What is Azure OpenAI service?
The Azure OpenAI service provides REST API access to OpenAI’s powerful language models including the GPT-3, Codex and Embeddings model series.
How does OpenAI works?
OpenAI states that their ChatGPT model, trained using a machine learning technique called Reinforcement Learning from Human Feedback (RLHF), can simulate dialogue, answer follow-up questions, admit mistakes, challenge incorrect premises and reject inappropriate requests.
What framework does OpenAI use?
Emplifi Launches AI Composer Using OpenAI’s GPT-3 Framework to Generate Ready-to-Publish Social Media Copy for Businesses. Brands increase operational efficiency, productivity and workforce optimization by leveraging OpenAI’s GPT-3 language and learning models for automatic social media content generation.
Key concepts
Prompts & Completions
The completions endpoint is the core component of the API service. This API provides access to the model’s text-in, text-out interface. Users simply need to provide an input prompt containing the English text command, and the model will generate a text completion.
Here’s an example of a simple prompt and completion:
Prompt:
""" count to 5 in a for loop """Completion:
for i in range(1, 6): print(i)
Tokens
OpenAI Enterprise processes text by breaking it down into tokens. Tokens can be words or just chunks of characters. For example, the word “hamburger” gets broken up into the tokens “ham”, “bur” and “ger”, while a short and common word like “pear” is a single token. Many tokens start with a whitespace, for example “ hello” and “ bye”.
The total number of tokens processed in a given request depends on the length of your input, output and request parameters. The quantity of tokens being processed will also affect your response latency and throughput for the models.
Resources
The Azure OpenAI service is a new product offering on Azure. You can get started with the Azure OpenAI service the same way as any other Azure product where you create a resource, or instance of the service, in your Azure Subscription. You can read more about Azure’s resource management design.
Deployments
Once you create an Azure OpenAI Resource, you must deploy a model before you can start making API calls and generating text. This action can be done using the Deployment APIs. These APIs allow you to specify the model you wish to use.
In-context learning
The models used by the Azure OpenAI service use natural language instructions and examples provided during the generation call to identify the task being asked and skill required. When you use this approach, the first part of the prompt includes natural language instructions and/or examples of the specific task desired. The model then completes the task by predicting the most probable next piece of text. This technique is known as “in-context” learning. These models aren’t retrained during this step but instead give predictions based on the context you include in the prompt.
There are three main approaches for in-context learning: Few-shot, one-shot and zero-shot. These approaches vary based on the amount of task-specific data that is given to the model:
Few-shot: In this case, a user includes several examples in the call prompt that demonstrate the expected answer format and content. The following example shows a few-shot prompt where we provide multiple examples (the model will generate the last answer):
Convert the questions to a command:
Q: Ask Constance if we need some bread.
A: send-msg `find constance` Do we need some bread?
Q: Send a message to Greg to figure out if things are ready for Wednesday.
A: send-msg `find greg` Is everything ready for Wednesday?
Q: Ask Ilya if we're still having our meeting this evening.
A: send-msg `find ilya` Are we still having a meeting this evening?
Q: Contact the ski store and figure out if I can get my skis fixed before I leave on Thursday.
A: send-msg `find ski store` Would it be possible to get my skis fixed before I leave on Thursday?
Q: Thank Nicolas for lunch.
A: send-msg `find nicolas` Thank you for lunch!
Q: Tell Constance that I won't be home before 19:30 tonight — unmovable meeting.
A: send-msg `find constance` I won't be home before 19:30 tonight. I have a meeting I can't move.
Q: Tell John that I need to book an appointment at 10:30.
A:The number of examples typically range from 0 to 100 depending on how many can fit in the maximum input length for a single prompt. Maximum input length can vary depending on the specific models you use. Few-shot learning enables a major reduction in the amount of task-specific data required for accurate predictions. This approach will typically perform less accurately than a fine-tuned model.
One-shot: This case is the same as the few-shot approach except only one example is provided.
Zero-shot: In this case, no examples are provided to the model and only the task request is provided.
Models
The service provides users access to several different models. Each model provides a different capability and price point. The GPT-3 base models are known as Davinci, Curie, Babbage, and Ada in decreasing order of capability and increasing order of speed.
The Codex series of models is a descendant of GPT-3 and has been trained on both natural language and code to power natural language to code use cases. Learn more about each model on our models concept page.
