From 4d5a3b8f60a96694b757ffbf2d9d663dba92bc2b Mon Sep 17 00:00:00 2001 From: Dylan Couzon Date: Thu, 23 Apr 2026 23:40:09 -0400 Subject: [PATCH] ann-precision: add Prerequisites block and rename closing to Next Steps Adds an explicit Prerequisites line matching the pattern in Measuring Retrieval Relevance and Evaluating Pipeline Output Quality. Renames "Wrapping Up" to "Next Steps" for naming consistency across the series. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../tutorials-search-engineering/retrieval-quality.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md index 489ba79f1..337ae537a 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md @@ -14,6 +14,8 @@ weight: 5 This tutorial focuses on **ANN precision**: how closely approximate nearest-neighbor (ANN) search matches exact kNN search. To measure ANN precision, you compare Qdrant's approximate top-k against the exact kNN top-k using `precision@k`, then tune HNSW parameters to trade memory and build time for higher precision. +**Prerequisites.** A Qdrant collection with your corpus indexed. For the CI section, Python with `qdrant-client` installed. + ## The Four Layers of Retrieval Evaluation Retrieval quality operates at four layers. Each catches different failure modes at a different cadence and cost. This tutorial covers layer 1. @@ -89,7 +91,7 @@ def avg_precision_at_k( Wire it into CI and fail the job when precision falls below your target threshold. This catches regressions from embedding model swaps or index config changes before they reach production. -## Wrapping Up +## Next Steps Measuring ANN precision keeps HNSW tuning honest. The Search Quality tab gives you a quick interactive read; the Python helper plugs into CI to catch regressions after embedding model changes or index config updates.