Business Library: Conversational AI Knowledge Base

GEMINI:  Follow-On Suggestions

While there isn't one single, universally agreed-upon formal term for the specific practice of "making suggestions for follow-up questions at the end of answers" in AI, it falls under several related concepts in the fields of:

1. Conversational Design / UX Design for AI:

• Proactive Prompting / Suggesting Next Steps: This is a very common technique in conversational design. The idea is to anticipate what the user might want to ask next or what information they might need, and offer those options to guide the conversation. This prevents the user from having to think of the next question themselves, improving efficiency and user satisfaction.
• Guidance / Handholding: By suggesting follow-up questions, the AI is essentially "guiding" the user through potential avenues of inquiry related to the topic.
• Implicit vs. Explicit Suggestions: Sometimes these are explicit questions ("Would you like to know more about X or Y?"), and sometimes they are more implicit calls to action ("Let me know if you have any other questions about..."). My suggestions tend to be more explicit.
• Reducing Cognitive Load: By offering options, the AI reduces the mental effort required from the user to formulate the next query.

2. Pedagogical Strategies (especially in educational AI or tutoring systems):

• Scaffolding Learning: In an educational context, suggesting next questions can be seen as a form of scaffolding, providing support to help the user explore a topic more deeply and build their understanding.
• Promoting Further Inquiry: This directly encourages the user to continue learning and asking questions, fostering a more active and engaged learning process.
• Directed Discovery: While still user-driven, the suggestions direct the user towards relevant information or related concepts, facilitating a more structured discovery process.

3. Information Retrieval and User Engagement:

• Anticipating User Needs: The AI predicts what information is most likely to be relevant or helpful next, based on the current context and typical user behavior.
• Maintaining Engagement: By providing clear next steps, the AI keeps the conversation flowing and encourages the user to continue interacting, rather than reaching a dead end.

In summary, while you might not find a single, catchy name like "Post-Answer Query Suggestion (PAQS)," it's a deliberate strategy that combines elements of proactive conversational design, pedagogical scaffolding, and user engagement techniques. It aims to make the interaction more helpful, efficient, and less frustrating by anticipating your needs and guiding you toward further relevant information.