Open AI offers a wide range of features that enable businesses to leverage the power of artificial intelligence. Open AI's features provide businesses with the ability to automate tasks, gain valuable insights from data, and create new and innovative products and services.
OpenAI's language models can be used to perform tasks such as language translation, language generation, and text summarization
OpenAI's language models can be used to answer questions by extracting information from a given text and providing a relevant response
OpenAI's language models can be used to classify text into different categories based on its content
OpenAI's language models can be used to determine the sentiment of a given piece of text (e.g. positive, negative, neutral)
OpenAI's language models can be used to build chatbots that can carry on natural conversations with humans
OpenAI's language models can be used to generate content such as articles, stories, or social media posts
OpenAI's language models can be used to extract insights and information from large amounts of text data
Davinci is the most powerful model and ada is the fastest
Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models
Good at: Complex intent, cause and effect, summarization for audience
Curie is quite capable for many nuanced tasks like sentiment classification and summarization. Curie is also quite good at answering questions and performing Q&A and as a general service chatbot.
Good at: Language translation, complex classification, text sentiment, summarization
Babbage can perform straightforward tasks like simple classification. It’s also quite capable when it comes to Semantic Search ranking how well documents match up with search queries.
Good at: Parsing text, simple classification, address correction, keywords
Ada is usually the fastest model and can perform tasks like parsing text, address correction and certain kinds of classification tasks that don’t require too much nuance. Ada’s performance can often be improved by providing more context
Good at: Moderate classification, semantic search classification
