RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production

Standard Retrieval-Augmented Generation (RAG) systems are blind to time, often retrieving outdated information because vector search prioritizes semantic similarity over freshness. To fix this, a temporal layer can be implemented between the retriever and the LLM to rerank search results. This layer classifies documents based on their validity state (expired, valid, temporal) and document kind (static, versioned, event). The system then applies rules to hard-remove expired content, boost time-sensitive information, and apply a time-decay function to favor newer versions of documents, ensuring the LLM receives context that is both relevant and current.