rename Building a Golden Query Set to Measuring Retrieval Relevance

Aligns the layer-2 tutorial title with the parallel "Measuring X" /
"Evaluating X" pattern used by the other two and maps directly to the
four-layer framework. Slug stays the same to preserve URLs and the
golden-set artifact identity in the path. Also updates the nav descriptions
to reflect the tutorial's full scope (build + score, not just build).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

qdrant-landing/content/documentation/headless/content/tutorials/search-engineering.md

@@ -6,7 +6,7 @@
 | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
 | [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN precision with the Web UI and tune HNSW parameters. | <span class="pill">Web UI</span> | 15m | <span class="text-green">Beginner</span> |
-| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
+| [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Build a labeled golden set and score retrieval relevance with ranx. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
 | [Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/) | Score a RAG pipeline with Ragas and isolate retrieval vs generation failures. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
 | [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |

qdrant-landing/content/documentation/tutorials-lp-overview.md

@@ -69,7 +69,7 @@ partition: develop
 | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
 | [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure ANN precision with the Web UI and tune HNSW parameters. | <span class="pill">Web UI</span> | 15m | <span class="text-green">Beginner</span> |
-| [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Generate ground truth data at scale and avoid data leakage. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
+| [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/) | Build a labeled golden set and score retrieval relevance with ranx. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
 | [Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/) | Score a RAG pipeline with Ragas and isolate retrieval vs generation failures. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Reranking for Better Search](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
 | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |

qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md

@@ -1,11 +1,11 @@
 ---
-title: Building a Golden Query Set
+title: Measuring Retrieval Relevance
 weight: 6
 aliases:
   - /documentation/tutorials/retrieval-quality-golden-set/
 ---
 
-# Building a Golden Query Set
+# Measuring Retrieval Relevance
 
 | Time: 40 min | Level: Intermediate |  |    |
 |--------------|---------------------|--|----|

qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output.md

@@ -15,7 +15,7 @@ To measure pipeline output quality, you run your golden query set through the fu
 
 For orientation on the four layers of retrieval evaluation and where this tutorial fits, see [Measuring ANN Precision](/documentation/tutorials-search-engineering/retrieval-quality/#the-four-layers-of-retrieval-evaluation).
 
-**Prerequisites.** A Qdrant collection with your corpus indexed (chunk text in a `text` payload field), a labeled golden set (see [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)), LLM access for generation and judging, and Python with `ragas` installed. The Wiring section shows the exact entry shape this tutorial expects.
+**Prerequisites.** A Qdrant collection with your corpus indexed (chunk text in a `text` payload field), a labeled golden set (see [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)), LLM access for generation and judging, and Python with `ragas` installed. The Wiring section shows the exact entry shape this tutorial expects.
 
 ## Wiring the RAG Pipeline
 

qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md

@@ -19,7 +19,7 @@ To measure ANN precision, you compare Qdrant's approximate top-k against the exa
 Retrieval quality operates at four layers. Each catches different failure modes at a different cadence and cost. This tutorial covers layer 1.
 
 - **Layer 1: ANN precision** (this tutorial). How closely approximate nearest-neighbor search matches exact kNN. Run on every index or embedding change.
-- **Layer 2: Retrieval relevance** ([Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)). How well the results match query intent against a labeled dataset. Run weekly, or on retrieval-stack changes.
+- **Layer 2: Retrieval relevance** ([Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/)). How well the results match query intent against a labeled dataset. Run weekly, or on retrieval-stack changes.
 - **Layer 3: Pipeline output quality** ([Evaluating Pipeline Output Quality](/documentation/tutorials-search-engineering/retrieval-quality-pipeline-output/)). Whether the full pipeline (retrieval plus an LLM generator, a ranker, or a UI) produces the right output. Run weekly, or on retrieval or generator changes.
 - **Layer 4: Business impact**. Whether better retrieval moves the KPIs the business cares about. Measured per release once the offline layers pass.
 
@@ -93,4 +93,4 @@ Wire it into CI and fail the job when precision falls below your target threshol
 
 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.
 
-Once ANN precision is on target, the next layer is whether the retrieved results are relevant to users. See [Building a Golden Query Set](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/).
+Once ANN precision is on target, the next layer is whether the retrieved results are relevant to users. See [Measuring Retrieval Relevance](/documentation/tutorials-search-engineering/retrieval-quality-golden-set/).