Bot Learns from Conversations by Editing Auto Suggestions

I. Introduction

The process by which a bot learns from conversations through editing auto suggestions is a sophisticated feedback loop that enhances the bot's ability to respond accurately over time.

1. Initial Adjustment: The cycle starts when a Customer Service (CS) representative adjusts the auto suggestions in a live chat based on their interactions with customers.

2. Bot Learning: Approximately an hour later, the bot analyzes these conversations and integrates the new insights into its learning system, showcasing what it has learned in a section labeled "Bot Learns From Conversation."

3. Admin Review: An administrator then reviews these new insights to ensure their accuracy and relevance. This step is crucial for maintaining the quality of responses.

4. Knowledge Base Update: Upon approval, the administrator imports the validated information into the bot’s knowledge base.

5. Data Availability: For the bot to utilize this updated information effectively, it must be readily available in its knowledge base.

This continuous learning process allows the bot to refine its auto-suggestions, improving its ability to provide accurate and contextually appropriate responses. By involving both human oversight and automated learning, the system ensures that the bot's knowledge remains up-to-date and reliable.

II. How to use

• To access this feature you can follow: from Dashboard -> Bots -> Bot's Knowledge -> Bot Learns from Conversations.

• There, a tab appears:

• 3 features let you decide each Question - Answer Data:

  • Import (): Import the data to the Knowledge Base.

  • Edit (): Edit your data before import to the Knowledge Base

  • Delete (): Delete unwanted data.