Dialogflow Intents: Know what your users want [Basics 1/3]

Hey, everyone. This is the first of three
videos that will teach you the basics of Dialogflow, an
incredible tool for building conversational experiences
that help your users get things done. My name's Dan
Imrie-Situnayake and I'm a developer advocate at Google. In this video,
we're going to talk about intent matching,
which is how we figure out what a user wants. When we're building a
conversational experience, our number one task
is to understand what our users are saying. With Dialogflow, our
fundamental tool for doing this is the intent. We create an intent for
anything a user might request.

In this example, we'll
create a conversational app to handle customer service
for a bike repair shop. For each intent, we provide
examples of the various ways the user might communicate it. You'll only need a few
examples to get started. Dialogflow will use
this information to train a machine-learning
model to understand not just the examples
we've entered, but numerous other phrases
that mean the same thing. Now whenever the
user says something, our model will match it so
whichever intent is a good fit. And for each intent,
we can specify how our app will respond. Let's see an example of
intents being matched. We can see our intents on the
right, along with our example phrases, and our user
interaction on the left. What's your opening time? We're open from
10:00 AM to 4:00 PM. Notice how even when the
user words their question differently from the
examples in our intent, we still know what they mean.

That's possible thanks to
our machine-learning model. I need to get my bike fixed. No problem. How about 11:00 AM? So we've seen how the examples
we provide in our intents train a machine-learning model
that matches what they user says to one intent or another. A typical Dialogflow
agent, which represents a single
conversational experience, might have anywhere from a few
to thousands of intents, each trained to recognize
a specific user need. You can even build
agents that are able to understand and
respond to multiple languages. As people use your
agent, you can incorporate what they
say into its intents as training examples.

So the more usage you get, the
smarter your agent becomes. In the next video,
we'll go over how to extract detailed
information and parameters from what the user says. And the final video
of this series will show how we can
bring everything together to build rich,
natural conversations. Check out the links in
the description for more information about intents. Thanks for watching and
see you in the next video. [MUSIC PLAYING] .

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