Decision trees are a powerful tool in the realm of chatbots, allowing them to handle complex queries with a structured and logical approach. In this article you can read how decision tree prompting can improve your chatbot's interactions.
Decision tree prompting will enable your chatbot to ask follow-up questions and respond appropriately based on the user's input. We'll explore some common scenarios and provide detailed examples to help you understand and implement decision tree Instructions in your chatbot.
What is a decision tree instruction?
A decision tree instruction guides a chatbot on how to respond based on a series of conditions that stem from a user's response. It essentially allows the chatbot to handle a conversation flow that can branch out into various paths, depending on the information provided by the user.
Example scenario: tracking a package
Let's consider a practical example involving a customer inquiring about the whereabouts of their package:
- Initial inquiry: The user asks, "Where is my package?"
- Chatbot response: The chatbot replies, "I'm here to help! What's your order number?"
- User response and chatbot follow-up:
- If the order number starts with 1-: The chatbot informs the user, "The tracking code can be found in the order confirmation email you received."
- If the order number starts with 2-: The chatbot advises, "I recommend calling customer service to get the most up-to-date status."
- If the order number starts differently: The chatbot responds, "This order number is not familiar to me and is not recognized in our system."
The instruction you write would be: If someone asks about the whereabouts of their package, ask for the order number. If the order number starts with 1-, inform them that the tracking code can be found in the order confirmation email. If the order number starts with 2-, advise them to call customer service for the order status. If the order number starts differently, inform them that the order is not recognized in the system.
This decision tree approach ensures that the chatbot can provide specific guidance based on the unique order number, enhancing the user experience by providing tailored responses.
Example scenario: modifying an order
Another scenario that can benefit from decision tree prompting is when a user wants to make changes to their order:
- User query: "I want to make changes to my order."
- Chatbot response: "Can you please tell me how long ago the order was placed?"
- Decision tree:
- If less than 24 hours ago: The chatbot guides, "You can modify your order by logging into your account."
- If more than 24 hours ago: The chatbot explains, "Changes are not possible as the order is already on its way, but you can always return unwanted products or place a new order."
The instruction you write would be: When someone wants to make changes to an order, first ask how long ago the order was placed. If less than 24 hours, inform them they can modify their order by logging into their account. If more than 24 hours, explain that changes are not possible as the order is already on its way, but they can always return unwanted products or place a new order.
Implementing Decision Tree Instructions
To effectively implement decision tree Instructions in your chatbot, follow these steps:
- Identify scenarios: Determine scenarios where users may have queries that require a series of logical follow-ups.
- Map out the decision tree: Visualize the decision tree, outlining each potential path the conversation could take based on the user’s responses.
- Script each branch: Write specific instructions for the chatbot for each decision point in the tree. This includes the questions it should ask and the responses it should provide.
- Test and refine: Use the Interactive tester to simulate the chatbot conversation and refine the branches based on real user interactions.