Scraping for RAG: Keeping Your Retrieval Index Fresh (and Why Staleness Hallucinates)
When a RAG system confidently states last quarter's price, last year's policy, or a fact that stopped being true in March, the reflex is to blame the model. Often the model did exactly its job: it grounded its answer faithfully in what retrieval handed it, and what retrieval handed it was stale. Your vector index is a cache of the world, and like any cache it goes wrong not by erroring but by…
Retrieval-augmented generation systems can confidently state outdated facts if the retrieval index is stale. The problem lies not with the model, but with the freshness of the information it retrieves. Keeping a vector index built from scraped web data fresh is crucial to prevent hallucinations. There are three main ways an index goes stale: stale content, missing content, and orphaned content.
The root cause is the index and the world drifting apart, which leads to models generating accurate but incorrect answers. A freshness failure in a data pipeline is a more specific diagnosis than a hallucination. To fix this, treat the retrieval index as a cache of the live web and continuously reconcile it against a moving source.
This involves detecting changes, refreshing content on a cadence matched to its volatility, invalidating missing or orphaned content, and keeping track of the age of each piece of information. By applying cache engineering principles to a corpus, teams can allocate crawl-and-embed resources efficiently and prevent stale retrievals from causing hallucinations.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
