Since the chatbot allows users to ask anything at any time and there is no fixed number of questions, the system must intelligently decide when to stop asking and start recommending plans. This can be managed using a context-aware decision mechanism, which includes the following approaches:

1. Minimum Required Data Points Check

• The chatbot will maintain a predefined set of essential data points needed for an accurate recommendation (e.g., age, income, dependents, financial goals, risk preference).
• Once these essential data points are collected, the chatbot can stop asking questions and move to plan recommendations.
• Example: If the user has provided sufficient details for a goal-based plan, the chatbot proceeds with recommendations.

2. Confidence Score Threshold

• The AI assigns a confidence score based on how well it understands the user's needs.
• The score increases as more relevant data is collected.
• Once the score reaches a threshold, the chatbot transitions to recommending plans.
• If confidence is low (due to vague or missing details), it continues asking clarifying questions.

3. Adaptive Questioning Approach

• The chatbot dynamically adapts based on the conversation flow:
  • If the user keeps asking exploratory questions, it provides information instead of moving to recommendations.
  • If the user responds with direct financial details, it fast-tracks the recommendation process.
  • If the user explicitly asks, "What plan do you recommend?", the chatbot immediately provides options.

4. User-Triggered Transition

• The chatbot can give the user the ability to trigger recommendations manually by saying:
  • “You can now proceed to see plan recommendations.”
  • Providing a button: "Show My Plan Recommendations Now"
• This ensures users who want a quick recommendation can skip additional questions.

5. Time-Based or Interaction-Based Transition

• If a user has been interacting for too long (e.g., more than X interactions or Y minutes), the chatbot can suggest moving forward:
  • “I have enough information to recommend plans. Would you like to see them now?”
• If the user continues to provide more details, the chatbot refines its suggestions instead of delaying recommendations.