You built a RAG pipeline. You embedded your documentation. You chunked it properly, tuned the retrieval, and the results are good. Today.
In three months, 30% of your knowledge base will be stale. In six months, your agent will be confidently answering questions with outdated information. This is the freshness problem, and almost nobody solves it at build time.
The silent decay
A documentation page gets updated. A product changes its API. A policy document gets revised. Your embeddings still represent the old versions. Your retrieval still returns them. Your agent still generates answers from them.
The user doesn't know the answer is stale. The agent doesn't know either. No error, no warning, no degradation signal. Just quietly wrong answers that erode trust over time.
Freshness strategies
Timestamp-aware retrieval. Every chunk gets an embedded timestamp and a source URL. At retrieval time, penalize chunks older than a threshold. This doesn't guarantee freshness, but it creates a natural decay that surfaces newer content.
Change detection pipelines. Monitor your source documents for changes. When a source changes, invalidate its chunks and re-embed. This sounds obvious but requires infrastructure: file watchers, webhook receivers, diff comparison, and incremental embedding jobs.
Confidence windows. Attach a "valid until" date to each chunk based on the content type. API documentation: 30 days. Legal policies: 90 days. Product descriptions: 14 days. When retrieval returns a chunk past its confidence window, flag the answer as potentially outdated.
User feedback loops. The fastest way to detect stale content is users telling you it's wrong. Build a thumbs-down button. Track which chunks generated flagged answers. Prioritize those for re-indexing.
The metadata investment
Most RAG tutorials focus on chunking and embedding quality. Those matter. But the metadata around each chunk, when it was embedded, where it came from, when it was last verified, how often it's retrieved, how often it's flagged, this metadata is what separates a demo RAG from a production RAG.
Invest in metadata from day one. You'll need it by month three.
If your RAG system is drifting and you need it to stay accurate, we design for that.