Rename retrieval-quality slugs to match tutorial scope

Tutorial slugs now match their content:
- retrieval-quality/ -> ann-recall/ (alias preserves the old published path)
- retrieval-quality-golden-set/ -> retrieval-relevance/
- retrieval-quality-pipeline-output/ -> pipeline-output-quality/

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

qdrant-landing/content/documentation/tutorials-search-engineering/ann-recall.md

@@ -0,0 +1,90 @@
+---
+title: Measuring ANN Recall
+aliases:
+  - /documentation/tutorials/retrieval-quality/
+  - /documentation/beginner-tutorials/retrieval-quality/
+  - /documentation/tutorials-search-engineering/retrieval-quality/
+weight: 5
+---
+
+# Measuring ANN Recall
+
+| Time: 15 min | Level: Beginner |  |    |
+|--------------|---------------------|--|----|
+
+This tutorial focuses on **ANN recall**: how closely approximate nearest-neighbor (ANN) search matches exact kNN search, measured with `recall@k`.
+
+**Prerequisites.** A Qdrant collection populated with your documents as points (vectors + optional payload).
+
+## The Retrieval Evaluation Stack
+
+ANN recall measures how closely approximate search matches exact kNN. It's the first of four evaluation layers; each higher layer measures a different property of the retrieval system, with different tools.
+
+- **ANN recall** (this tutorial). Is the approximate index close to exact kNN?
+- **Retrieval relevance** ([Measuring Retrieval Relevance](/documentation/improve-search/retrieval-relevance/)). Do the top-k results match query intent?
+- **Pipeline output quality** ([Evaluating Pipeline Output Quality](/documentation/improve-search/pipeline-output-quality/)). Does the end-to-end pipeline (retrieval + generator, ranker, or UI) produce the right output?
+- **Business impact**. Do the KPIs the business cares about move? Application-specific, out of scope for these tutorials.
+
+A high score on a higher layer requires acceptable scores on the layers below. Embedding quality (separately measured by benchmarks like [MTEB](https://huggingface.co/spaces/mteb/leaderboard)) sets the ceiling on every downstream metric.
+
+## Measure ANN Recall with the Web UI
+
+Qdrant's Web UI includes an ANN Recall tab that measures the gap between approximate and exact search without writing evaluation code. Open the dashboard at `http://localhost:6333/dashboard` (or your cluster's dashboard on Qdrant Cloud), navigate to your collection, open the ANN Recall tab, and click **Check Index Quality** to run the comparison.
+
+![ANN Recall tab with default evaluation results](/documentation/tutorials/retrieval-quality/search-quality-tab.png)
+
+The tab reports average **recall@k** (1.0 = perfect overlap; 0.95+ is typical for well-tuned HNSW).
+
+## Tuning Search Recall
+
+Toggle **advanced mode** in the ANN Recall tab to tune search-time parameters inline. The main one is `hnsw_ef`: the number of candidates evaluated during a search. Raising it explores more of the graph, improving recall at the cost of higher query latency. To see the effect, raise `hnsw_ef` (for example, to 256) and run the evaluation again.
+
+Recall should increase at the cost of higher query latency.
+
+![ANN Recall advanced mode with HNSW parameters](/documentation/tutorials/retrieval-quality/search-quality-advanced.png)
+
+If `hnsw_ef` alone does not get you to your recall target, the build-time parameters `m` and `ef_construct` set the ceiling on the recall approximate search can achieve. Changing them requires rebuilding the HNSW index. For the trade-offs and how to choose values, see [HNSW Indexing Fundamentals](/course/essentials/day-2/what-is-hnsw/) in the Qdrant Essentials course.
+
+## Automate in CI with Python
+
+The Web UI is the fastest way to check recall interactively. For continuous integration or scripted regression tests, the Qdrant client exposes the same exact-search mode via `search_params=models.SearchParams(exact=True)`. Compare the ANN and exact top-k sets yourself and compute recall@k.
+
+This helper takes a list of query vectors and returns the average recall@k. Use a representative sample of query vectors from your workload (typically 20–50, embedded with the same model your collection uses) as your test set.
+
+```python
+from qdrant_client import QdrantClient, models
+
+
+def avg_recall_at_k(
+    client: QdrantClient,
+    collection_name: str,
+    test_vectors: list,
+    k: int,
+) -> float:
+    recalls = []
+    for vector in test_vectors:
+        ann_ids = {
+            p.id for p in client.query_points(
+                collection_name=collection_name,
+                query=vector,
+                limit=k,
+            ).points
+        }
+        knn_ids = {
+            p.id for p in client.query_points(
+                collection_name=collection_name,
+                query=vector,
+                limit=k,
+                search_params=models.SearchParams(exact=True),
+            ).points
+        }
+        recalls.append(len(ann_ids & knn_ids) / k)
+
+    return sum(recalls) / len(recalls)
+```
+
+Wire it into CI and fail the job when recall falls below your target threshold. This catches regressions from embedding model swaps or index config changes before they reach production.
+
+## Next Steps
+
+Once ANN recall is on target, continue with [Measuring Retrieval Relevance](/documentation/improve-search/retrieval-relevance/) to check how well those results match user intent.