Further Resources

1. Azure OpenAI Service Documentation

Description: Comprehensive official documentation covering setup, usage, pricing, and best practices for Azure OpenAI.
Link: Azure OpenAI Service Docs

2. Retrieval-Augmented Generation (RAG) with Azure AI Search

Description: Detailed guide on implementing RAG-based applications using Azure AI Search, including architecture and sample workflows.
Link: RAG with Azure AI Search

3. Azure OpenAI Service Samples and Quickstarts

Description: Repository of quickstart guides and sample applications demonstrating various features of Azure OpenAI, including RAG and document interactions.
Link: Azure OpenAI Samples

Additional Valuable Resources

Microsoft Azure Blog: AI and Machine Learning
Description: Stay updated with the latest announcements, use cases, and deep dives into Azure’s AI and machine learning offerings.
Link: Azure AI Blog

In today's era of Generative AI, customers can unlock valuable insights from their unstructured or structured data to drive business value. By infusing AI into their existing or new products, customers can create powerful applications, which puts the power of AI into the hands of their users. For these Generative AI applications to work on customers data, implementing efficient RAG (Retrieval augment generation) solution is key to make sure the right context of the data is provided to the LLM based on the user query.