Replace our with the

Author: ajosh0504

docs/40-prepare-the-data/1-load-data.mdx

@@ -1,5 +1,5 @@
 # 👐 Load the dataset
 
-First, let's download the dataset for our lab. We'll use a subset of our technical documentation as the source data for our documentation chatbot.
+First, let's download the dataset for the lab. We'll use a subset of MongoDB's technical documentation as the source data for the documentation chatbot.
 
 Run all the cells under the **Step 2: Load the dataset** section in the notebook to load the articles as a list of Python objects consisting of the content and relevant metadata.

docs/40-prepare-the-data/3-embed-data.mdx

@@ -1,6 +1,6 @@
 # 👐 Generate embeddings
 
-To perform vector search on our data, we need to embed it (i.e. generate embedding vectors) before ingesting it into MongoDB.
+To perform vector search on the data, we need to embed it (i.e. generate embedding vectors) before ingesting it into MongoDB.
 
 Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 4: Generate embeddings** section in the notebook to embed the chunked articles.
 

docs/40-prepare-the-data/4-ingest-data.mdx

@@ -2,7 +2,7 @@ import Screenshot from "@site/src/components/Screenshot";
 
 # 👐 Ingest data into MongoDB
 
-The final step to build a MongoDB vector store for our chatbot is to ingest the embedded article chunks into MongoDB.
+The final step to build a MongoDB vector store for the chatbot is to ingest the embedded article chunks into MongoDB.
 
 Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 5: Ingest data into MongoDB** section in the notebook to ingest the embedded documents into MongoDB.
 

docs/50-perform-semantic-search/3-vector-search.mdx

@@ -1,6 +1,6 @@
 # 👐 Perform semantic search
 
-Now let's run some vector search queries against our data present in MongoDB. 
+Now let's run some vector search queries against the data present in MongoDB. 
 
 Fill in any `<CODE_BLOCK_N>` placeholders and run the cells under the **Step 7: Perform semantic search on your data** section in the notebook to run vector search queries against your data.