Merge pull request #280004 from wmwxwa/patch-23

Update references to vector search algos

Author: prmerger-automator[bot]

articles/cosmos-db/gen-ai/knn-vs-ann.md

@@ -10,7 +10,7 @@ ms.date: 07/01/2024
 
 # kNN vs ANN
 
-Two popular vector search algorithms are k-Nearest Neighbors (kNN) and Approximate Nearest Neighbors (ANN, not to be confused with Artificial Neural Network). kNN is precise but computationally intensive, making it less suitable for large datasets. ANN, on the other hand, offers a balance between accuracy and efficiency, making it better suited for large-scale applications.
+Two major categories of vector search algorithms are k-Nearest Neighbors (kNN) and Approximate Nearest Neighbors (ANN, not to be confused with Artificial Neural Network). kNN is precise but computationally intensive, making it less suitable for large datasets. ANN, on the other hand, offers a balance between accuracy and efficiency, making it better suited for large-scale applications.
 
 ## How kNN works
 

articles/cosmos-db/gen-ai/vector-search-overview.md

@@ -14,7 +14,7 @@ Vector search is a method that helps you find similar items based on their data
 
 This [interactive visualization](https://openai.com/index/introducing-text-and-code-embeddings/#_1Vr7cWWEATucFxVXbW465e) shows some examples of closeness and distance between vectors.
 
-Two popular types of vector search algorithms are [k-nearest neighbors (kNN) and approximate nearest neighbor (ANN)](knn-vs-ann.md). Some well-known vector search algorithms belonging to these categories include Inverted File (IVF), Hierarchical Navigable Small World (HNSW), and the state-of-the-art DiskANN.
+Two major types of vector search algorithms are k-nearest neighbors (kNN) and approximate nearest neighbor (ANN). Between [kNN and ANN](knn-vs-ann.md), the latter offers a balance between accuracy and efficiency, making it better suited for large-scale applications. Some well-known ANN algorithms include Inverted File (IVF), Hierarchical Navigable Small World (HNSW), and the state-of-the-art DiskANN.
 
 Using an integrated vector search feature in a fully featured database ([as opposed to a pure vector database](../vector-database.md#integrated-vector-database-vs-pure-vector-database)) offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.