Merge pull request #270718 from wmwxwa/patch-8

Move up related concepts pt 3.md

Author: prmerger-automator[bot]

articles/cosmos-db/vector-database.md

@@ -78,7 +78,22 @@ The process of creating good prompts for a scenario is called prompt engineering
 
 Tokens are small chunks of text generated by splitting the input text into smaller segments. These segments can either be words or groups of characters, varying in length from a single character to an entire word. For instance, the word hamburger would be divided into tokens such as ham, bur, and ger while a short and common word like pear would be considered a single token. LLMs like ChatGPT, GPT-3.5, or GPT-4 break words into tokens for processing.
 
-Here are multiple ways to implement RAG on your data by using our vector database functionalities:
+### Retrieval-augmented generation
+
+Retrieval-augmentated generation (RAG) is an architecture that augments the capabilities of LLMs like ChatGPT, GPT-3.5, or GPT-4 by adding an information retrieval system like vector search that provides grounding data, such as those stored in a vector database. This approach allows your LLM to generate contextually relevant and accurate responses based on your custom data sourced from vectorized documents, images, audio, video, etc.
+
+A simple RAG pattern using Azure Cosmos DB for NoSQL could be:
+
+1.	Insert data into an Azure Cosmos DB for NoSQL database and collection
+2.	Create embeddings from a data property using Azure OpenAI Embeddings
+3.	Link the Azure Cosmos DB for NoSQL to Azure Cognitive Search (for vector indexing/search)
+4.	Create a vector index over the embeddings properties
+5.	Create a function to perform vector similarity search based on a user prompt
+6.	Perform question answering over the data using an Azure OpenAI Completions model
+
+The RAG pattern, with prompt engineering, serves the purpose of enhancing response quality by offering more contextual information to the model. RAG enables the model to apply a broader knowledge base by incorporating relevant external sources into the generation process, resulting in more comprehensive and informed responses. For more information on "grounding" LLMs, see [grounding LLMs](https://techcommunity.microsoft.com/t5/fasttrack-for-azure/grounding-llms/ba-p/3843857).
+
+Here are multiple ways to implement RAG on your data by using our integrated vector database functionalities:
 
 ## How to implement integrated vector database functionalities
 
@@ -125,21 +140,6 @@ The natively integrated vector database in our NoSQL API will become available i
 > [!div class="nextstepaction"]
 > [Use the Azure Cosmos DB lifetime free tier](free-tier.md)
 
-### Retrieval-augmented generation
-
-Retrieval-augmentated generation (RAG) is an architecture that augments the capabilities of LLMs like ChatGPT, GPT-3.5, or GPT-4 by adding an information retrieval system like vector search that provides grounding data, such as those stored in a vector database. This approach allows your LLM to generate contextually relevant and accurate responses based on your custom data sourced from vectorized documents, images, audio, video, etc.
-
-A simple RAG pattern using Azure Cosmos DB for NoSQL could be:
-
-1.	Insert data into an Azure Cosmos DB for NoSQL database and collection
-2.	Create embeddings from a data property using Azure OpenAI Embeddings
-3.	Link the Azure Cosmos DB for NoSQL to Azure Cognitive Search (for vector indexing/search)
-4.	Create a vector index over the embeddings properties
-5.	Create a function to perform vector similarity search based on a user prompt
-6.	Perform question answering over the data using an Azure OpenAI Completions model
-
-The RAG pattern, with prompt engineering, serves the purpose of enhancing response quality by offering more contextual information to the model. RAG enables the model to apply a broader knowledge base by incorporating relevant external sources into the generation process, resulting in more comprehensive and informed responses. For more information on "grounding" LLMs, see [grounding LLMs](https://techcommunity.microsoft.com/t5/fasttrack-for-azure/grounding-llms/ba-p/3843857).
-
 ## Related content
 
 - [Azure Cosmos DB for MongoDB vCore Integrated Vector Database](mongodb/vcore/vector-search.md)