Introduction

This summary provides an in-depth explanation of the process involved in designing and implementing an effective AI chatbot flow, focusing on intent detection, flow routing, and system optimization.

The goal is to ensure the chatbot accurately identifies user intents and directs conversations seamlessly, achieving a high success rate in flow triggering.

The process involves creating intents, configuring an intent selector, and integrating these components within the automation framework to enhance user experience and operational efficiency.

Detailed Breakdown of the AI Chatbot Flow Design

1. Overall Flow Architecture

The core structure of the chatbot is designed to detect the user's intent and route the conversation accordingly. The flow is laid out in a stepwise manner:

• Intent Detection: Identifying what the user wants.

• Flow Routing: Sending the user to the appropriate conversation flow based on detected intent.

• Default Reply Handling: Ensuring continuous engagement even if intent detection is uncertain.

2. Creating and Managing Intents

Intents are predefined categories representing user goals, such as small talk, FAQ, or coaching call scheduling. The process involves:

Note: The intent detection system is intentionally disabled for auto-detection because triggering intents within chat completions yields higher accuracy (~95%).

3. Configuring Intent Detection in Chat Completions

Instead of relying on the system's automatic intent detection, the intents are used within chat completion functions. This approach:

• Improves accuracy by leveraging chat context.

• Ensures consistent flow triggering.

• Reduces false positives.

Implementation details:

• Disable auto intent detection.

• Use intents as markers within chat completions.

• Assign intents to specific flows (e.g., small talk, FAQ, coaching).

4. Flow Assignment and Routing

Each intent is linked to a specific flow:

• Small Talk intent → Small Talk Flow

• FAQ intent → FAQ Support Flow

• Coaching Call intent → Scheduling Flow

Example:

Intent Name: small_talk
Description: Handles casual conversations
Confidence Minimum: 0.8
Json Storage: Random field
Connected to: Small Talk Flow

Similarly, other intents follow the same pattern, ensuring each user input is mapped to the correct flow based on the detected intent.

5. Using the AI Chot Intent Selector

The AI Chot Intent Selector acts as a decision engine that:

• Analyzes user input.

• Determines the most appropriate intent.

• Routes the conversation to the corresponding flow.

Key points:

• It is triggered under the default reply.

• Every user reply activates the selector.

• The selector's decision guides the flow transition.

6. Default Reply and Continuous Context

The default reply is set to active, meaning:

• Every user message triggers the intent selector.

• The chat history is maintained, providing context.

• The system can re-evaluate the user's intent at each step.

This setup ensures dynamic and context-aware routing, improving overall accuracy and user experience.

7. Flow Repetition and Context Preservation

Because chat history is enabled, the AI:

• Remembers previous interactions.

• Adjusts flow routing based on ongoing conversation.

• Prevents misclassification by considering prior context.

This iterative process allows the chatbot to refine its understanding and maintain conversation coherence.

Summary

In conclusion, this structured approach to intent creation, detection, and flow routing significantly enhances the chatbot's performance. By disabling auto intent detection and leveraging chat completions with predefined intents, the system achieves a 95% success rate in directing users to the correct flow. The AI Chot Intent Selector serves as a pivotal component, ensuring conversations are contextually relevant and seamlessly guided. The combination of persistent chat history and precise intent assignment creates a robust, intelligent conversational experience that adapts dynamically to user needs.

Summary Highlights

• Intent Creation & Management

  • Clear naming conventions

  • Confidence threshold set to 0.8

  • Stored in JSON fields

• Flow Routing Strategy

  • Connect intents to specific flows

  • Use within chat completions for accuracy

• AI Chot Intent Selector

  • Acts as decision engine

  • Triggered on every reply

  • Ensures correct flow transition

• Default Reply & Context

  • Maintains conversation flow

  • Enables iterative intent detection

• System Optimization

  • Achieves ~95% accuracy

  • Improves user experience

  • Ensures conversation relevance