the model. It was in the gap between them. I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches. The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.