Powering ChatGPT with your Data - A Deep Dive

🎯 Azure OpenAI allows users to leverage models like GPT 3.5 on their own data without coding or trading.

🔍 Fine-tuning and retrieval augmented generation (RAG) are two approaches to working with language models like ChatGPT.

💡 Fine-tuning involves retraining the model on new text data related to the desired task, while RAG retrieves relevant text data for each task.

🤔 The decision between fine-tuning and RAG depends on the amount of task-specific data available.

⏰ The frequency of model updates is a factor in choosing between fine-tuning and RAG.

💡 The need for accuracy and coherence in the model's output influences the choice between fine-tuning and RAG.

📚 The importance of citing sources affects the decision between fine-tuning and RAG.

🔍 Using Rag works well for most use cases, but fine-tuning may not always be necessary.

📚 The Azure OpenAI Service leverages a knowledge base and allows for data connection from various sources.

💻 The architecture includes a web app and a managed chat bot to handle queries and provide relevant results.

🔍 Azure OpenAI provides content filters to ensure safety and quality of outputs.

📊 Different quotas determine the usage limits for Azure OpenAI services.

💻 The chat playground in Azure OpenAI allows for deploying and leveraging the GPT-35 Turbo model with customizable instructions and data sources.

💡 Permissions are required to create resources like Azure cognitive search indexes and private endpoints are not supported in the preview.

🔍 Azure blob storage and Azure cognitive search are available options for storing and retrieving data, with the latter allowing for efficient data retrieval.

🔗 The video demonstrates how to connect Azure blob storage and Cosmos DB with Azure cognitive search, enabling the retrieval of indexed documents.

🔍 Azure OpenAI and Cognitive Search can be used together to retrieve and interact with data.

💬 Azure OpenAI and Cognitive Search provide the ability to generate responses in a chat bot-like manner.

🌐 The data can be deployed and used in a web app using Azure App Services.

🔍 The video discusses how to configure and customize Azure Cognitive Search for use in the ChatGPT application.

💻 It explains the connection between the application code running on App Services, the GitHub repository, and the Flask framework.

🤖 By leveraging the Azure Cognitive Search index created for Cosmos DB, users can interact with the ChatGPT application and retrieve specific information.