Updates to content to ensure correct AI messaging is clear.

Author: codingbandit

00_Introduction/README.md

@@ -12,6 +12,10 @@
 - [Bicep CLI](https://learn.microsoft.com/azure/azure-resource-manager/bicep/install#install-manually)
 - [Powershell](https://learn.microsoft.com/powershell/scripting/install/installing-powershell?view=powershell-7.3)
 
+## Why use this guide?
+
+The future of software involves combining AI and data services, also known as intelligent applications. This guide is for MongoDB developers looking to implement intelligent applications quickly while leveraging existing skills. The content will focus on the developer journey implementing an Azure-based AI-enabled GPT-based chat application that is augmented using data stored in vCore for Azure Cosmos DB for MongoDB while leveraging Azure OpenAI services.
+
 ## Introduction
 
-This guide will walk you through the creating intelligent solutions that combine vCore for Azure Cosmos DB for MongoDB vector search with Azure OpenAI services to build a chat bot experience. The guide includes labs that build and deploy a sample chat app using these technologies, with a focus on vCore for Azure Cosmos DB for MongoDB, Vector Search, and Azure OpenAI using the Python programming language. For those new to using Azure OpenAI and Vector Search technologies, the guide includes explanations of the core concepts and techniques used when implementing these technologies.
+This guide will walk you through the creating intelligent solutions that combines vCore-based Azure Cosmos DB for MongoDB vector search and document retrieval with Azure OpenAI services to build a chat bot experience. The guide includes labs that build and deploy a sample chat app using these technologies, with a focus on vCore-based Azure Cosmos DB for MongoDB, Vector Search, and Azure OpenAI using the Python programming language. For those new to using Azure OpenAI and Vector Search technologies, the guide includes explanations of the core concepts and techniques used when implementing these technologies.

01_Azure_Overview/README.md

@@ -1,6 +1,6 @@
 # Azure Overview
 
-Millions of customers worldwide trust the Azure platform, and there are over 90,000 Cloud Solution Providers (CSPs) partnered with Microsoft to add extra benefits and services to the Azure platform. By leveraging Azure, organizations can easily modernize their applications, expedite application development, and adapt application requirements to meet the demands of their users.
+Millions of customers worldwide trust the Azure platform, and there are over 90,000 Cloud Solution Providers (CSPs) partnered with Microsoft to add extra benefits and services to the Azure platform. By leveraging Azure, organizations can easily modernize their applications, expedite application development, and adapt application requirements to meet the demands of their users. This section provides an overview of Azure, its services, and recommendations on how to get started.
 
 ## Advantages of choosing Azure
 

02_Overview_Cosmos_DB/README.md

@@ -2,15 +2,19 @@
 
 [Azure Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/introduction) is a globally distributed, multi-model database service that enables you to query and store data using NoSQL models using one of five APIs: SQL (document database), Cassandra (column-family), MongoDB (document database), Azure Table, and Gremlin (graph database). It provides turnkey global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, and guaranteed high availability with multi-homing capabilities. Azure Cosmos DB provides comprehensive service level agreements (SLAs) for throughput, latency, availability, and consistency guarantees, something not found in any other database service.
 
+## Azure Cosmos DB and AI
+
+The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance. Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data. Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models, making it an ideal platform for AI applications. The wide array of data model support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
+
 ## Azure Cosmos DB for Mongo DB
 
-[Azure Cosmos DB for MongoDB](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/introduction) simplifies utilizing Azure Cosmos DB as a MongoDB database. You can leverage your current MongoDB expertise and still use your preferred MongoDB drivers, SDKs, and tools simply by directing your application to the connection string for your account.
+The focus for this developer guide is [Azure Cosmos DB for MongoDB](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/introduction). Developers can leverage their current MongoDB expertise and use their preferred MongoDB drivers, SDKs, and tools simply by directing applications to the connection string for on the Azure Cosmos DB for MongoDB account.
 
 ### Azure Cosmos DB for Mongo DB API Architectures
 
 The [RU architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/ru/introduction) for Azure Cosmos DB for MongoDB offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, unlimited scalability, real-time analytics, and serverless deployments where you pay only per operation.
 
-Azure Cosmos DB for MongoDB [vCore architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/introduction) integrates AI-based applications with your data, with text indexing for easy querying. Simplify your development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
+[vCore-based Azure Cosmos DB for MongoDB architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/introduction) integrates AI-based applications with your data, with text indexing for easy querying. Simplify your development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
 
 The [choice between vCore and Request Units (RU)](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/choose-model) in Azure Cosmos DB for MongoDB API depends on the workload. A list of [compatibility and feature support between RU and vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/compatibility) is available.
 

06_Provision_Azure_Resources/README.md

@@ -5,12 +5,12 @@
 Bicep deployment is available in the [`deploy` folder of the lab repository](https://github.com/solliancenet/cosmos-db-openai-python-dev-guide-labs/tree/main/deploy).
 Currently deploying the following:
 
-  - Resource Group (this is done manually in the Azure Portal)
-  - Azure vCore-based Azure Cosmos DB for MongoDB account
-  - Azure OpenAI resource
-    - Chat GPT-3.5 `completions` model
-    - text-embedding-ada-002 model `embeddings` model
-  - Azure Container Registry to host Docker images
-  - Azure Container Apps Environment to host Docker containers
-  - Azure Container App to run Docker containers (initially with hello-world, but updated during the Backend API lab to run the backend Python API)
-  - Azure App Service to host Front-End SPA written in React
+- Resource Group (this is done manually in the Azure Portal)
+- Azure vCore-based Azure Cosmos DB for MongoDB account
+- Azure OpenAI resource
+  - Chat GPT-3.5 `completions` model
+  - text-embedding-ada-002 model `embeddings` model
+- Azure Container Registry to host Docker images
+- Azure Container Apps Environment to host Docker containers
+- Azure Container App to run Docker containers (initially with hello-world, but updated during the Backend API lab to run the backend Python API)
+- Azure App Service to host Front-End SPA written in React

09_Vector_Search_Cosmos_DB/README.md

@@ -24,7 +24,7 @@ In this example, assume textual data is vectorized and stored within an vCore-ba
 
 It is common practice to store vectorized data in a dedicated vector store as vector search indexing is not a common capability of most databases. However, this introduces additional complexity to the solution as the data must be stored in two different locations. vCore-based Azure Cosmos DB for MongoDB supports vector search indexing, which means that the vectorized data can be stored in the same document as the original data. This reduces the complexity of the solution and allows for a single database to be used for both the vector store and the original data.
 
-## Lab 3 - Use vector search on embeddings in vCore-based Azure Cosmos DB for MongoDBvCore
+## Lab 3 - Use vector search on embeddings in vCore-based Azure Cosmos DB for MongoDB
 
 In this lab, a notebook demonstrates how to add an embedding field to a document, create a vector search index, and perform a vector search query. The notebook ends with a demonstration of utilizing vector search with an LLM in a RAG scenario.