Update AI-ad-gen.md

Author: khelanmodi

articles/cosmos-db/mongodb/vcore/AI-ad-gen.md

@@ -13,7 +13,7 @@ ms.date: 03/12/2024
 
 # AI-Enhanced Advertisement Generation using Azure Cosmos DB for MongoDB vCore
 ## Overview
-In this guide, we demonstrate how to create dynamic advertising content that resonates with your audience, using the power of AI. Utilizing Azure Cosmos DB for MongoDB vCore, we harness the [vector similarity search](./vector-search.md) functionality to semantically analyze and match inventory descriptions with advertisement topics. The process is made possible by generating vectors for inventory descriptions using OpenAI embeddings, which significantly enhance their semantic depth. These vectors are then stored and indexed within the Cosmos DB for MongoDB vCore resource. When generating content for advertisements, we vectorize the advertisement topic to find the best-matching inventory items. This is followed by a retrieval augmented generation (RAG) process, where the top matches are sent to OpenAI to craft a compelling advertisement. The entire codebase for the application is available in a [GitHub repository](https://aka.ms/adgen) for your reference.
+In this guide, we demonstrate how to create dynamic advertising content that resonates with your audience, using our personalized AI assistant, Heelie. Utilizing Azure Cosmos DB for MongoDB vCore, we harness the [vector similarity search](./vector-search.md) functionality to semantically analyze and match inventory descriptions with advertisement topics. The process is made possible by generating vectors for inventory descriptions using OpenAI embeddings, which significantly enhance their semantic depth. These vectors are then stored and indexed within the Cosmos DB for MongoDB vCore resource. When generating content for advertisements, we vectorize the advertisement topic to find the best-matching inventory items. This is followed by a retrieval augmented generation (RAG) process, where the top matches are sent to OpenAI to craft a compelling advertisement. The entire codebase for the application is available in a [GitHub repository](https://aka.ms/adgen) for your reference.
 
 ## Features
 - **Vector Similarity Search**: Uses Azure Cosmos DB for MongoDB vCore's powerful vector similarity search to improve semantic search capabilities, making it easier to find relevant inventory items based on the content of advertisements.
@@ -152,7 +152,7 @@ else:
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 ### 4.2 Create the vector index
-To perform efficient vector similarity searches within our collection, we need to create a vector index. Cosmos DB supports different types of vector indexes, and here we discuss two: IVF and HNSW.
+To perform efficient vector similarity searches within our collection, we need to create a vector index. Cosmos DB supports different types of [vector indexes](./vector-search.md), and here we discuss two: IVF and HNSW.
 
 ### IVF
 IVF stands for Inverted File Index, is the default vector indexing algorithm, which works on all cluster tiers. It's an approximate nearest neighbors (ANN) approach that uses clustering to speeding up the search for similar vectors in a dataset. To create an IVF index, use the following command: