Why Does RAG Evaluation Cost Matter More as the Document Library Grows at the Same Query Volume?

RAG evaluation matters more as a library grows because retrieval ambiguity and regression surface expand even when users ask the same number of questions.

A family may still run 100 searches per week after its archive grows from 10,000 to one million chunks. Yet every query now has more near-duplicates, stale versions, languages, and permission boundaries that can compete for rank. A small fixed test set covers a shrinking fraction of possible retrieval failures across every newly added content stratum.

More Candidates Create More Ways to Retrieve Plausible Errors

Approximate search does not score every passage exactly. As the corpus expands, more chunks may sit near a query, including redundant and outdated evidence. Precision can fall even if query count and top-k remain fixed.

A controlled study of retrieval strategies compares dense, hybrid, reranked, and expanded retrieval under fixed generation conditions, showing that strategy changes produce measurable precision and recall tradeoffs.

Evaluation must therefore inspect relevance, rank, version, and permission, not just whether an answer exists. More documents also increase the chance that a fluent answer cites a plausible but nonauthoritative duplicate.

Coverage and Labeling Grow With Corpus Diversity

A test set represents document types, languages, dates, entities, and query intents. When the library gains new families of content, the old questions no longer sample the whole risk surface. Adding coverage requires relevance judgments and expected evidence, even with unchanged production traffic.

Research on information coverage argues that classic precision and recall may miss whether results cover distinct information needs. Corpus diversity can rise faster than query volume.

Each index, chunker, embedding model, filter policy, and reranker variant multiplies comparisons. Automated judges reduce labor but add inference cost and their own calibration work. Evaluation cost is the price of knowing whether corpus growth changed behavior.

Where Library Size Is Not the Main Cost Driver

Index size may have little effect when queries target unique identifiers and deterministic filters narrow candidates first. A larger corpus of homogeneous duplicates may add storage without adding meaningful query diversity.

A framework for claim verifiability decomposes queries and answers into coverage and verifiability units, showing that evaluation burden depends on claim structure as well as corpus size.

The mechanism also fails if evaluation cost is dominated by expensive generation or human review per answer. In that case, library growth is secondary. More tests are not automatically better; redundant questions can raise spend without increasing risk coverage.

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Tie Evaluation Spend to New Corpus Risk

Maintain a core regression set, then add stratified questions only when the library gains a new language, document type, permission class, time period, or high-value entity. Label required evidence and known hard negatives. Run retrieval-only metrics before expensive generation metrics.

Connect test updates to index freshness events so newly indexed or missed files trigger targeted evaluation rather than a full unstructured rerun. Preserve a fixed holdout for trend comparison.

Track cost per covered stratum and defect found, not cost per production query. Sample routine low-risk strata and fully test permission-sensitive or frequently changed content. If scores drift only in one new stratum, repair and rerun that slice before expanding the entire suite.