diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/_index.md b/qdrant-landing/content/course/multi-vector-search/module-3/_index.md
index d4a5e3de7..95a889bd0 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/_index.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/_index.md
@@ -20,5 +20,6 @@ Tackle the memory and performance challenges of production-scale multi-vector se
 3. Pooling Techniques
 4. MUVERA
 5. Evaluating Search Pipelines
+6. Final Project
 
-You'll master the optimization strategies needed to deploy multi-vector search at scale. Eventually, you will utilize all the learned skills to build a real-world project implementing multi-vector search for multi-modal data.
+You'll master the optimization strategies needed to deploy multi-vector search at scale. The module concludes with a final project where you'll apply all the learned skills to build a real-world multi-vector search system for multi-modal data.
diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md b/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
index 464c7cafc..ecdd25af2 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/evaluating-pipelines.md
@@ -1,7 +1,7 @@
 ---
 title: "Evaluating Search Pipelines"
 description: Learn how to evaluate different search configurations in terms of cost, latency, and retrieval quality using ground truth datasets and standardized metrics.
-weight: 6
+weight: 5
 ---
 
 {{< date >}} Module 3 {{< /date >}}
diff --git a/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md b/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
index 3d4a1ece0..f1ac1eadc 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-3/pooling-techniques.md
@@ -164,7 +164,7 @@ from scipy.cluster.vq import kmeans2
 
 # Embed a document image
 image_path = "images/financial-report.png"
-embeddings = list(model.embed_images([image_path]))[0]
+embeddings = list(model.embed_image([image_path]))[0]
 
 def hierarchical_pool(embeddings: np.ndarray, k: int) -> np.ndarray:
     """Pool embeddings using k-means clustering."""
@@ -187,7 +187,7 @@ for k in [16, 32, 64, 128]:
 
 ## What's Next
 
-You've learned two complementary strategies for reducing the number of vectors per document:
+This lesson covered two complementary strategies for reducing the number of vectors per document:
 
 - **Row/column pooling**: Exploits spatial structure in image embeddings for a fixed reduction (32x for ColPali)
 - **Hierarchical pooling**: Content-adaptive clustering that works for any multi-vector representation