Equation 1 · Ten Failure Modes That Define Production RAG
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Vu and colleagues built FreshQA specifically to probe this failure in language models’ own parametric knowledge, and found that every model they tested, regardless of size, struggled on questions involving fast-changing facts, which motivated search augmentation as a mitigation in the first place [ 2 ] . Their diagnosis transfers directly to the retrieval layer itself: a store consulted at query time is a snapshot of a particular assembly moment, and its staleness is a function of how long ago that moment was, not of how good the retriever scoring the snapshot happens to be. Under a periodic full rebuild with interval , and changes to the corpus arriving at roughly uniform times…
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Vu and colleagues built FreshQA specifically to probe this failure in language models’ own parametric knowledge, and found that every model they tested, regardless of size, struggled on questions involving fast-changing facts, which motivated search augmentation as a mitigation in the first place [ 2 ] . Their diagnosis transfers directly to the retrieval layer itself: a store consulted at query time is a snapshot of a particular assembly moment, and its staleness is a function of how long ago that moment was, not of how good the retriever scoring the snapshot happens to be. Under a periodic full rebuild with interval , and changes to the corpus arriving at roughly uniform times within that interval, the expected age of an index entry just before the next rebuild and the worst case obey
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