Prompt Engineering for Beginners - Tutorial 2 - Accepting User Input with Granny
TLDRIn this tutorial, Ian from the New Boston demonstrates how to use OpenAI's GPT-3.5 Turbo model to create dynamic conversations with AI. By accepting user input through the terminal, the program allows users to interact with the AI, which is programmed to respond in the voice of a 'sweet old helpful grandma'. The video covers setting up the OpenAI API, handling dynamic user input, and controlling the conversation's context. It also discusses the importance of managing token usage to ensure the AI's responses are both creative and within the desired length.
Takeaways
- 😀 This tutorial is part of an AI series by Ian from the New Boston, focusing on Prompt Engineering for Beginners.
- 💡 The video demonstrates how to use OpenAI's GPT-3.5 Turbo model to have a conversation with AI and control the conversation's context.
- 🔧 The tutorial builds upon the first video by introducing dynamic user input through the terminal, allowing users to drive the conversation.
- 👵 A unique aspect is instructing the AI to assume the role of a 'sweet old helpful grandma', personalizing the AI's responses.
- 💻 The script explains how to set up an environment variable for the OpenAI API key and use Python's input method to gather user input.
- 📝 The video covers the structure of a chat completion API request, including model selection, message roles, and parameters like temperature and max tokens.
- 🔖 The 'system' role in the API request is crucial as it sets the context for the AI's responses, in this case, as a grandmotherly figure.
- 🗨️ The tutorial shows how to handle and print the AI's response, navigating through the response object to extract the content.
- ⏱️ There's a discussion on the importance of managing token limits to ensure the AI's responses are complete and contextually appropriate.
- 🔄 The video concludes with a quick overview of the code and its functionality, encouraging viewers to experiment with the API for different outcomes.
Q & A
What is the main focus of the video tutorial?
-The main focus of the video tutorial is to demonstrate how to use the OpenAI chat completion API to interact with the GPT 3.5 Turbo model and control the conversation by accepting dynamic user input.
How does the video build upon the previous tutorial?
-The video builds upon the previous tutorial by moving from a hardcoded question to the model to accepting dynamic input from the user, allowing the user to drive the conversation.
What is the role assigned to the model in this tutorial?
-In this tutorial, the model is assigned the role of a 'sweet old helpful grandma' to give the responses a unique and friendly tone.
How does the user input get incorporated into the chat completion request?
-The user input is gathered through Python's built-in input method and then passed into the chat completion API request, which allows the user to ask questions and receive responses.
What is the purpose of setting the 'temperature' in the API request?
-The 'temperature' parameter in the API request determines the creativity and randomness of the model's responses. A lower temperature results in more deterministic responses, while a higher temperature allows for more creativity, albeit potentially more random.
What does the 'max_tokens' parameter control in the API request?
-The 'max_tokens' parameter controls the maximum number of tokens the model's response can use, not including the tokens used for the input.
How does the tutorial handle the scenario where the model's response is cut off due to token limits?
-The tutorial checks the 'finish_reason' in the response object to determine if the model's response was cut off due to reaching the token limit. If the finish reason is 'length', it indicates the response was cut off and may not be fully finished.
What is the significance of the 'system' role in the messages list?
-The 'system' role in the messages list is significant because it sets the context and instructions for how the model should respond in future interactions, in this case, as a 'sweet old helpful grandma'.
How does the tutorial ensure that the user is aware they are communicating with 'Granny'?
-The tutorial ensures the user is aware they are communicating with 'Granny' by setting the pretext in the user input prompt to 'What can Granny help you with today?'
What is the next step mentioned in the tutorial after setting up the dynamic user input?
-The next step mentioned in the tutorial is showing how to keep the conversation going with the model, allowing the user to have a full conversation with tracked history and context understanding.
Outlines
😀 Introduction to AI Series with OpenAI
Ian, the host, welcomes viewers to the second video of the AI series with the New Boston. He expresses excitement for the content, which involves using OpenAI's chat completion API to interact with the GPT 3.5 Turbo model. The video aims to build on the previous one by allowing dynamic user input through the terminal, enabling users to drive the conversation with the AI. Ian also plans to customize the AI's responses by setting a 'sweet old helpful grandma' persona, adding a unique flair to the interactions.
👩💻 Setting Up and Using OpenAI's API
The video demonstrates how to set up the OpenAI API by importing necessary libraries and retrieving the API key from an environment variable. It then guides viewers on how to gather dynamic user input using Python's input function, which is used to customize the conversation with the AI. The script explains the process of making a chat completion request to the API, detailing the parameters such as model, messages, temperature, and max tokens. The 'system' role is set to a 'sweet old helpful grandma' to influence the AI's responses, and the user's input is dynamically incorporated into the request.
📝 Exploring the Chat Completion Response
The video delves into the structure of the chat completion response from the OpenAI API. It explains the significance of the 'choices' attribute, which contains the AI's response. The response object is traversed to extract the content, which is then printed to the console. The video also discusses the 'temperature' parameter, which affects the creativity of the AI's responses, and the 'max tokens' parameter, which limits the length of the response. It highlights the importance of the 'finish reason' in determining whether the response is complete or cut off due to token limits.
📚 Conclusion and Future Exploration
In the concluding part, Ian summarizes the video's content, emphasizing the power of the code demonstrated and encouraging viewers to experiment with it. He hints at future videos where the conversation with the AI model will be expanded to include a full tracked history, allowing for more natural and contextually aware interactions. Ian invites feedback and questions from viewers and expresses gratitude for their engagement, promising more insights in upcoming videos.
Mindmap
Keywords
💡OpenAI
💡API
💡GPT-3.5 Turbo
💡Dynamic Input
💡Environment Variables
💡Python
💡Conversational AI
💡Max Tokens
💡Role
💡Temperature
Highlights
Introduction to Prompt Engineering for Beginners with Ian from the New Boston.
Building on the first video, using OpenAI's ChatGPT 3.5 Turbo model for conversational AI.
Controlling the conversation by setting the pretext for the AI model.
Accepting dynamic user input from the terminal to interact with the AI.
Using dynamic input to drive the conversation with the AI model.
Instructing the AI to role-play as a 'sweet old helpful grandma' for responses.
Setting up the OpenAI API key using environment variables.
Using Python's built-in input method to gather user queries.
Sending user queries to the Chat Completion API for responses.
Explaining the role of 'model', 'messages', 'temperature', and 'max_tokens' in the API request.
Demonstrating how to structure the 'messages' list for system, user, and assistant roles.
Using the 'user text' variable to personalize the AI's responses.
Adjusting the 'temperature' to control the creativity of the AI's responses.
Setting 'max_tokens' to limit the length of the AI's responses.
Printing the AI's responses to the console with formatted output.
Analyzing the response object to extract and display the AI's message content.
Discussing the 'usage' attribute and its significance in tracking token usage.
Exploring the impact of max_tokens on the completeness of open-ended responses.
Providing practical examples of interacting with the AI as 'Granny'.
Highlighting the importance of checking the 'finish_reason' for response completeness.
Encouraging viewers to experiment with the code and customize the AI's behavior.
Outlining future tutorials on maintaining conversation history with the AI.
Closing remarks and call for comments, feedback, and questions from viewers.
