In this session, you learned about the intricacies of working with OpenAI models and leveraging cutting-edge language models for various NLP tasks through OpenAI APIs.
OpenAI APIs offer several advantages for developers and users seeking to integrate OpenAI into their applications. These APIs provide programmatic access to the GPT model, allowing for seamless integration and interaction with the AI system.
We started this session with a brief overview of the various models offered by ChatGPT, such as:
- GPT-4
- GPT-3.5-turbo
The models differ in their input token sizes, capabilities, the modality (language or image based models) as well as the pricing. Users can select the most suitable model for their specific use case, whether it be for creative writing, code generation, or other natural language processing tasks.
The models also differ in terms of their input token sizes, capabilities and pricing.
We then used the Chat Completions API for working with single-turn and multi-turn conversations. We looked at the ChatCompletion API and its integration with python. The Chat Completion API requires the system, user, and assistant messages to be given to the model. This enables users to establish contextual conversations with the model, leading to more coherent and relevant responses.
We also explored how providing few-shot examples to the prompt helped guide the output in the right direction. We performed basic prompt engineering and built a AI Math Tutor that guides learners to the answer with the help of few-shot prompting technique.
In the last segment, we looked at the costs associated with working with OpenAI API and the pricing policy. OpenAI offers a pay-as-you-go model to make API calls on-demand and pay for only what they consume. With the models being constantly updated, users can also optimize the cost for their specific use case to stay within their budget. We also looked at how to prevent common errors while dealing with OpenAI APIs.