Hi everyone and welcome.

I’m Liam and today I’m joined by

Naomi and Alex from Azure AI Foundry.

Thank you for joining.

I understand, Naomi,

that you’re going to be talking a little bit

about GPT-5.

Can you give me a little bit of the

highlight of that model?

Yeah, absolutely Liam. I’d love to.

So GPT-5 is the latest addition

to the Azure OpenAI family.

It's the first model suite

that really unifies chat

and reasoning capabilities,

and intelligently routed

by our model router

to optimize performance and save cost.

It's really designed for real-world,

high stakes workloads.

Whether that's advanced coding,

it might be deep analytics,

decision making,

or powering enterprise copilots.

That makes a lot of sense,

but are there any areas

where you've found that it

particularly stands out?

Yeah, there's really three big things

I would mention.

First off, is that frontier reasoning capability.

GPT-5 really excels

at that multi-step logic

and contextual problem solving.

It can reason over large, complex inputs.

Think about thousands of lines of code

or hundreds of pages of documents

without losing that context.

It has also high performance generation.

It's not just about thinking,

it's about delivering

usable and production-ready output code

that runs, analyzes, and stands up

to scrutiny and language

that matches your brand tone.

Lastly, I'll mention

cost-efficient scaling.

So thinking about

the Azure AI Foundry model router,

we can think about customers

can automatically get that right

GPT-5 variant for their task.

Whether that's GPT-5 reasoning

for deep analysis,

they might use GPT-5 mini

for faster turnaround,

or GPT-5 nano for lightweight calls.

Well, that's obviously very compelling,

but Alex, let's be real.

People can get GPT-5

at places outside of Azure.

Why should organizations

be looking at Azure AI Foundry

for this model?

Absolutely Liam.

So enterprises

choose Azure AI Foundry

for the trusted security and compliance.

Also for the seamless integration

they get across the entire

Microsoft stack.

And of course, also the governance tools

that they can use to deploy

GPT-5 responsibly.

And lastly, as Naomi mentioned,

with the model router,

customers get the best balance of cost

and performance.

That makes sense.

Having the trust

and security in their models

is so important

to all of the organizations we work with.

Now Naomi,

you mentioned advanced

frontier reasoning.

I wonder if you can tell me

a little bit more about that and maybe

do you have anything you could show us?

I'd love to.

Of course I'd love to show off

how GPT-5 can assist researchers,

especially in peer reviewing academic work.

So first off, we're going to start off

by prompting GPT-5

to act as a critical peer reviewer.

So here's the prompt that we're using.

First off, act as a critical peer reviewer.

Identify any logical flaws,

suggest extensions, flag any verbosity,

and then provide 20 concrete extensions.

And you can see this sets GPT-5 up to deliver

structured and actionable feedback

that is both practical and thorough.

And what kind of feedback does

GPT-5 actually generate?

So you can see here like

it’s returning a list of suggestions,

each with a clear header

and a concise summary.

So let's look at some of them.

For example,

this paper here doesn't

explain how agents are chosen.

So GPT-5 is suggesting

how to add selection heuristics.

It also recommends

elaborating on success metrics,

in this case to improve clarity

and reproducibility,

and then GPT-5 is also flagging that there’s

a lack of discussion on performance

under large scale scenarios.

Wow, so we're basically getting

technical critique here from GPT-5.

I wonder, does GPT-5 go beyond this?

Like for example,

can we adjust the writing style?

Yeah exactly.

So this is a great question.

So it flags that verbose

or overly technical language,

and it's just suggesting simpler

alternatives for

the mere humans among us.

So you can see here in seconds, GPT

GPT-5 has delivered

20 targeted suggestions

from those structural improvements

to the future research directions.

So with that, of course,

Alex, do we still need humans

to review our papers?

Of course.

So while GPT-5 is a powerful assistant,

human peer review remains

absolutely essential.

So academic research

is going to demand

critical thinking, domain expertise,

and perhaps most importantly,

ethical judgment.

And this is only something

that humans can provide.

Exactly.

I can see this advanced frontier

reasoning being so applicable

to so many different organizations,

not just in research.

It's really great.

Now Alex, we’ve seen how

this can help researchers,

but I wonder,

can you tell us a little more

about how this can help developers,

perhaps to build more advanced applications?

I heard you might have a cool little demo.

Is that something you could show us?

Absolutely.

So Liam, I'm actually using GPT-5

integrated directly into VS Code,

using GitHub Copilot

to generate code for a website

where students can enter different words

like nouns, verbs, adjectives.

And the app is going to generate

both a short story and a matching image.

So what I'm trying to do is create

a fun way to combine

natural language with creativity.

That sounds great.

So how does GPT-5 handle that?

So what we do is we start

with a single prompt.

Here's the prompt.

And the prompt has everything GPT-5 needs

to create the complete application.

So in real world environments

people will typically start

with a simple prompt.

And they're going to use GPT-5

to iterate over that prompt

and make improvements.

That makes sense

because what you're showing here

is quite an extensive prompt,

which is great,

but I can imagine

most people are starting

small a couple of sentences

just to get the idea and start iterating,

is that the right way

of thinking about this?

Absolutely Liam.

So what we have found is that GPT-5 does

quite well at following instructions.

So as you can see in this prompt,

the more details

you give the better in helping you create

exactly the application that you want.

This is great.

So can you show me a little bit more

about what it looks like

once it actually builds the application?

Absolutely.

So let's take a look here at the code.

So GPT-5 is going to instantly produce

a complete HTML page.

We see the embedded JavaScript.

It's going to handle the form inputs,

dynamically build a story using the words,

and then also call an image generation

API like Azure OpenAI Image 1

to visualize the user inputs.

Wait a minute,

we're using GPT-5

to create an application

that's going to use GPT-5

as well as image gen.

That's pretty cool.

I like that.

Absolutely,

and it's all available in Azure AI Foundry.

Let's take a look, Liam

here at the website of what's

generated by this code.

So as you can see here,

the user, the student,

can enter in the various words.

And they can also see the story

and the corresponding image.

Wow, that's amazing.

Just from a single prompt.

Now Naomi, like this is cool

and amazing in the education space,

but I can actually imagine

this might be applicable in other areas.

Is that right?

Yeah, that's exactly right.

So what you've seen here

of course with education,

but we could use this

in all kinds of enterprises

and industry use cases.

And so being able to use perhaps

brand imagery maybe, or recreating,

you know, different portfolios,

or maybe you've got product imagery

that you want to do as part of this too,

this is a great way to be able to use

all of that power of GPT-5.

Amazing.

That's a great example of how GPT-5

makes coding more intuitive

through natural language.

But coding is just one side of the story.

Naomi, I understand through GPT-5

there's something called tool calling

as well as free-form tool calling.

Can you tell me a little bit more

about what that means

and why that's so important?

Yeah, I'd love to share that with you, Liam

and most importantly, to show you it.

So first off, let’s look at one of the

most developer-centric features

that we have in GPT-5.

So instead of rigid JSON, GPT-5

can actually call tools with natural text

like raw Python scripts,

or SQL queries, or configs.

And that means faster integration

and more intuitive workflows.

So unlike traditional tool

calling that leverages JSON,

the free-form makes it more natural language

that allows us to really extend

the types of tools

such as calling Python code

or SQL, as you mentioned.

Is that right?

That's exactly right.

So no wrapper and there is no friction.

So let's check it out.

Let's imagine that we are inventory specialists

at a national retailer.

One of our daily tasks is to generate

those weekly revenue summaries.

So normally this means

I’ve got to write SQL code,

I’ve got to run it,

I’ve got to export the data.

And then I’ve got to format it with Python.

So it's time consuming

and it requires

a lot of different handoffs.

So with GPT-5’s free-form tool calling,

we can really make this an easy way

to automate that entire workflow.

So the model is going to generate that SQL,

it's going to call a tool to execute it,

and then it's going to call another tool

to format the results

and it’s doing it all in one loop.

No JSON and no switching tools,

just natural language.

So now we're defining two tools here,

So SQL Execute Lite

basically helps us here.

So we're going to define two tools

sql_exec_sqlite runs

that SQL and returns results in CSV.

code_exec_python is going to run our Python

and return our output.

So I'm going to ask GPT-5 here

to generate that SQL

to create a sales table

and that compute revenue.

And then I'm going to ask it

to go run a conversation loop.

We're going to use that loop

to basically send the prompt to GPT-5,

detect that there's a tool called,

execute the tool,

and then send that output back to GPT-5

and continue until it's done.

So as you can see here,

GPT-5 has returned a neatly formatted

summary of all of those product

revenues that we need,

all via orchestrated toolchains.

Well that's amazing.

So not only did GPT-5 figure out

which tools it needed to use,

but it created

all of the natural language text

to actually call those tools

and execute it on its behalf.

That's really amazing.

I can imagine how this could open it

up for all kinds

of different types of tools

within applications.

Exactly.

So there's really three big ones

that we can look at,

so there's no JSON that's been required,

you can see that's natural output,

and then there's

multi-tool orchestration.

All of this makes it

a very developer-centric tool.

So we've simplified scripting,

we sped up all of that reporting,

and we've enabled richer

and more intuitive interactions

into the data.

GPT-5 maintains context

across all of these steps,

so it can chain

those tool calls intelligently,

and it makes it perfect

for dynamic automation

and multi-tool orchestration.

This is really a game changer

for developers in any industry.

It's absolutely true.

I mean, this is going to be applicable

to so many different

organizations and developers.

Naomi and Alex, amazing demos.

Before we wrap up,

what should AI practitioners

and developers take away from today?

Yeah, so GPT-5 is so much more

than just a model.

It is a toolkit for reasoning,

for generation and orchestration.

And Alex, how can people get started?

So the best way is to start small.

Pick one high-impact use case

and then try GPT-5

on Azure AI Foundry.

Naomi and Alex,

thank you for walking us through this.

And thanks to all of you for watching.

And go start building with GPT-5 now.