Enterprise RAG (Retrieval-Augmented Generation) systems struggle with knowledge decay, where outdated information undermines credibility. Even with optimized retrieval processes, stale knowledge can lead to significant issues. For example, legal firms may rely on old case precedents, and medical systems may provide outdated treatment protocols. Research shows that 60% of RAG projects fail not due to retrieval issues but because they can't keep data fresh as knowledge bases expand. As these systems grow, the time taken to update documents increases, leading to longer intervals where outdated information is served. Static architectures are particularly problematic when information changes frequently. Traditional RAG systems process documents in batch cycles, which can become cumbersome as the number of documents grows. A company with thousands of documents may experience updates that take hours, resulting in stale information being accessible for days. Enterprises often overlook staleness metrics, focusing instead on retrieval speed and accuracy, creating a blind spot that can lead to major errors, such as providing incorrect legal or medical advice. To combat these issues, the article advocates for incremental indexing and dynamic retrieval methods. Incremental indexing processes updates in real-time, only re-embedding modified chunks of documents instead of re-indexing everything. This drastically reduces update time from hours to seconds. Dynamic retrieval with freshness awareness adds a staleness penalty in ranking documents, ensuring that the most relevant and recent information is prioritized during queries. This approach helps maintain an accurate and up-to-date knowledge base, critical for operational integrity across industries.