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Why does RAG quality drop as the corpus grows even though vector search still returns results?

Because the retriever always returns k results regardless of corpus size, and what degrades is whether the answer-bearing chunk is among them. As chunks pile up, more semantically similar but irrelevant chunks outscore the right one, so it gets pushed below the top-k cutoff. The search succeeds operationally on every call while recall of the chunk that matters collapses.

What is recall@k and why does it matter more than cosine similarity scores?

Recall@k is the fraction of queries where the answer-bearing chunk lands anywhere in the top k retrieved results, measured on a labeled set. Cosine scores are relative rankings, not calibrated relevance: a retrieval can fail completely while every returned chunk scores above 0.8. Recall@k is the only retrieval number that moves when the corpus-growth cliff happens.

How large should k be as a RAG corpus grows?

Roughly linearly with corpus size once you are past the cliff edge. Under a distractor-density model, the expected number of chunks outscoring the answer is corpus size times a per-query beat probability, and k must exceed that mean plus a margin of about two to three times its square root. A corpus needing k of 10 at a hundred thousand chunks can need k above 200 at ten million, which is only workable with a reranker compressing the final context back to a handful.

Does deduplication improve RAG retrieval quality?

Yes, and it is the only fix that improves recall while shrinking the index and cheapening queries. Near-duplicate chunks have near-identical embeddings, so copies of popular pages occupy multiple top-k seats and evict distinct answer-bearing chunks. Removing duplicates at ingestion, with exact hashing, MinHash, and a conservative embedding-similarity pass, deletes those distractors once for every future query.

When should you partition a vector index by tenant or topic?

Partition by tenant, or any key that arrives with the authenticated request, as early as possible: routing is then a zero-error lookup, each query searches a far smaller slice, and cross-tenant leakage becomes structurally impossible. Partition by topic only with caution, because a query router that predicts the partition from text has an error rate, and every misroute makes recall zero for that query.

A team's corpus grew from 1M to 10M chunks with k fixed at 10. Top-result cosine scores still average 0.85, so they conclude retrieval is healthy. What is wrong with that conclusion?

Cosine scores are relative rankings, not calibrated relevance, so they stay high while the answer-bearing chunk is pushed out of the top 10 by ten times as many expected competitors; only recall@k on a labeled set can see the failure, Similarity scores are orderings within whatever is in the index, and a crowded index produces plenty of high-scoring wrong chunks. Growing N tenfold multiplies the expected number of chunks that outscore the answer by ten, so recall of the answer-bearing chunk can collapse while every returned result still scores 0.85. The metric that moves is recall@k measured against labeled answer chunks, which is why it, and not any score threshold, is the alarm worth wiring.

Under the distractor model with p = 2e-5, recall@10 at 1M chunks is about 0.005. For tenant-scoped queries where each tenant owns about 40k chunks, which change most directly restores recall?

Pre-filter or partition by tenant, so ranking runs over ~40k candidates: lambda falls from 20 to 0.8 and recall@10 returns to essentially 1.0, Lambda is N times p. Shrinking the candidate set from 1M to 40k cuts lambda from 20 to 0.8, and a top-10 absorbs one expected competitor easily, so recall recovers to about 1.0. Raising k to 15 against lambda of 20 buys almost nothing, since recall around a Poisson mean is all-or-nothing until k clears it. Swapping stores does not change the geometry, and the temperature change addresses a symptom produced downstream of the actual failure.

Why must raising k to a few hundred be paired with a reranker, rather than passing all retrieved chunks to the LLM?

Because of both cost and quality: 250 chunks is ~75k input tokens per query (~$225k per 1M queries at $3 per 1M tokens) versus ~$4.6k reranked to 5, and long noisy context degrades answers even when the right chunk is present, The wide k exists to get the answer chunk into the candidate set; it is unusable as a final context. Feeding 250 chunks to the model costs around 75,000 input tokens per query, two orders of magnitude more than a reranked 5-chunk context, and the padding of near-misses actively harms answer quality via the lost-in-the-middle effect. The reranker cannot raise first-stage recall, it can only preserve it while restoring a small context, which is exactly the division of labor: the wide net owns recall, the reranker owns precision.

Six of the ten retrieved chunks for a query are near-copies of the same popular page, and the second answer-bearing chunk sits at rank 11. Why is deduplication at ingestion the right fix rather than handling duplicates at query time?

Because the duplicates crowd the answer chunk out inside the ranking itself: removing them at ingestion deletes those distractors from every future query's competition, shrinks the index, and cheapens queries, while query-time cleanup still lets copies consume the top-k seats, Near-duplicates have near-identical embeddings, so they rank as a pack and occupy seats that distinct chunks need, evicting the rank-11 answer chunk before any query-time step can help it. Deduplicating at ingestion, with exact hashing, MinHash for near-copies, and a conservative semantic pass, removes those worst-case distractors from the ranking competition permanently. It is the one fix in the lesson that raises recall while making the index smaller and every query cheaper, where every other fix trades compute or memory for recall.

The RAG Retrieval Cliff: Why Quality Collapses as the Corpus Grows

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