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A growing corpus can make a retrieval-augmented generation (RAG) system slower and more expensive without making its answers better. More documents can also mean more distractors, while chunk boundaries, ranking, filters, and query type determine whether the right evidence reaches the model. The reliable fix is to evaluate retrieval strategies on your own representative queries and track relevance alongside latency, throughput, and cost—not to assume that a particular search method or setting wins everywhere.
Why can retrieval get worse as a corpus grows?
Adding documents expands the pool of potential evidence, but it also expands the pool of near-matches and irrelevant passages. A system can retrieve a plausible-looking chunk that misses an exact identifier, separate a definition from its context, or rank a useful passage below less useful candidates. If filtering is too broad—or incorrectly excludes documents—the correct evidence may never reach the retriever.
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Scale also affects operations: indexing and updates consume resources, and additional search or reranking stages can increase response time. Track retrieval quality together with end-to-end latency and cost so that an apparent relevance gain is not accepted without accounting for its operational price. Benchmarks can reveal useful hypotheses, but their scores are not interchangeable when they use different corpora, queries, labels, metrics, or protocols.
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- Exact terms are missed: Queries containing names, product codes, identifiers, or rare phrases may benefit from testing lexical retrieval alongside semantic search.
- Paraphrased questions are missed: Check whether the retrieval method finds relevant passages when the query uses different wording from the source.
- The right source is found, but the passage is weak: Inspect whether chunk boundaries split a definition from its explanation, or include so much unrelated text that the useful evidence is hard to surface.
- Useful candidates appear too low in the list: Compare the initial ranking with a reranked list, and verify the ordering against judged examples.
- Relevant material is absent from results: Check metadata-filter logic and scope. A filter that narrows the candidate set incorrectly can hide the answer.
- Answers degrade despite more indexed material: Compare the retrieved passages and the final answers against a stable evaluation set. A larger index or higher throughput alone does not demonstrate improved relevance.
Should you use BM25, dense retrieval, or hybrid search?
Treat each approach as a candidate to measure, not a rung in a universal hierarchy. BM25 uses lexical matching; dense retrieval is intended to find semantically related text; hybrid retrieval combines lexical and semantic signals. Their relative value depends on the query mix, corpus, relevance judgments, and implementation.
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| Approach | What to test | Useful comparison questions |
|---|---|---|
| BM25 or other lexical retrieval | Queries with exact names, identifiers, codes, and rare terms | Does it surface exact evidence reliably, and how do relevance, latency, and cost compare on the same queries? |
| Dense retrieval | Paraphrases and queries whose wording differs from the source | Does semantic matching find evidence that lexical matching misses, and does it introduce distractors? |
| Hybrid retrieval | Queries where exact terms and semantic similarity both matter | Does combining signals improve judged relevance enough to justify its added complexity and operational cost? |
A 2026 corpus-scaling preprint evaluated 28 nested tiers, from roughly 1,000 to 512,000 documents. It held a set of questions and a base of relevant and adversarial documents fixed while increasing corpus size. Under that study’s protocol, BM25 led accuracy from mid-scale onward; that finding does not establish that BM25 always wins. At full scale, the paper reported scores of 69.4 for Agent+BM25, 36.9 for raw-file agency, and 54.8 for native BM25 on the same 150 questions. These are reported scores, not percentages. The study also reported 39 times more query tokens per answer at the bedrock for its File-System Agent relative to its BM25 baseline—a result specific to that setup. Wang et al., “Which RAG Paradigm Wins at Scale?”
A 2026 scientific-RAG preprint reported robust hybrid retrieval in its setup. Its reported Recall@10 was 1.000 at the 1K and 15K tiers, but the relevance labels were pseudo-relevance judgments derived from the hybrid system itself. That limits how far the result can be generalized as independent proof of hybrid retrieval quality. A separate 2026 biomedical hybrid framework was a pilot proof of concept on 25 queries; its reported 100% grounding figure is not evidence of general production reliability. Apurba et al., “SciRet”; Irany and Akwafuo, “A Hybrid Retrieval and Reranking Framework for Evidence-Grounded Retrieval-Augmented Generation”
How should you choose chunk size?
Chunk size and overlap are inputs to tune against the material and the downstream task, not universal recipes. A chunk that is too narrow may separate an answer from the context that makes it meaningful; a broad chunk may include unrelated text that weakens retrieval or consumes more of the model’s context.
- Choose a representative set of documents and queries, including examples where the answer depends on nearby context.
- Compare a small number of chunking strategies while keeping the query set and other retrieval settings stable.
- Judge whether retrieved chunks contain the answer-bearing context, then review the resulting answers as well as retrieval metrics.
- Record the impact on indexing, query cost, and latency before choosing a setting.
For its vector store file API, OpenAI documents an automatic default of 800 tokens per chunk with 400 tokens of overlap. Its static chunk settings allow 100–4,096 tokens, with overlap no greater than half the chunk size. These are limits and defaults for that API implementation, not evidence that those values are optimal for other corpora or RAG systems. OpenAI API Reference: Vector store files
Why can reranking make results worse?
A reranker reorders candidates returned by an initial retrieval stage. It cannot recover relevant material that was never retrieved, and its scoring may not transfer well to a different domain. Compare the candidate list before and after reranking, use held-out queries from the target domain, and include the added latency in the evaluation.
The domain dependence is visible in two 2025–2026 preprints with different study conditions. SciRet reported that an MS MARCO-trained reranker reduced precision on scientific material. A 2025 financial-domain study reported a 59% absolute improvement in MRR@5 for cross-encoder reranking at selected parameters. In its financial Q&A comparison, the same paper reported a 65% win rate with 0.2 seconds of additional latency for small-to-big retrieval against its baseline. These are study-specific outcomes, not comparable scores or general promises of a reranker gain. SciRet; Lumer et al., “Rethinking Retrieval”
Small-to-big retrieval is another strategy to test: retrieve a smaller, focused unit, then expand the context supplied to the model. The financial study’s result is a reason to measure this option on relevant tasks, not to assume that expansion will help every corpus.
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- Create a stable query set. Include common requests, exact-term queries, paraphrases, difficult cases, and adversarial queries that are likely to retrieve convincing distractors.
- Judge relevant passages. Record which passages contain useful evidence for each query. Keep evaluation judgments independent of the retrieval method where possible, especially when comparing methods.
- Compare one change at a time. Run lexical, dense, and hybrid retrieval against the same queries and corpus. Then test chunking, filters, or reranking without changing several variables at once.
- Choose metrics that reflect the task. Use Recall@k when missing relevant material is costly, or ranking measures such as MRR when early rank position matters. Add end-to-end answer review to check whether retrieved evidence supports the response.
- Measure operating impact. Record latency—including reranking—throughput, index-building and update costs, and query costs. Do not compare headline metrics across studies as if they shared a benchmark.
- Repeat after material changes. Re-evaluate when the corpus, chunking, query distribution, filters, or retrieval configuration changes, using the same stable set to spot regressions.
Production guidance from Pinecone recommends testing query results in the application context and lists reranking, metadata filtering, hybrid search, and chunking strategies among relevance-related options. Its checklist also describes batching, parallel operations, and object-storage imports for throughput, plus namespaces, metadata filtering, connection reuse, and same-region deployment as latency tactics. Treat these as options to validate against your workload, not guaranteed performance improvements. Pinecone: Production checklist
Quick Recap
What should you check before production?
- Relevance: Confirm the system retrieves useful evidence for representative and difficult queries, not just that indexing completed successfully.
- Filters and namespaces: Verify that each filter returns the intended documents and does not exclude relevant evidence.
- Latency and capacity: Measure end-to-end response time at expected concurrency, including extra retrieval and reranking stages.
- Ingestion and updates: Check that batching, parallel work, or object-storage imports fit the update pattern without compromising freshness.
- Deployment path: Assess whether connection reuse and same-region placement suit the architecture, and verify their actual effect in the target workload.
- Cost and diagnosis: Monitor build and query costs alongside relevance so regressions can be traced to data, chunking, filters, or ranking rather than hidden by aggregate throughput.
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