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retrieval-quality-fundamentals: structure & scope pass

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- Add "why measure retrieval quality" lead-in paragraph
- Expand ANN on first use; flag sparse vectors as out of scope
  for layer 1 (they use exact matching)
- Add LLM-as-judge to layer-2 ground-truth options
- Break the Tooling bullet into per-layer recommendations: Web UI
  for L1, ranx for L2, Ragas/Phoenix/DeepEval for L3
- Reorder Quality Metrics so layer 1 (ANN recall formula + exact
  kNN equivalence) comes before the generic layer-2 relevance
  metrics; trim a redundant sentence
- Add end-to-end answer quality as a distinct third layer in the
  prose intro so it matches the ladder table's four rows
- Standardize vocabulary on "layer" (was mixing "level" in the
  intro with "layer" everywhere else); update the section anchor
  to #connecting-the-layers-in-practice in this file and the two
  cross-linking tutorials

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dylan Couzon
2026-04-22 22:31:35 -04:00
co-authored by Claude Opus 4.7
parent 5748d6db8c
commit e2df842fb5
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qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md
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| Time: 15 min | Level: Intermediate | | |
|--------------|---------------------|--|----|
This tutorial measures **layer 1** of the <a href="/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/#connecting-the-levels-in-practice" target="_blank">evaluation ladder</a>, **ANN precision**: the share of Qdrant's approximate nearest-neighbor top-k that appears in the exact kNN top-k. For retrieval relevance (layer 2), see the <a href="/documentation/tutorials-search-engineering/retrieval-quality-golden-set/" target="_blank">Building a Golden Query Set</a> tutorial.
This tutorial measures **layer 1** of the <a href="/documentation/tutorials-search-engineering/retrieval-quality-fundamentals/#connecting-the-layers-in-practice" target="_blank">evaluation ladder</a>, **ANN precision**: the share of Qdrant's approximate nearest-neighbor top-k that appears in the exact kNN top-k. For retrieval relevance (layer 2), see the <a href="/documentation/tutorials-search-engineering/retrieval-quality-golden-set/" target="_blank">Building a Golden Query Set</a> tutorial.
We'll measure Qdrant's ANN precision with `precision@k` and tune HNSW parameters to control the precision/latency trade-off. The ANN algorithm is one of several levers that shape retrieval quality in a production pipeline, alongside the embedding model, retrieval strategy (dense, sparse, hybrid, and multi-vector), filtering, and reranking.