Smol updates

Author: ajosh0504

docs/20-dev-env/2-dev-env-setup.mdx

@@ -35,7 +35,7 @@ In the modal that appears, click **Python environments...** and select the inter
 That's it! You're ready for the lab!
 
 <details>
-  <summary>🦹 Use the MongoDB for VS Code extension to interact with the local MongoDB cluster from the VSCode IDE. </summary>
+  <summary>🦹 Use the MongoDB for VS Code extension to interact with the local MongoDB cluster from the VSCode IDE </summary>
 
     In the left navigation bar of the IDE, click on the leaf icon. Under Connections, click the **Add Connection** button.
 

docs/30-import-data/1-import-data.mdx

@@ -13,7 +13,7 @@ Run the cells under the **Step 2: Import data into MongoDB** section in the note
 
     <Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/1-verify-collection.png" alt="Verify collection" />
 
-    Click the **>** arrow next to _books_ and ensure that you see _50_ documents.
+    Click the **>** arrow next to _books_ and note the number of documents.
 
     <Screenshot url="https://github.com/codespaces" src="img/screenshots/30-import-data/1-import-data/2-document-count.png" alt="Verify document count" />
 

docs/40-perform-vector-search/3-create-vector-index.mdx

@@ -1,6 +1,6 @@
 # 👐 Create a vector search index
 
-To retrieve documents from MongoDB using vector search, you must configure a vector search index on the collection into which you ingested your data.
+To retrieve documents from MongoDB using vector search, you must configure a vector search index on the collection into which you ingested your data. In this lab, you will programmatically create vector search indexes using MongoDB's Python driver.
 
 Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 5: Create a vector search index** section in the notebook to create a vector search index on the `books` collection.