This content provides a condensed overview of Google's 8-hour Generative AI (Gen AI) leadership course, breaking down its five modules and offering practical advice for passing the official certification exam. It aims to demystify Gen AI concepts and guide professionals in leveraging AI for organizational transformation.
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I took Google's 8hour Gen AI leadership
course so you don't have to. And once
we've gone through the condensed
version, I'll share a bonus at the end
outlining the exact steps to follow if
you want to pass Google's official
certification exam. I'm Ali Salam. I
currently work as a director in a tech
company. And on this channel, I'll help
you turn tech and finance into your
personal advantage. Let's go. The course
is structured into five modules. The
first module explores the full
capabilities of generative AI. The
second module unlocks foundational
concepts of generative AI by defining it
and differentiating it from AI and
machine learning. The third module
provides a comprehensive overview of the
Gen AI landscape. The fourth module
teaches you how to use Gen AI apps by
covering key prompting techniques and
concepts like grounding and retrieval
augmented generation to transform your
work. And the last module explores how
to build and deploy generative AI
agents, covering their core components,
advanced techniques, and outlining a
plan for transforming your organization.
Module one is called Gen AI beyond the
chatbot. Let's kick things off with the
definition. Generative AI is a specific
type of AI that focuses on generating
new content and ideas. It's multimodal,
meaning that it can work with text,
images, code, and more. And Google
groups its capabilities into four
categories. The ability to create new
content, summarize information, discover
information at the right time, and
automate tasks that used to be manual.
And at the core of these capabilities
are something called foundational
models. Examples would be Google's
Gemini, OpenAI's GPT, or Anthropics
Claude. And if you're wondering how
foundational models fits into the
broader landscape of artificial
intelligence, don't worry about it.
We'll cover that in module 2. But for
now, just remember that foundational
models share three key features. They
are trained on diverse data, making them
flexible across many use cases, but at
the same time adaptable to niche domains
through targeted training. So trained on
diverse data, flexible and adaptable.
And the way we interact with them is
through something called prompting which
is essentially what you do when you're
talking to chat GPT either in the chat
or through your voice. Now since this is
a Google course there are two key
products that you need to be aware of
and first is Gemini which is the
foundational model behind Gemini the app
which is Google's equivalent of chat
GPT. It also powers workspace
integrations like Docs, Gmail, and
Slides where you can draft emails,
generate images, or just summarize
notes. Also, Gemini powers Google's
cloud service where it helps you write
and debug code or analyze large amounts
of data in BigQuery. And the second
product that you need to be aware of is
a product called Vertex AI, which is
Google's unified machine learning
platform. It gives you access to models
like Gemini and lets you fine-tune them
and eventually drop them into
production. And if you haven't heard
about Vertex AI, don't worry about it.
It's mainly an offering that is targeted
against businesses rather than retail.
And lastly on module one are two more
highle topics that you need to be aware
of. The first is that Google calls
itself an AI first company. That means
that AI is integrated across their
ecosystem, built with security and
ethics at its core and most importantly
for you is that they advocate for an
open approach which is awesome. It
essentially means that you're not locked
into using models like Gemini. Instead,
you can plug in other models like GPT,
Claude or Llama when you're setting up
your AI workflows in Google's ecosystem.
And the second highle topic comes down
to the strategy of how you apply AI
adoption in your company. Google
advocates a combined top-down and bottom
up approach where leaders set the vision
and priority of what AI should achieve.
Whereas employees on the ground try to
identify practical applications of AI
within their workspace and feed them up.
When done right, these two streams will
help reinforce each other. Moving on to
module two, which is called unlock
foundational concepts. And this module
is all about connecting all of those
different terms that you've heard in AI
and showing how they all fit together.
And we'll start at the top. Artificial
intelligence is simply machines doing
tasks that would normally require human
level intelligence. Inside AI, you have
machine learning, which are algorithms
that learn from data to perform specific
tasks. And a subset of machine learning
is called deep learning which uses
multi-layered neural networks to
identify complex patterns. And within
this space you will find generative AI
which again are machine learning that
focuses on creating new content. And at
the core are the foundational models
which are machine learning models that
are trained to execute various different
tasks. And lastly, a subset of those are
large language models which are
specifically designed to understand and
generate human language. Now let's talk
about the fuel for these models which is
data. And really there are two types of
data. You have structured data which is
clean, organized, often times divided
into columns and rows. Think about
databases or spreadsheets. And then you
have the other type of data which is
often times referred to as unstructured
data. This is usually raw, messy data
that doesn't have a predefined
structure. Think about data like
customer emails, social media posts, or
call transcripts. And our AI models can
of course work with both. But what
really matters are two things. And the
first is quality of the data. As
famously said by someone very smart,
garbage in equals garbage out. And the
second thing is accessibility. meaning
that the data needs to be available at
the right time in the right format. Now
the data can include numbers, dates,
text, images, even sound. But it needs
to comply with these two conditions. And
once we have the right data, models can
start to learn using one of three
approaches. The first is called
supervised learning where models are
trained on labeled data to predict
outcomes. And the second approach is
called unsupervised learning where
models get trained on unstructured data
to try and find complex patterns. And
the last approach is called
reinforcement learning where models
learn through trial and error and
feedback loops. And by the way, if
you're a nerd like me, reinforcement
learning is what powered those Starcraft
and Dota bots a couple of years ago that
ended up beating all the pro players.
Anyways, I digress. Let's talk about how
all of this fits into practice. Google
frames the machine learning life cycle
into four stages. First you have data
preparation where you collect, clean and
transform raw data. From there you do
your model training which essentially
builds your model based on the data.
Third step is deployment where you put
your model in production. And lastly is
management where you monitor, maintain
and improve your model over time. So in
short module two connects the dots. what
AI really is, how data drives it, and
the way machines learn. Kind of like a
simple road map that makes everything in
the gen AI space feel less confusing.
Module three is called navigating the
landscape, and it covers two main
topics. First is what you need to
consider before starting a Gen AI
project, and second are the five layers
of the AI landscape. Before starting any
Gen AI project, Google says that you
should assess two areas, needs and
resources. And it breaks down the needs
into six categories for evaluation.
First is scale. And scale refers to the
overall breadth of the use case across
the organization such as the number of
users, data volume, and workflows.
Second is customization. How tailored
does the AI need to be in order to fit
your organizational needs? Is general
purpose models enough or do you need
something that is fine-tuned? And third
is user interactions. How are people
going to engage with the AI? Will it be
through a chat? Will it be embedded into
certain workflows? Or will it just run
automatically in the background? And
fourth is privacy. How sensitive is the
data that is going to be involved in the
workflow? Is it public information,
internal knowledge or regulated data
like in healthcare or finance? Fifth is
latency. So how fast does the AI need to
respond? Is a few seconds okay or do you
need something that is real time? And
the last topic is connectivity which are
the network conditions that the model
needs to run under. Will it always be
cloud connected or does it need to
function in low connectivity devices
such as factories, fieldwork or maybe
even edge devices. Shifting gear into
the second assessment category, which
are your resources. This is actually
super straightforward. It boils down to
people, money, and time. So, do you have
access to the right talent such as AI
expertise? What's your project budget
and what's the project timeline? And
really, that's the list to consider if
you're going to start an AI project.
Let's take a look at the second part of
the module, which covers the five layers
of the AI landscape. The first layer is
Gen AI powered applications. This layer
you're likely very familiar with. It's
going to be your Claude, Chachi PT,
Llama, and Friends. One level deeper are
the agents. They are autonomous systems
that use foundational models to reason
and act. They operate in reasoning loops
observing, interpreting, and iterating.
They use tools to interact with data,
software, and hardware. And they rely on
models such as chat GPT as the brain of
the system. And an example here could be
as simple as an AI assistant that
researches prospects and updates CRM
once it's found the relevant
information. From there, you have
platforms. These are managed
environments that provides the tools and
infrastructure to build, deploy, and
manage AI. And here's where Vertex AI
comes in. It will let you do two key
things. First is Model Garden, which
lets you pick Google models, third party
options, or even open-source models. And
really, this is a nod to Google's
approach to openness when it comes to
AI, which we discussed in the first
module. either fully custom at scale
with various machine learning frameworks
or via something called AutoML which
automates creation and training of your
models for users with limited technical
knowledge. The fourth layer are the
models which is the core engines like
Gemini and it's important to
distinguish. Gemini the model powers
applications while Gemini the app is the
interface you interact with when you are
chatting with it in your browser. And
the last layer is the infrastructure
layer, the foundational GPUs, TPUs, and
servers. Most of it runs in the cloud,
but sometimes you'll hear about edge AI,
where compute happens locally on the
device. And a good example use case is
self-driving cars, which just can't
afford cloud latency when making split
decisions. So, navigating the AI
landscape means checking your needs and
resources first, then understanding the
five layers powering the AI landscape
from the apps that you and me use all
the way down to the infrastructure.
Quick pause. I have a favor to ask. If
you're enjoying the video so far, you
should consider becoming a part of the
small but very exclusive group of around
5% of viewers that have subscribed so
far. And if you've already subscribed, I
just want to say thank you. You're the
reason why this channel can keep growing
and keep getting better. Next is module
four, which is called transform your
work. And module four is about how to
actually work with Gen AI in practice
through better prompting, refining
outputs, and streamlining workflows.
Let's talk about prompting techniques.
And again, prompting is simply how you
talk to the model like when you use chat
GPT or Gemini. And Google highlights
three key techniques. First is to assign
a role. So give the model a persona. For
example, act as a lawyer or act as a
sales coach. This changes its tone,
style, and focus. The second technique
is something called prompt chaining.
Don't expect a perfect answer in one
prompt. Instead, treat it like a back
and forth conversation, refining the
outputs in a step-by-step manner. And
the next technique is something called
zero, one, or few shoting. In the world
of AI, the word shot refers to the
number of examples that you provide in
your prompt. So, for example, zero shot
means no example, and that's great for
simple tasks. One shot means that you
provide one example, which is great if
you want to give your model a bit of
context. And lastly, few shot means
multiple examples. And this is great for
complex tasks. So role assignment,
prompt chaining, and shot selection.
Those are essentially your three levers.
Next in module four is model guidance
and refinement. And the key concept here
is something called grounding. It
essentially means reducing a model's
hallucination by connecting the AI to
real verifiable sources of information.
And the most common method of grounding
is something called rag which is short
for retrieval augmented generation. Step
one is retrieve where the model
retrieves relevant information from
external sources. And step two is
augment where the retrieved information
is incorporated into the prompt of the
large language model. And step three is
generate where the LLM processes the
prompt and generates a response. And the
last topic for module four is that
Google recommends ways to make prompting
more efficient and repeatable. In
summary, it comes down to three things.
First is reusing prompts. Store your
best prompts as templates. Second is
used save info in Gemini. You can store
context in the model so you can recall
it consistently. And the third is to
explore gems. This is essentially a
personalized AI assistant inside Gemini
that bundles templates, instructions,
and guided interactions into one
workflow. So module 4 is all about
control, prompting well, grounding your
outputs, and streamlining your workflows
so that AI becomes a reliable teammate
instead of just a novelty. And with
that, let's shift into the last module
of the course called transform your
organization. And module five goes one
level deeper on agents, reasoning,
tooling, and customer engagements.
Starting off on the agents, Google
categorizes them into two main types.
You have deterministic agents that are
traditional rule-based systems that
follow a strict predefined script. They
are predictable and designed for
specific tasks with a limited set of
actions but lack flexibility to handle
unexpected inputs. Think of simple chat
bots like only respond to commands like
check order status. And the second type
is generative AI and they are built on
large language models. These agents use
natural language and can reason, learn
and adapt on the fly. Their behavior is
not hard-coded. Instead, they generate
responses dynamically, leading to a more
conversational and adaptive style of
interaction. Think of an AI assistant
that can brainstorm ideas or write
creative stories. And the key
distinction is that deterministic agents
follow a rigid script while generative
agents reason and responds dynamically.
And the enabler of that flexibility in
generative agents is through something
called a reasoning loop. And the
reasoning loop is how it thinks through
a problem to find the solution. It's all
about using different thinking styles to
get to the right answer. And Google
highlights three key styles. The first
one is called react, short for reason
and act. Think of react as the agent who
reasons out their next move before
taking action. For example, if you ask
an agent to find a good restaurant, it
first reasons, I need to find a place
that is highly rated and nearby. And
then it acts by using a search tool to
find one. This loop of reasoning and
acting helps it tackle really complex
questions. And the second thinking style
is something called chain of thought.
Think of an agent who is thinking out
its thought process step by step instead
of just jumping straight to the final
answer. The agent breaks down a larger
problem into smaller logical steps. For
example, solve a tricky math problem and
the agent would first show how it adds
the numbers and then how it subtracts it
in the next line etc. This approach make
the reasoning visible and more accurate.
And the last thinking style is something
called metarrompting. Very advanced
word. This is the equivalent of an agent
who tells a junior agent how to do their
job. And it's using one prompt to guide
the AI to create, change, or understand
other prompts. It's a powerful technique
for fine-tuning the AI's behavior and to
make sure that it follows a specific
instruction more precisely. Now, in
order for an AI to have the ability to
act, it needs access to tools, bringing
us into the next key concept within the
module, which is tooling for agents. And
Google boils them down into four
categories. First, you have extensions.
And an extension could, for example,
connect the agent to a live weather API
to get the current forecast. Second is
function. And a function allows the
agent to execute a specific action like
sending a confirmation text. Third is
data stores. And a data store provides
the agent with access to a company's
product catalog for example to answer
customer questions. And lastly, you have
the plugins. And a plug-in gives the
agent a new capability such as
generating an image from a text
description. And together they all make
agents not just conversational but
actually useful in real workflows. And
the last piece outlines one of Google's
core offerings that applies all of this
in practice and that is the customer
engagement suite. This suite provides
tools to help a company effectively
engage with its customer and can be
built directly on top of Google's
contact center as a service. And it
really has three main features. The
first is conversational agents. Those
are AI chat bots that acts as firstline
support for your customers. The second
is agent assist which is a feature to
support your live human agents during
customer interactions. And the last
piece is something called conversational
insights which at its core provides
analytics on your customer communication
to help you draw deeper insights. So
module five shows how agents go from
simple scripts to adaptive systems with
reasoning tools integration and how
Google is packaging this into an
enterprise solution. And that is the
8hour course summarized for you in a few
minutes. Now if you're planning to take
the certification exam, you will want to
have a plan in place. I'll tell you what
I did to pass and what I would do
differently if I were to do it again.
The exam was not easy. I wouldn't say it
was super difficult. I probably put it
in the moderate difficulty level, but
you will definitely need to get
prepared. So, I'll give you a three-step
approach. Step one is to skim through
the official course plus the study guide
and flag the areas where you feel less
confident. For me, that was definitely
Google's own offerings. Things like
Verdict AI and Agent Space, which I
honestly didn't have that much exposure
to before. So, I had to spend quite a
bit of time familiarizing myself with
them. I'll leave a link to the course
and the study guide in the description,
and then I'll also pin it in the chat.
Step two is to do the tests in each
course module. That will help you lock
in the fundamentals. From there, you can
move over to Google's official mock test
to get a feel for the full exam. And
I'll leave a link for it in the
description and in the chat as well. And
step three is to build the mileage. So
Google's module tests and mock exams
alone won't be enough. You will need
additional practice content. So find
online tests to practice. I use
something called Skillert Pro, which
costs around 20 bucks for a bunch of
practice tests. I'm not affiliated. In
fact, I have never heard about them
before. It's just a path I took. So, in
case you want to do the same, I'll leave
the link for that in the description as
well. All right, let's shift gear into
some general good to knows about the
exam. So, the exam itself is 90 minutes
long with around 40 to 60 scenario-based
questions. That means that they will
come in the format of something like you
are working for a pharmaceutical company
that deployed AI agents to summarize
client data. Analysts say that summaries
are inaccurate. What do you do about it?
And from there, you get to choose from
four options. Here's my top tip.
Don't just go skimming down into the
answers for which option is most likely
to be true. Instead, try to imagine that
it's your boss or your customer asking
the same question. Think about what you
would default into being the right
answer. That way of approaching it is
much more effective than skimming for
the most probable test answers. Because
what I can tell you is when you do the
mock exam or the module exams, the
options that is going to be handed to
you are actually super obvious. you
could probably just keep going through
the course and you'll probably strike
the right one anyways. When you're doing
the real test, the answer is not going
to be blatantly staring in your face.
So, you need to be really prepared to
figure out which of the potential
options is the right one. In my case, I
did the course and then I spent an
additional 2 three hours on the mock tests.
tests.
Um, I passed, but honestly,
I was not feeling very confident about
myself going through the test, and I
would not recommend doing so little. If
you put in the time and follow the steps
that I outlined earlier, I'm sure that
you're going to pull off a home run on
the exam. And if you enjoyed this
episode, hit like and let me know in the
comments if you'd like to see more
summaries like this. And if not, let me
know that, too. this channel is for you.
So, your feedback really matters. And as
always, thank you for trusting me with
your time. And I'll see you in the next one.
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