The AI engine works with Natural Language Processing (NLP) for matching text triggers. For this to work you'll need to add training data.
The core of well functioning conversational AI is intent classification. Intent classification is the process of understanding what a user means by the text they type.
For example, a user who types
I want to fly from Amsterdam to San Francisco probably has the intention of booking a flight.
Our Natural Language Processing (NLP) takes care of intent classification, but in order to function it needs to be trained with examples.
Adding a Text Trigger lets you train an intent. When you click the Train button you can add examples for this intent.
Adding more examples enables the classifier to make better predictions. Always make sure you add a least ten examples that are diverse and have little overlap. When you have a sufficient amount of examples you can click Save. This will save your examples and initiate the training process.
Note: Best practice is 10 examples
When you add fewer than 5 examples, only exact matching will be used.
A great way to speed up the development process is reusing intents. This also improves the quality of intent classification.
Every intent you create automatically becomes part of you intent library. You can drag and drop any existing intent in other flows to give different answers depending on the context.
Tip: Always reuse intents
Make sure you do not create semantically similar intents: if two intents are very similar, the probability of misclassification rises. In that case the best practice is to create a more general intent and then ask follow up questions.
Start by creating an intent to understand greetings (hi, hello, hi there, hey, etc). After training this intent with examples, reuse it by dragging and dropping it from the library.
Now, if a user greets, the AI will reply
Hi! How can I help you?. Next, if the user greets again. The AI knows that he already has greeted, so it can reply with
Follow up intents
The AI engine works best if you provide clear examples. In some cases you can also train more generic intents.
For example, if a users says
the closest without any context, it doesn't mean much. But if the user asks it after
I'm searching for a place to park my car, then it becomes relevant. The AI engine makes it possible to create and train these intents in context, and then reuse them in other places.
So, if you have a flow on finding parking spaces, training the followup intent 'the closest' makes sense. You can then easily reuse this intent in a flow on finding restaurants or toilets.