What can agents leverage to identify relevant knowledge articles during a Chatter Question session?

During a Chatter Question session, agents can utilize input suggestions while typing to identify relevant knowledge articles. This feature enhances the efficiency of finding appropriate knowledge articles by providing real-time, context-sensitive suggestions based on the keywords and phrases being typed. As agents pose their questions or topics in the Chatter feed, the system analyzes the input and can suggest articles that match or are related, which aids in quickly retrieving valuable information.

This approach provides a streamlined and dynamic way for agents to connect with existing knowledge resources without needing to search manually through the database or other reports. By using input suggestions, agents can interactively refine their queries and access pertinent knowledge articles that can help them address customer inquiries or concerns more effectively.

In contrast, database queries would require more technical skills and be less intuitive for real-time interactions. Scheduled reports on trending topics may not provide timely or specific information during a live session, as they typically present data gathered over a period rather than in the moment. Direct communications with knowledge authors could be helpful but may not be practical during a time-sensitive Chatter Question session. Hence, leveraging input suggestions aligns perfectly with the immediate need for relevant information during such interactions.