Prompt Engineering for Beginners - Tutorial 1 - Introduction to OpenAI API

The video begins with Ian introducing a tutorial on using the OpenAI API with Python. The focus is on interacting with the chat completions API, which allows conversations with AI models like GPT-3.5 Turbo or GPT-4. Ian explains that using Python provides more granular control over these interactions compared to using a GUI. The tutorial aims to be useful for those interested in building chatbots, image generators, recommendation engines, and more. The video is part of a series that will delve into various APIs, including those from Anthropic and the Prompt Layer APIs. Ian encourages viewers to check out AI projects on GitHub for inspiration and provides a link to the code repository in the video description.

Ian demonstrates how to set up the OpenAI API key by exporting it as an environment variable. He explains the need for the 'openai' Python module to interact with the API. The program's goal is to send a request to the chat completion API, creating a chat completion object. This object represents the conversation history and the response to a query, similar to interacting with a chatbot. Ian details the program's boilerplate code, explaining the need for the 'os' module to handle the API key and the 'openai' module for API interactions. He also discusses the program's functionality, which includes specifying the AI model, managing conversation history, setting the conversation tone, and determining the variability and creativity of the AI's responses.

Ian delves into the code, explaining the parameters used in the chat completion API request. These include the model ID, a list of messages for context, a temperature setting to control randomness and creativity, and a maximum token limit to manage costs and output length. He provides a docstring that outlines the code's functionality and the significance of each parameter. Ian also discusses the concept of tokens, which measure the input and output text's length, and how they relate to API costs. The video includes an example response from the API, detailing the structure of the response data and the information it provides, such as the unique ID, object type, timestamp, model used, and the actual response content.

Ian explains the role of the messages list in maintaining conversation context, which is crucial for the AI to provide accurate follow-up responses. He discusses the three roles in the conversation: system, user, and assistant. The system role sets the tone for the AI, the user role represents the user's input, and the assistant role provides the AI's responses. Ian emphasizes the importance of the initial system message in defining the AI's behavior. He also shows how to append the AI's response to the messages list for subsequent requests, allowing for a continuous conversation history. The video includes a demonstration of running the Python script and the response received from the API.

Towards the end of the tutorial, Ian provides additional resources for further learning, including the OpenAI API documentation, a playground for experimenting with the API, and a tokenizer page for understanding tokenization. He encourages viewers to explore these resources to gain a deeper understanding of the API's capabilities and to experiment with different parameters and models. Ian also mentions future videos that will cover more advanced topics related to the API.

Ian concludes the video by thanking viewers for watching and inviting them to ask questions in the comments. He reminds viewers to check the description for resources and links to the code repository. Ian also teases the next video in the series, promising more in-depth exploration of the OpenAI API and related technologies.