Imagine you’re building a chatbot using OpenAI’s GPT-3.5-turbo, and you need it to extract specific information from users, asking one question at a time.
Seems simple, right?
But sometimes, crafting the perfect prompt to achieve this goal can be tricky.
In this case study, we’ll explore how one client tackled this challenge and achieved the desired outcome of asking one question at a time.
The Initial Attempt
Our client started with a prompt to guide the AI in gathering the necessary information from users. The prompt looked like this:
The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly. If the user wants they can talk about anything as soon as the assistant finishes collecting the following fields from the user: The user's name The user's departure location The user's destination location The user's dates of travel The user's budget for the trip Any additional preferences or constraints the user may have
However, the model sometimes responded with multiple questions at once, like this:
Sure, I can help you book a flight. Before we proceed, can you please provide me with the following information: - Your name? - Your departure location? - Your destination location? - Your dates of travel? - Your budget for the trip? - Any additional preferences or constraints you may have?
This response didn’t achieve the goal of asking one question at a time, so the client needed to find a different approach.
Trying the Stop Parameter
One suggestion was to set the
stop parameter to
?. This would make the model stop generating text when encountering a question mark. However, the client encountered some issues with this method, as the model’s output still lacked question marks and continued to ask multiple questions in a single response. For example:
Great! I'd be happy to help you with that. Before we get started, can you please provide me with the following information: - Your name - Your departure locatio - Your destination location - Your dates of travel - Your budget for the trip - Any additional preferences or constraints you may have Once I have all of this information, I can help you find the best flight options.
Reformulating the Prompt
The next problem occurred when the language model failed to ask questions. This committed to outputting any question mark, which prevented use of the
stop parameter. Instead, it simply listed the series of requirements to the user.
We decided to reformat the original prompt to solve this, turning the list of required fields into questions. This made the model more likely to generate questions instead of regurgitating a list of requirements. The revised prompt looked like this:
The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly. If the user wants they can talk about anything as soon as the assistant finishes collecting the following fields from the user: What is the user's name? What is the user's departure location? What is the user's destination location? What are the user's dates of travel? What is the user's budget for the trip? Does the user have any additional preferences or constraints?
That solved it!
By reformatting the prompt, in combination with the stop parameter being set to
?, the chatbot started asking one question at a time.
This case study highlights a few important lessons for anyone working with AI chatbots:
- Experiment with different techniques: Don’t be afraid to try different approaches when facing a challenge. In this case, our client tried the stop parameter and reformulated the prompt to achieve their desired outcome.
- Refine your prompts: The perfect prompt is key to making your AI chatbot work effectively. Be prepared to iterate and revise your prompts based on the responses you receive from the model.
- Keep the user experience in mind: Remember, the ultimate goal is to create a chatbot that provides a smooth and enjoyable experience for the user. By asking one question at a time, you can improve the user experience and collect the necessary information more efficiently.
Let’s Tackle Your Chatbot Challenges Together
So there you have it! By exploring different techniques and refining the prompts, our client was able to create a more efficient and enjoyable chatbot experience. And remember, these lessons can be applied to any AI-powered project you’re working on.
As you embark on your AI journey, don’t hesitate to reach out for help or guidance. I’m here to share my experiences and offer support to ensure your AI project is a success.
Happy experimenting, and good luck with your project!