Model user Intents and train your Virtual Agent to understand them!
To help your end customers achieve their goals, your Virtual Agent needs some training to match some well-defined user intents with the bandwidth of potential ways to express those intents. Learn more about Intents in the developer documentation.
#1 Create a Flow and navigate to the NLU Engine
Start with creating a new Flow and name it e.g. "IT Service Request". Then select "NLU" from the top menu.
#2 Create an Intent
Click Create Intent on the left-hand side. Give your Intent a name that describes its purpose and serves as an identifier e.g. "createTicket". Enter an example sentence a user could use to express their intent e.g.: "I need to create ticket". In the lines below, add more examples like "open a ticket", "i have a problem". Save your Intent.
#3 Create another Intent
Create a second Intent. Use e.g. "resetPassword" with the sentences "forgot my password", "lost password" and "cannot login" and save the Intent.
#4 Train Intents
Click Train Intents on top of the screen to trigger a machine learning process based on your samples. You can always check the task list in the top bar to see what running tasks.
#5 Try it out!
Open the Interaction Panel, switch to "Settings" and activate "Expert mode". Go back to the chat and type "file ticket". The Virtual Agent won't respond (yet) but you see meta data indicating that the Virtual Agent clearly understood the Intent "createTicket".
#6 Look behind the scenes
Switch to the "Info" tab in the Interaction Panel. You'll see a JSON object giving you insights into the process: "createTicket" was recognized as an Intent and there is an IntentScore representing the confidence level.