azure openai completions playground for gpt-4o: Explore, Create, and Deploy AI Models

The Azure OpenAI Completions Playground for GPT-4 is a powerful tool for exploring, creating, and deploying AI models. It's available on the Azure OpenAI portal, where you can start using it right away.

With the Completions Playground, you can interact with GPT-4 models in a variety of ways, including text input, API calls, and even a visual interface. This makes it easy to experiment with different prompts and see how the models respond.

You can also use the Completions Playground to create and deploy your own custom AI models, which can be integrated into your applications and services. This is a great way to take advantage of the latest advancements in AI technology and enhance your products and services.

The Completions Playground is a great resource for anyone looking to get started with AI development, from beginners to experienced developers.

GPT-4 is a large language model that can process human-like language and respond accordingly.

It was developed by OpenAI and has a significantly larger model size than its predecessor, GPT-3.5.

The GPT-4 model has a 175 billion parameter count, which is a substantial increase from the 1.5 billion parameter count of GPT-3.5.

This increase in model size allows GPT-4 to better understand and generate human-like language.

GPT-4 has a more advanced architecture than GPT-3.5, with a more efficient and scalable design.

It is capable of handling a wide range of tasks, including text classification, question answering, and language translation.

GPT-4 can also generate text that is more coherent and contextually relevant than GPT-3.5.

The GPT-4 model has been fine-tuned on a large dataset of text from the internet, which enables it to learn from a vast amount of information.

This fine-tuning process allows GPT-4 to generate text that is more accurate and informative than GPT-3.5.

GPT-4 is also capable of handling multiple languages, including English, Spanish, French, and many others.

It can generate text in these languages with a high degree of accuracy and fluency.

The GPT-4 model is designed to be highly scalable and can be easily deployed in a variety of applications.

It has a number of use cases, including chatbots, virtual assistants, and language translation tools.

To get a chat completion from the GPT model in Azure OpenAI, you need to use an HTTP action to call the chat completions endpoint.

The URI for the Azure OpenAI chat completions endpoint looks like this: your specific endpoint will have a different resource name, deployment name, and API version. You can find your own URI by opening Chat Playground and selecting View Code.

To make the HTTP request, include the following header values: Content-Type should be application/json, and api-key is found in the footer of the sample code menu.

The Chat Playground will break each newline into a separate message, so if you're sending multiple messages, they'll be treated as one.

The Azure OpenAI Completions Playground for GPT-4 offers leading performance and flexibility.

With the ability to generate human-like text, the GPT-4 model can complete a wide range of tasks, from answering questions to generating creative writing.

This flexibility is made possible by the model's vast knowledge base and ability to understand context.

The Azure OpenAI Completions Playground allows users to experiment with GPT-4's capabilities in a user-friendly interface.

You can fine-tune the model to suit your specific needs, making it an invaluable tool for developers and businesses alike.

The model's performance is also impressive, with the ability to process and respond to large amounts of data in a short amount of time.

This makes it an ideal solution for applications that require rapid processing and response, such as chatbots and virtual assistants.

To begin with Azure OpenAI Completions Playground for GPT-4, you need to sign up for an Azure account if you don't already have one.

You can create a free account, which is a great way to start exploring the features of GPT-4 without any upfront costs.

The Playground is designed to be user-friendly, so you can quickly get started without needing extensive technical knowledge.

Getting started with Azure is a great place to begin your AI journey. The future of AI starts here, and it's a powerful platform to envision your next great AI app.

Azure offers the latest technologies to build and deploy AI solutions. You can get started with Azure to explore its capabilities.

The Azure AI solutions are designed to help you build and deploy AI apps efficiently. This means you can focus on developing your app without worrying about the underlying infrastructure.

To get started, you can explore the Azure AI solutions and envision your next great AI app.

To create an Azure OpenAI resource, you need to open the Azure Portal and go to the Azure OpenAI service. This is where you'll host your GPT model.

Fill in the project details, including subscription, resource group, region, name, and pricing tier. Then press next to proceed.

You can choose from different pricing tiers, but the specifics of each tier aren't mentioned in this article. Be sure to review the pricing options carefully before making a decision.

Create a new Azure OpenAI resource by following the prompts in the Azure Portal. This will set up the foundation for your GPT model deployment.

To deploy a GPT model in Azure OpenAI Studio, you need to go to the deployments tab and deploy a base model. This is the first step in making your GPT model available for use in Power Automate.

You can choose from a list of available GPT models, and for this tutorial, we're using GPT-4o-mini. Keep in mind that each model has different abilities and costs, so select the one that best fits your needs.

To deploy the model, use the default deployment name and model version, and set the deployment type to global standard. This is the recommended setting for most users.

Increasing the Tokens per Minute Rate Limit to the maximum allowed value is also a good idea, as it will give you more flexibility and freedom to use your GPT model.

To integrate Azure OpenAI with other tools, you can create a Power Automate flow. This flow can be triggered manually, allowing you to test and refine it before making it automatic.

You'll need to add a Data Operations – Compose action to the flow, which will hold the system message that was used in Chat Playground. This message will serve as the input for the Azure OpenAI call.

Next, add a second Compose action to format the helpdesk ticket as an email, making it easier to follow and understand. In a real-world scenario, you might use an Office 365 Outlook – When A New Email Arrives trigger to collect values from the email message.

By automating the process of sending helpdesk tickets to Azure OpenAI, you can streamline your workflow and get more accurate priority levels for your tickets. This can save you time and help you respond to customer inquiries more efficiently.

Using Power Automate with GPT & AI Prompts is an affordable option, with a cost of USD$ 0.05 per million input tokens.

You can break down user content into smaller segments, like I did in my case, to make the most of this feature.

The Chat Playground will treat each newline as a separate message, so be mindful of how you format your input.

It's possible to submit multiple messages through the chat window, and the Chat Playground will wait to respond to them as one.

I found the instructions for using the Chat Playground to be excellent, and I appreciate the clarity they provided.

You can use Azure OpenAI On Your Data securely by protecting data and resources with Microsoft Entra ID role-based access control, virtual networks, and private endpoints. This helps safeguard sensitive information and ensures only authorized users have access.

To fully utilize Azure OpenAI On Your Data, you need to set one or more Azure RBAC roles. This is a crucial step in managing access and permissions for your data sources.

Restricting documents that can be used in responses for different users can be done using Azure AI Search security filters. This feature allows you to tailor the information provided to each user's needs and permissions.

Safety by default is now available for GPT-4o mini on Azure OpenAI Service.

This means that Azure AI Content Safety features, including prompt shields and protected material detection, are automatically enabled for you to use with GPT-4o mini.

Our investment in improving the throughput and speed of Azure AI Content Safety capabilities has paid off, allowing you to maximize the advancements in model speed while maintaining safety.

An asynchronous filter has been introduced to further enhance the speed and efficiency of the content safety features.

Azure AI Content Safety is already supporting developers across industries, including game development, tax filing, and education.

This ensures that your generative AI applications are safeguarded against potential risks.

Microsoft's Customer Copyright Commitment will also apply to GPT-4o mini, giving you peace of mind that your output content is protected against third-party intellectual property claims.

API Management is a crucial aspect of modern software development, and it's essential to understand its role in management and security.

APIs are the backbone of modern software development, and they can be vulnerable to security threats if not managed properly.

API gateways can help protect APIs from attacks by acting as a single entry point for API traffic, making it easier to implement security measures such as authentication and rate limiting.

API security is a top priority, and many companies are turning to API gateways to help protect their APIs from unauthorized access and data breaches.

API gateways can also help with scalability and performance by acting as a buffer between the API and the underlying infrastructure, allowing for more efficient use of resources.

By implementing API management best practices, developers can ensure that their APIs are secure, scalable, and performant, which is essential for delivering high-quality software products.

To use Azure OpenAI On Your Data securely, you can rely on Microsoft Entra ID role-based access control to protect your data and resources. This feature ensures that only authorized users can access your data.

You can also set up virtual networks and private endpoints to further secure your data. This is especially important when working with sensitive information.

Azure AI Search security filters allow you to restrict the documents that can be used in responses for different users. This adds an extra layer of protection to your data.

By implementing these security measures, you can have peace of mind knowing that your data is protected.

To develop and use Azure OpenAI On Your Data, you'll need to connect your data source using Azure OpenAI Studio. This is where the magic happens, and you can start asking questions and chatting on your data.

The development process typically involves three steps: Ingest, Develop, and Inference. Ingest is where you upload your files using Azure OpenAI Studio or the ingestion API, which enables your data to be cracked, chunked, and embedded into an Azure AI Search instance.

During the Develop stage, you'll begin creating your application using the available REST API and SDKs, which are available in several languages. This is where you'll create prompts and search intents to pass to the Azure OpenAI service.

To get started with Inference, your application will send prompts to Azure OpenAI, which will perform several steps before returning a response. This is where the AI model kicks in and starts generating answers based on your data.

Here's a quick rundown of the development process:

Azure OpenAI On Your Data provides several search types to help you find relevant data based on user queries. You can use these search types when you add your data source to enhance your chat solution.