diff --git a/notebooks/agents/mongodb_with_aws_bedrock_agent.ipynb b/notebooks/agents/mongodb_with_aws_bedrock_agent.ipynb
index a3b87e6a..bda3f0a5 100644
--- a/notebooks/agents/mongodb_with_aws_bedrock_agent.ipynb
+++ b/notebooks/agents/mongodb_with_aws_bedrock_agent.ipynb
@@ -14,7 +14,7 @@
       },
       "source": [
         "# MongoDB with Bedrock agent quick tutorial\n",
-        "MongoDB Atlas and Amazon Bedrock have joined forces to streamline the development of generative AI applications through their seamless integration. MongoDB Atlas, a robust cloud-based database service, now offers native support for Amazon Bedrock, AWS's managed service for generative AI. This integration leverages Atlas's vector search capabilities, enabling the effective utilization of enterprise data to augment the foundational models provided by Bedrock, such as Anthropic's Claude and Amazon's Titan. The combination ensures that the generative AI models have access to the most relevant and up-to-date data, significantly improving the accuracy and reliability of AI-driven applications​ with [MongoDB](https://www.mongodb.com/developer/products/atlas/rag-workflow-with-atlas-amazon-bedrock/)​.\n",
+        "MongoDB Atlas and Amazon Bedrock have joined forces to streamline the development of generative AI applications through their seamless integration. MongoDB Atlas, a robust cloud-based database service, now offers native support for Amazon Bedrock, AWS's managed service for generative AI. This integration leverages MongoDB vector search for Atlas's capabilities, enabling the effective utilization of enterprise data to augment the foundational models provided by Bedrock, such as Anthropic's Claude and Amazon's Titan. The combination ensures that the generative AI models have access to the most relevant and up-to-date data, significantly improving the accuracy and reliability of AI-driven applications​ with [MongoDB](https://www.mongodb.com/developer/products/atlas/rag-workflow-with-atlas-amazon-bedrock/)​.\n",
         "\n",
         "This integration simplifies the workflow for developers aiming to implement retrieval-augmented generation (RAG). RAG helps mitigate the issue of hallucinations in AI models by allowing them to fetch and utilize specific data from a predefined knowledge base, in this case, MongoDB Atlas Developers can easily set up this workflow by creating a vector search index in Atlas, which stores the vector embeddings and metadata of the text data. This setup not only enhances the performance and reliability of AI applications but also ensures data privacy and security through features like AWS PrivateLink​​.\n",
         "\n",
@@ -397,7 +397,7 @@
         "Here you go! You have a powerful bedrock agent with MongoDB Atlas.\n",
         "\n",
         "Conclusions\n",
-        "The integration of MongoDB Atlas with Amazon Bedrock represents a significant advancement in the development and deployment of generative AI applications. By leveraging Atlas's vector search capabilities and the powerful foundational models available through Bedrock, developers can create applications that are both highly accurate and deeply informed by enterprise data. This seamless integration facilitates the retrieval-augmented generation (RAG) workflow, enabling AI models to access and utilize the most relevant data, thereby reducing the likelihood of hallucinations and improving overall performance.\n",
+        "The integration of MongoDB Atlas with Amazon Bedrock represents a significant advancement in the development and deployment of generative AI applications. By leveraging MongoDB vector search capabilities and the powerful foundational models available through Bedrock, developers can create applications that are both highly accurate and deeply informed by enterprise data. This seamless integration facilitates the retrieval-augmented generation (RAG) workflow, enabling AI models to access and utilize the most relevant data, thereby reducing the likelihood of hallucinations and improving overall performance.\n",
         "\n",
         "The benefits of this integration extend beyond just technical enhancements. It also simplifies the generative AI stack, allowing companies to rapidly deploy scalable AI solutions with enhanced privacy and security features, such as those provided by AWS PrivateLink. This makes it an ideal solution for enterprises with stringent data security requirements. Overall, the combination of MongoDB Atlas and Amazon Bedrock provides a robust, efficient, and secure platform for building next-generation AI applications​ .\n"
       ]