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 bd4c6be23..5ac9faeda 100644
--- a/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md
+++ b/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md
@@ -28,21 +28,21 @@ Retrieval quality sits on top of embedding quality, measured separately by bench
 
 ## Measure ANN Recall with the Web UI
 
-Qdrant's Web UI includes a Search Quality 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 Search Quality tab, and click **Check Index Quality** to run the comparison.
+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.
 
-![Search Quality tab with default evaluation results](/documentation/tutorials/retrieval-quality/search-quality-tab.png)
+![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 Search Quality 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.
+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.
 
-![Search Quality advanced mode with HNSW parameters](/documentation/tutorials/retrieval-quality/search-quality-advanced.png)
+![ANN Recall advanced mode with HNSW parameters](/documentation/tutorials/retrieval-quality/search-quality-advanced.png)
 
 Recall should increase at the cost of higher query latency.
 
-![Search Quality results after HNSW tuning](/documentation/tutorials/retrieval-quality/search-quality-after-tuning.png)
+![ANN Recall results after HNSW tuning](/documentation/tutorials/retrieval-quality/search-quality-after-tuning.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.