Introducing Learning
The Learning section gathers real conversations that help Smart Assistant continuously improve through positive examples, negative cases, and fallback responses.
The Learning Conversations section is designed to enhance Smart Assistant’s performance and accuracy by collecting real interactions that contribute to model training and optimization.
It allows teams to review how the assistant performs in real-world scenarios, identify improvement opportunities, and mark conversations that serve as learning material.
There are four subsections within Learning: Positive, Negative, Fallback, and Initial Drop-off conversations.
Positive Conversations
Conversations that have been manually marked as positive by a manager from the conversation detail view. They represent accurate, well-handled, and successful interactions where Smart Assistant met or exceeded user expectations.
These examples are used as reinforcement data, helping maintain the quality and tone of the assistant’s answers.
Negative Conversations
Conversations manually marked as negative by a manager. They represent unsatisfactory interactions where Smart Assistant misunderstood the question, failed to answer correctly, or created user frustration.
These cases are used as training corrections, allowing the assistant to learn from its mistakes and refine intent detection or content.
Fallback Conversations
Conversations that include at least one fallback event, moments when Smart Assistant could not answer due to missing knowledge or uncertainty.
They are captured automatically to identify knowledge gaps or topics not yet covered by the assistant’s content base.
Initial Drop-offs
Conversations where users abandon without messages.
By reviewing initial drop-offs, teams can identify opportunities to improve the welcome experience, refine early responses, and reduce friction at the very first steps of the interaction.