>> You're not going to want to miss
this episode of the AI Show where we talk about how
Azure Open AI service in AI Builder
is bringing new levels of efficiency and productivity.
Stay tuned. What is
up my party people from around the world.
Thank you for joining The AI Show today.
My name is Rob Nunez.
It's been a while since I've been on the show,
but I was here previously to talk
about some things around AI Builder,
a very amazing tool in Power Platform.
If you've never heard of it before,
you'll hear about it again today.
Seth could not be here today,
but I'm here to take the reins and
introduce our guests today who is joining
us for the second time so we have
Asheesh Bhatia who works on the Power Automate team,
who's going to be highlighting some of
the new capabilities of
AI Builder so Asheesh welcome back to the show.
>> Thank you all. It's great to be
back and nice talking to you again.
I love this conversation and I hope it going to be good today.
>> Absolutely and I hope everyone's doing great.
Just as Seth wants to know,
I want to know where everyone is tuning in around the globe.
Feel free to write in the chat.
We'd love to hear from people around
the world and just get to know each other.
Asheesh, the last time we were on the show,
we covered some of the existing capabilities within AI Builder.
Some really great use-case scenarios and an incredible demo.
If you haven't checked that episode out,
feel free to do so. It's absolutely amazing.
But for those that are joining for the first time
who either may have not heard about AI Builder,
don't know what it is, what can it do.
Maybe give a quick high-level overview of what it is.
This way, we preface
with that before we happen to some of the new capabilities.
>> Absolutely, that makes sense so can just level-set people.
AI Builder is the no-code AI tool within Power Platform.
Power Platform is collection of Power Automate, Power pages,
Power Apps, PVA and these are
just the power tools which are specialized to do a specific task.
The bunch of horizontal capabilities like data works,
which is the data management layer,
and then AI Builder,
which is the AI layer for all of these Power tools.
Our goal always has been to keep it no-code so that you don't
have to write any piece of code
to bring the latest and greatest AI.
So far we've been focusing on document related technologies,
texts related technologies, and images.
There's also a couple of models
which you can work with structured data as well.
That has been the case so far.
But with some of recent announcements,
we are bringing generative AI capabilities.
Again, that's going to be a focus of our conversation today.
>> Absolutely. We all know there's been a ton of
hype around Open AI ChatGPT.
When we think about how fast it has propelled the industry.
I think it was common sense for us to
incorporate it into our products at some point.
Quick question for you before we happen
to do any of the demo and stuff.
How does this GPT model work?
What data sources is this model pulling
from just so we can level set where that's at
and then we can dive into some hands-on stuff so we can get
into the nitty-gritty and see the capability in action?
>> Sure, makes sense. The generative AI model
that we're using here is Azure Open AI,
so large language model.
Large language model is a foundation model,
it's trained on wealth of internet data since very good at
discerning language and generating
language in human-like form factor.
Again, it's a multimodel.
It can do multiple scenarios with it.
You can summarize that. You can categorize,
you can ask it to generate your text.
All of the model that we are talking about today
are going to be pre-built models.
You're not necessarily building a new model,
you just prompting the existing model
to perform new tasks for you.
A lot of times you can bundle in some contexts data for
it to use it as anchor data or grounding data we call it.
Again, that's all you need to do
to harness the power of generative AI in your own creation.
>> That's the beauty of AI Builder.
That's why I love it so much.
Just the ability to just hop in and be able to
take full advantage of these capabilities.
It's absolutely incredible Asheesh and I would
love for us to see it in motion.
I know you have a demo plan to show us,
so let's get into that and see this new capability.
>> Let's go to my screen-share.
If this feels familiar,
this is AI Builder form factor or interface.
Anytime you go to Power Automate or
Power Apps on the side car here,
on the left side, you will see a bunch of capabilities and then
you'll see AI Builder or all the there as well.
Within AI Builder, you'll see Explore,
which is all the AI capabilities that are available.
Models will give you all your models,
all the model that you have been working on.
Then within Power Automate,
there is a first-class citizenship
for document automation scenarios.
Again, just to orient people,
when you come to the Explore,
you can see a lot of document capabilities,
text capabilities, and so on and so forth.
The texts you will see a new model that has lit
up because of the generative AI model
and Azure Open AI integration,
it's called text creation here.
When we click it, we come to a playground-like experience.
Our goal in AI Builder is always to simplify the user experience.
The persona that we have is the no-code citizen developer persona.
We like to keep things lightweight and keep
the focus on getting the job done, getting the task done.
A lot of other playgrounds,
if you would have seen,
they're legit tweak parameters, pick models,
all of that clutter has been just taken
away and we bring you right in the center of your work,
which is pick a template,
pick an existing template and we have
populated with a bunch of templates,
and we're going to talk about that.
Most of the time you will be prompting,
you will be telling the model what to
do and checking out the response.
This is your playground,
this is your experimentation place.
Once you have done your experiment,
then you can take it into a Power Apps or Power Automate workflow.
>> Is it possible Asheesh, can you zoom in a little bit on
the desktop area just so we can get a little bit of
a closer look into the template itself?
There we go. Perfect.
>> Let's start with a few warm-up activities.
What I think is we're going to start with
is top of the stack you will see summarize.
Summarize is one of the most popular scenario when we are
working with generative AI because there's ton of
text out there and people want to summarize and
gets stuff out from unstructured data.
The templates are a starting point.
They give you a base boilerplate prompt.
You don't have to stick to it.
You can create your own prompt,
but in this case,
we'll start with what we have.
What I'm going to do is I'm going to bring
a sample text or an e-mail in this case and this e-mail.
>> The field talks about some order details and stuff.
We don't know what it is, so we want to summarize this.
In this case, what I'm going to ask the model is
to take away all of this.
I'll say just summarize this for me in a paragraph or two.
I'm going to hit "Test" button and hopefully,
the model generates a summary for me.
>> Nice.
>> We see this model starts to create
a summary of whatever is in that e-mail.
I observe it and if the response looks good, I can move forward.
But in this case what I want to do
is I want to change the response a little bit,
because I see it talks about an order and the order
is missing the summary and I would want
to capture the order if it is there.
I'm going to change the instruction and come back and say,
"Hey, can you please add the order number if it is available?"
With this, it starts to again generate,
and the model is now responding at ordinal.
Sometimes things don't go your way.
I mean, this is prompt engineering.
Prompt engineering is like having a conversation with a person.
You ask the right question, you get the right response.
Sometimes you're not asking the right question,
you don't get right, things like that.
It's an iterative process and
you might not get it right in the first time.
The second time I would always say keep
trying and keep making a prompt.
>> Pretty much play around with it till it
really starts to fit what you're looking for, right?
>> Absolutely. This is great.
I can actually work with this,
but what I would like to do also
is getting some more structure to this response.
I come to the end of this and I say, hey,
generate the response in a given format,
and the format it's getting out a number and then give me summary.
Let's see if this works and I'll explain
what is it actually doing.
When we're doing prompt engineering,
a lot of times you will see the prompt goes first and then
the context data or
grounding data or supporting data then goes next.
But it's a free-form kind of text.
You can give some instructions on the top
and then you can give some instructions on the bottom as well.
What I've done is I was
happy with the outcome that it was generating,
but I did a little bit of formatting.
Work towards the bottom of the prompt and say,
hey, I love what you're giving me,
but just give it in this format,
so I can work with it a little bit
better and it's a little bit more structured for me.
If I'm sending this to a support staff or somebody,
they can easily grab the order number
and then they can look at the summary and do something with it.
>> Absolutely. Go ahead.
>> That's summarization. Again,
there are a bunch of other things that we can talk about.
I'm going to talk about complaints a little bit later.
Go into the flow because I have a flow created which is all
about controlling forward and that kind of thing.
Go ahead. Did you have a question?
>> No, I was just saying it's
interesting to see the same way that
AI Builder pretty much prompts different types of actions to take.
This is just another way of showcasing how easy it is to use.
Not knowing where to start sometimes is difficult,
but giving the user the ability to identify what they might need,
and then be able to create the instruction and prompts is like
the user interface just continues to
amaze me and this is just another prime example of that,
so I absolutely love it.
>> So with that, let me take you to Power,
Automate and show how power of this can be used in automation.
What I have here is flow, pre-created flow.
We're trying to automatically respond to customer complaining.
We see a ton of scenarios where customer complaints are coming
to a shared mailbox or some other form factor.
You want to quickly respond to your customers
and address their complaints.
In this case, what I've done is I've
created a triggered every time an e-mail comes to me,
and then you can specify
whatever criteria I just picked an easy criteria.
The subject filter is demo and complain. I want to address it.
But you can imagine that this could be something else.
It could be a shared mailbox as you
are observing the incoming e-mails.
What I've also done is I've added a step called HTML to text.
A lot of our e-mails are HTML content,
and they have a lot of HTML tags to it.
Remember, the token window of the model is limited.
About 4,000 characters.
The more stuff we can pack in it,
which is not relevant,
we're using important token space that
otherwise we could use for generation of the content.
What I'm trying to do is strip away
all the HTML content
from the e-mail body and just preserve the text.
That way I can give the GPT model my body of text
and it can then use that to just auto-generate the respond.
That's the next step.
This is where the next-
>> Where the magic happens, right?
>> Where the magic happens.
Edit the create text with GPT model the way you discover it.
If you go to add actions,
when you search for AI Builder or GPT,
you should see a new action light up.
In this case we already have it here,
so we'll skip that step.
>> Just like any other AI Builder model, right?
You're creating Power Automate Flow,
going through the same processes,
and then you hop into this and then the create text,
with GPT action is just ready to go and ready to use. Beautiful.
>> Right now we've pre-populated with something,
but we can go in to create more hints.
Just here, I feel this is unique to this model,
and we have done an experiment at it for the first.
What we've done is, we've created
a prompt engineering interface right inside Power Automate.
When you hit "Create prompt",
you will see a prompt engineering experience,
much like in the export page,
just pop up within Power Automate.
What you can do here is you don't have to go to the Explore page,
do your experimentation, bring your prompt here.
We of course can't do that.
There is copy option, you can bring your prompt here.
But if you start within Power Automate itself,
you can do that experimentation right here.
You don't have to leave that space.
For example, what we are trying to do here
is we're saying, hey,
anytime you get generating a response to a given text,
be humble and apologetic in your tone,
be creative in your response.
Always restate the problem so that
you're telling the user that we hear you,
and we acknowledge your problem,
and then give them
a brief about how we're going to do deal with it.
Before I send this to my customers,
I would always want to test it.
What I have done is I've brought in a couple of example e-mails.
In this case, what I'm going to do is I'm going to
copy body of an e-mail.
Standard complaint text e-mail.
For example, if I drop it here,
this talks about, hey,
I bought something, I'm disappointed, and all of that.
We hit "Test" here and we should see a reply.
The response is we apologize for
the inconvenience and experience with this product,
and we understand that you're not enjoying it or whatever and
we'll process your refund if that was
the ask or that's your business action.
You can customize what is your business action,
how you want to address a complaint in the prompt.
Based on that, the model generate something.
You feel happy with it if you like what it is generating,
and you feel this will generalize well to
most of your complaints or your product complaints,
that you can just pick this model and bring it back here.
In this case, we have already done that,
so we'll ignore that.
What we have done, as you'll see,
we have replaced the sample texts that we were experimenting
within the content and that dynamic content is the body of e-mail.
So anytime an e-mail comes in,
we have stripped off all the HTML,
we have taken all the text and that becomes your automation.
What I also want to talk about quickly is
before we run this is the human agency aspect,
that approval aspect, the human in the loop aspect of it.
>> Probably one of the most important aspects,
honestly. This is incredible.
>> Especially for two reasons,
a, this is an automation workflow.
You might be automatically responding to all your customers,
so you want to have some human oversight on it.
Also, since it's AI-generated content,
you want to be very sure what you
are responding to your customers.
What I've done in this case is I've picked
up a start and wait approval step,
which has a couple of states.
I'm sending it to a person.
You can send it to a group of people or whoever.
Then I'm picking up all the text that is getting generated.
I have the original content.
So the reviewer is getting all the information that they need.
Here's the AI-generated piece.
Here was the original customer problem,
who was the customer and stuff like that.
Once that is approved by the human observer or human oversight,
then you can send that e-mail back to your customer.
That's the flow. We'll save it,
and we can actually take it for a spin.
What we see here now is the flow has executed and
it has paused at the place
where it is waiting for a human approval.
What I'm going to do is I'm going to go to approvals here.
I see that one minute ago I have a new approval.
I can of course just click and approve,
but what I want to do is I want to check
what's waiting for my approval.
It gives me here is the AI-generated response.
It's going to John Doe,
and it is the content.
It also tells me who created
this initial e-mail and then just what was the complaint.
If I'm happy that all of this,
I can just quickly go and say, approve and confirm.
Once I click that and approve,
it goes back to the flow,
and we'll see the request of the flow gets executed then.
>> Approval went through, but no,
I think, honestly [inaudible],
first of all, incredible demo showcasing the new technology.
I mean, I'm super,
super excited of where AI Builder
is and where it's heading with this new capability as well.
This is just a perfect glance
into what this world is going to look like.
I think real quick question before we wrap up is,
how can people currently get their hands on it?
Is it available for everyone?
I guess a shot in the dark when we may see it
globally available for the world to use and try out.
>> Absolutely. Today the capability
is available as a gated preview in US only.
If you go to your US subscription
or US environments in Power Automate or Power Apps,
you will in-product sign-up experience,
where you can just sign up for this capability.
Again, folks who are in US can latch onto it,
sign on to it and we're
gradually approving everybody who's trying to come in.
We're working super hard to expand it beyond US.
It'll take a while but that's something that we are locked on and
focused on that how can we expand to
other regions and bring this capability to everybody?
>> Awesome. Sounds good.
Well, [inaudible] thank you,
thank you so much for joining us again,
and I'm looking forward to us working together in the future.
I'm sure there's going to be a lot
more AI Builder content to come.
But thank you all for joining,
and we'll see you guys soon.
>> Thank you.
[MUSIC]