Kind: captions
Language: en
I just finished Google's six-hour prompt
engineering course, and it is hands down
the best AI training I have ever taken.
So, I'm saving you the time, and I've
packed every essential tactic Google
revealed into this guide, so you can
master the entire system in under 12
minutes. Of course, I can't fit every
lesson from this 9-hour course into one
video. So, if you want the complete
training and an official certificate
from Google, you can actually put on
your resume or LinkedIn. I'll leave a
link to Google's course in the
description. It starts with
understanding how the model actually
thinks. So Google structures their
entire course around five core
principles. Task, context, references,
evaluate, and iterate. The foundation of
everything is task. This is simply what
you want the AI to do. Not the general
topic, but the exact output you need. A
bad task is help me with email. While a
good task is reformatting the sentence
to write an email to my gym staff about
a schedule change. But here's where
Google takes it further. You can add two
elements to make the task even stronger.
First is persona. This is about setting
the lens. When you tell the AI to act as
an expert, you aren't just playing
pretend. You are priming the model to
access a specific set of vocabulary and
logic. Asking for a workout plan is
fine, but adding the instruction like
act as a physical therapist ensures you
get safety tips and anatomical focus
rather than just a list of exercises. It
changes the entire vibe of the answer.
Second is format, and this is your
biggest timesaver. If you don't define
the format, the AI defaults to a generic
wall of text. By asking for a bulleted
list, a markdown table, or even a JSON
snippet, you force the AI to organize
its thinking. You stop getting raw
information that you have to fix later,
and start getting usable deliverables
that are ready to go. These two
additions turn a basic task into a
specific structured result instead of
random noise. Once the task is clear,
you need to layer in context. Context is
the background data that steers the
model. The rule here is absolute. The
more information you provide, the less
the AI has to guess. Let's look at an
example. You need a landing page copy.
If you just type write landing page copy
for my website, you get generic text
that could apply to anything. But look
at what happens when you inject context.
I'm building a project management tool
for freelance designers. The target
users are 25 to 40 and they are
frustrated with tools like a sauna being
too complex. My product focuses on
visual timelines and client portals.
Keep the tone professional but warm. Now
the AI knows exactly who you are talking
to and the output becomes targeted
instead of generic. Google's course
emphasizes this repeatedly. Most people
skip context entirely. They assume the
AI will figure it out, but it won't.
Now, to really lock in the quality, you
add references. References are examples
that show the AI what you're aiming for.
Sometimes words aren't enough to capture
a specific vibe or structure, and that
is where examples come in. Let's say you
are writing a product description and
you need it to match your specific brand
voice. Don't just explain the tone.
Paste in three of your best descriptions
and tell the model, "Write a new
description using the same style as
these examples." Or if you are creating
social media content, feed it your top
performing posts and tell the AI to
analyze why they worked. Then have it
generate new posts that follow that
exact pattern. References turn vague
instructions into concrete targets. It
stops the model from guessing your style
and forces it to match what you already
know works. Once you get a response, you
move to evaluate. This means checking if
the output actually hits the mark. It
sounds basic, but this is where most
people fail. They skim the text, settle
for good enough, and move on. Google
teaches systematic evaluation. You need
to actively verify that the output
matches the task, hits the right tone,
and relies on accurate data. If it
doesn't, you fix it. This leads us to
the final phase, iterate. Prompting
isn't a straight line. It is a loop. You
ask, check, adjust, and ask again.
Google provides four specific ways to
fix a broken prompt. The first method is
to simply revisit the framework. Go back
to the start and check if you missed the
context or forgot to assign a persona.
Sometimes the fix is just filling in the
gaps you missed the first time. If that
doesn't work, try the second method,
breaking into simpler sentences. The AI
processes information just like a human
does. If you dump a massive paragraph of
instructions, it gets overwhelmed. Don't
write a run-on sentence about a Q1
strategy targeting Gen Z with budgets
and KPIs all in one breath. Break it
down and write something like, "Create a
strategy for Q1. Target Gen Z, include a
budget, add KPIs." Writing the same
information, but with clearer structure
results in better output. The third
tactic is to use analogous tasks. If the
direct approach fails, try a different
angle. If write a business proposal is
giving you dry, boring results, switch
the frame. Ask it to write a persuasive
argument for a partnership, you are
changing the mental model the AI uses,
which often gives a much better result.
Finally, you can add constraints.
Constraints actually force creativity.
If you ask for video ideas and get
generic results, clamp down on the
requirements, tell it must be under 90
seconds, must focus on one single tip,
must start with a question. Now, the AI
has to work within a box, which makes
the ideas specific instead of broad.
Now, beyond text, there is multimodal
prompting. This is where models like
Gemini really separate themselves from
the pack. As besides just reading text,
they can process images, audio, and
video natively. Say you are redesigning
a website and need feedback. Instead of
wasting time describing the layout in
words, just upload a screenshot. Then
command it to analyze this homepage
design. Identify three specific areas
where user attention might drop off and
suggest improvements. Or if you are a
musician working on a track, upload the
audio file and ask it to describe the
mood of this piece. Then suggest five
alternative directions I could take the
arrangement. The framework still applies
here. You are just replacing vague text
descriptions with highfidelity visual or
audio references. But we need to address
the elephant in the room. Even the best
models today suffer from two massive
structural flaws which are
hallucinations and bias. First, these
models can be confident liars. Google
calls this hallucinating. The AI invents
information that sounds authoritative
but is completely false. A common
example occurs with simple logic. If you
ask how many E are in the word
intelligence, it might tell you four
when there are actually three. It isn't
counting. It is predicting patterns and
sometimes it misses. Then there is bias.
Since these models learn from the open
internet, they absorb human prejudices.
Gender bias, racial stereotypes, and
cultural assumptions are often embedded
in the training data. Google's solution
to this is a concept called human in the
loop. You are the safety net. You are
responsible for the final output. Don't
trust the AI blindly. Verify the claims
and question every assumption. Once you
have that mindset locked in, you can
start applying these tools to actual
work. Let's look at a practical real
world application. Let's say you are a
freelance consultant. Clients always ask
the same onboarding questions. Instead
of typing the same responses manually,
you create a master prompt. For example,
I'm a freelance marketing consultant and
a new client just signed. Write an
onboarding email that covers the project
timeline, what I need from them this
week, our communication channels, and
what to expect in month one. Keep it
under 250 words, and make the tone
confident but approachable. It takes 60
seconds to write that prompt, but it
saves you 15 minutes every single time a
client signs. Google's course is full of
scenarios like this, from cold outreach
to meeting summaries. But those are
simple singlestep tasks. To really
utilize the full power of the model, you
need to use an advanced technique called
prompt chaining. This means using the
output of one prompt as the input for
the next. You build complexity layer by
layer. Suppose you are launching a
podcast for indie game developers. You
start by asking it to generate 10
potential podcast names for a show about
indie dev, targeting aspiring developers
with a playful tone. Once you pick your
top three, you feed them back in and ask
it to write a two sentence tagline for
each, explaining exactly why listeners
should care. Finally, with the winning
concept selected, you execute the big
task. Using this specific name and
tagline, create a four-week launch plan,
including an announcement strategy,
guest lineup, and outreach targets. Each
step builds on the previous one. Instead
of trying to cram a massive project into
a single request, you are guiding the AI
through a logical sequence. Now, let's
level up again. Sometimes you don't just
need a sequence of steps. You need deep
logic and that is where a chain of
thought prompting comes in. Chain of
thought asks the AI to explain its
reasoning step by step. Instead of just
getting a final answer, you make the AI
show its work. This helps you spot
flawed logic immediately. For example,
you're helping me decide between three
different pricing models for my app.
Walk me through your reasoning for each
option step by step. Consider user
psychology, revenue sustainability, and
competitor pricing. The AI doesn't just
recommend a model. It explains the why
behind every choice. You can then guide
it further if the reasoning is off. But
what if there is no single correct
answer? That is where tree of thought
prompting comes in. This technique
explores multiple reasoning paths at the
same time. It is perfect for complex
problems like creative projects or
strategic decisions. Imagine you are
designing a mobile app onboarding flow.
Instead of asking for one idea, you ask
generate three completely different
approaches. One focused on speed, one on
education, and one on personalization.
For each approach, explain the user
experience and potential dropoff points.
You aren't following one linear path.
You are exploring branches. The AI
generates multiple options and you
evaluate them together. Now we arrive at
the absolute highlight of the entire
course which is AI agents. Google
dedicates an entire comprehensive module
to this concept and for good reason. An
AI agent is a specialized persona
designed to perform specific high-v
value tasks. Google teaches you to build
two specific types that are incredibly
powerful. First is the simulation agent.
This is your practice partner. It is
designed to run live scenarios with you
like highstakes sales calls or
presentations. Let's say you're
preparing for an interview. You prompted
act as a senior hiring manager. I am
applying for a project manager role.
Interview me using behavioral questions
one at a time. Continue until I say end
session. Then give me feedback on my
answers and suggest improvements. Now
you have a live practice partner. You
respond to questions and when you're
done, type end and get actionable
feedback instantly. The second type is
the expert feedback agent. Think of this
as a personal consultant. Let's say you
are writing cold emails and want to
improve your conversion rates. You
prompt it. You are a sales expert with
15 years of experience. I'm going to
show you my cold email template.
Critique it for subject line
effectiveness, value clarity, and call
to action weakness. Be brutally honest.
The AI critiques your work, suggests
improvements, and rewrites the copy
based on expert principles. And the best
part is that Google provides a simple
blueprint to build these agents for any
task you can imagine. It starts by
assigning a persona like act as an
experienced copywriter. Then you inject
the context, telling it, I run an
e-commerce store selling sustainable
homegoods. Next, you define the
interaction, review my drafts, and point
out weak spots. Finally, you set a stop
phrase like stop when I say session
complete and tell it to summarize the
top recommendations once you hit that
specific phrase. And just like that, you
have a functional AI tool tailored
exactly to your needs. This brings us to
the final technique, which is
metaprompting. And this is the ultimate
cheat code. Basically, if you are ever
stuck, use the AI to improve your own
prompts. Ask it, how can I make this
prompt more specific? Or, what context
am I missing for better output? The AI
becomes your co-pilot. It is prompting
about prompting. And this technique
ensures you always get the best result,
even if you don't know exactly how to
ask for it. And that is it. That is the
core of Google's six-hour course
condensed into a practical guide you can
use right now. Just remember the flow.
Define the task, set the context,
provide references, then evaluate and
iterate. That specific loop is the
difference between people who complain
AI doesn't work and the ones who use it
to save hours every week. So give this a
shot the next time you open Gemini or
Chat GPT. Stop guessing with a single
sentence and start building your prompts
layer by layer. And if you want to go
deeper than this video and actually get
the Google certificate, the full course
is linked in the description. But
knowing how to prompt is only half the
battle. You also need the right tools. I
tested seven free AI tools from Google
that go way beyond just Gemini. Click
right here to watch that breakdown, and
I'll see you there.