Multi-turn Conversations and Context Preservation
Multi-step prompts and complex interactions involve creating a series of responses that build upon each other. In these conversations, the chatbot retains context from previous exchanges, which enables a more coherent and engaging interaction with the user.
In multi-turn conversations, context preservation is crucial. The system needs to remember key details from earlier in the interaction, such as the user's question or provided information, to generate appropriate responses.
For example, when asking a chatbot about a product, the user might first inquire about the product details, and then ask a follow-up question regarding pricing. The chatbot needs to understand that both questions are related and respond with continuity.
Examples: Summarization with Follow-up Q&A
Summarization is a key application of multi-step prompts. The goal is to summarize key points from a larger body of text or information while maintaining the ability to engage in follow-up questions that refer to specific details in the summary.
Here’s an example of how multi-step prompts can be used for summarizing text and answering follow-up questions:
User: "Can you summarize the article on machine learning for me?"
Chatbot: "Sure! The article discusses the basics of machine learning, its applications in various industries, and key techniques like supervised and unsupervised learning. It also mentions the challenges of data quality and model accuracy."
User: "What is supervised learning?"
Chatbot: "Supervised learning is a type of machine learning where the model is trained using labeled data, meaning the input comes with a known output. It helps in predicting outcomes like classification and regression tasks."
In this example, the chatbot first provides a high-level summary of the article and is then able to answer specific follow-up questions about the details provided in the summary.
Exercise: Summarization Tool with Key Points
In this exercise, you will create a summarization tool using multi-step prompts. The chatbot will first summarize a provided article and then allow users to ask follow-up questions about specific points from the summary. This can be useful for applications like educational platforms or content summarization services.
Steps for the exercise:
- Step 1: Create a prompt that generates a concise summary of an article or a long passage of text.
- Step 2: Craft follow-up prompts that can answer specific questions related to the content of the summary.
- Step 3: Implement response chaining, where each new prompt depends on the context from the previous responses.
Here’s an example of a summarized response with follow-up prompts:
User: "Can you summarize this article on the impact of climate change?"
Chatbot: "The article discusses the current effects of climate change on ecosystems and communities, such as rising sea levels, extreme weather events, and habitat loss. It also covers the potential future impacts and mitigation strategies."
User: "What are the mitigation strategies mentioned?"
Chatbot: "The article mentions several strategies, including reducing carbon emissions, transitioning to renewable energy, and protecting vulnerable ecosystems."
10 Relevant Prompt Examples for Multi-step Conversations
- “Can you summarize the key points of the article on climate change?”
- “What are the main takeaways from the research paper on AI ethics?”
- “What is the first step in the process of solving a linear algebra problem?”
- “Can you clarify what you mean by ‘supervised learning’ in machine learning?”
- “Tell me more about the strategies to reduce plastic waste.”
- “What is the difference between deep learning and machine learning?”
- “Can you break down the concept of blockchain into simpler terms?”
- “What are the effects of the recent economic policies on small businesses?”
- “How does cloud computing benefit businesses in terms of scalability?”
- “What steps can individuals take to reduce their carbon footprint?”
