diff --git a/semantic-kernel/concepts/plugins/adding-mcp-plugins.md b/semantic-kernel/concepts/plugins/adding-mcp-plugins.md
index e6bac17a7..b9c37ddce 100644
--- a/semantic-kernel/concepts/plugins/adding-mcp-plugins.md
+++ b/semantic-kernel/concepts/plugins/adding-mcp-plugins.md
@@ -90,7 +90,7 @@ async def main():
         # Do something with the kernel
 ```
 
-In both case the async context manager is used to setup the connection and close it, you can also do this manually:
+In both cases the async context manager is used to setup the connection and close it, you can also do this manually:
 
 ```python
 import os
diff --git a/semantic-kernel/concepts/semantic-kernel-components.md b/semantic-kernel/concepts/semantic-kernel-components.md
index ca15661e3..6c848fc1f 100644
--- a/semantic-kernel/concepts/semantic-kernel-components.md
+++ b/semantic-kernel/concepts/semantic-kernel-components.md
@@ -30,7 +30,7 @@ The Kernel does not use any registered vector store automatically, but Vector Se
 in which case the plugin is made available to Prompt Templates and the Chat Completion AI Model.
 
 > [!TIP]
-> For more information on using memory connectors see [Adding AI services to Semantic Kernel](./ai-services/index.md).
+> For more information on using memory connectors see [Vector Store Connectors](./vector-store-connectors/index.md).
 
 ## Functions and Plugins
 
diff --git a/semantic-kernel/concepts/vector-store-connectors/index.md b/semantic-kernel/concepts/vector-store-connectors/index.md
index a62daaa20..06d55061e 100644
--- a/semantic-kernel/concepts/vector-store-connectors/index.md
+++ b/semantic-kernel/concepts/vector-store-connectors/index.md
@@ -35,7 +35,7 @@ One use case for storing information in a vector database is to enable large lan
 
 For example, if you want to write a blog post about the latest trends in AI, you can use a vector database to store the latest information about that topic and pass the information along with the ask to a LLM in order to generate a blog post that leverages the latest information.
 
-Semantic Kernel and .net provides an abstraction for interacting with Vector Stores and a list of out-of-the-box implementations that implement these abstractions for various databases. Features include creating, listing and deleting collections of records, and uploading, retrieving and deleting records. The abstraction makes it easy to experiment with a free or locally hosted Vector Store and then switch to a service when needing to scale up.
+Semantic Kernel provides an abstraction for interacting with Vector Stores and a list of out-of-the-box implementations that implement these abstractions for various databases. Features include creating, listing and deleting collections of records, and uploading, retrieving and deleting records. The abstraction makes it easy to experiment with a free or locally hosted Vector Store and then switch to a service when needing to scale up.
 
 The out-of-the-box implementations can be used with Semantic Kernel, but do not depend on the core Semantic Kernel stack and can also therefore be used completely independently if required.
 The Semantic Kernel provided imlementations are referred to as 'connectors'.