You type a simple question into the chat window.
It just spits back a generic robotic wall of
text. Yeah, and it's practically unusable for
your actual daily tasks. So you tweak the prompt,
you try it again. But the result is exactly the
same frustrating garbage. Welcome to the deep
dive. That is a deeply relatable frustration
for all of us. It really is. Today, our mission
is unpacking a very popular mindset. People constantly
tell you to learn prompt engineering. Right.
They say to study frameworks and memorize these
arbitrary rules. We're going to explore why that
is pointless. It is honestly a massive waste
of your time. A total waste. Instead, we're outlining
a totally different approach today. We will build
a custom prompt optimizer from scratch. You can
build it using totally free Google tools. And
it really only takes about 10 minutes to set
up. I have a vulnerable admission to make right
here. I still wrestle with getting generic robotic
answers myself. Oh, yeah. Yeah, I just throw
my messy thoughts at the screen. I expect the
machine to magically understand my intent. But
it almost never actually does what you want.
Well, you're definitely not alone in that specific
struggle. It was a very common friction point
for everyone. So our roadmap for the discussion
today is clear. We're going to explore why AI
constantly guesses. Then we will build this optimizer
in two steps. Finally, we'll plug it into your
daily workflows. Let's start with that guessing
game you just mentioned. Before we can fix the
AI, we must understand it. We need to know why
it gives us terrible answers. Especially when
we just type naturally, you know. Right. The
core issue is that AI isn't your friend. Think
about talking to a very close personal friend.
Okay. You might say you want to lose some weight,
but you also admit you absolutely hate the gym.
Right. A good friend knows your personality very
well. They might suggest swimming or... taking
a long walk. Exactly. They tailor the advice
to fit your specific lifestyle. But the AI does
not know your daily habits. It doesn't know what
you actually like doing, so it just tries to
guess what you want. You throw a messy idea at
the chat window. And it gives you a boring 10
-page workout plan. It's a rigid plan you're
never going to use. It is incredibly frustrating
to deal with that output. The source material
introduces a brilliant core analogy here. Yeah,
I love this part. It calls the prompt optimizer
a cleaning machine. It's essentially a cleaning
machine for your brain. I really love that specific
framing for this tool. Instead of stressing to
write a mathematically perfect instruction, you
just throw in your messy half -formed human thoughts.
The system automatically cleans them up for you.
It turns a simple sentence into a highly structured
prompt. It fills in the missing pieces of your
logic. Right. And the AI then knows exactly what
you actually need. It stops making wild guesses
about your underlying intent. Yeah. Beat. But
that brings us to the dreaded fluff problem.
Oh, the fluff problem is absolutely everywhere
in AI today. The model thinks talking a lot means
being helpful. It adds these incredibly long,
useless introductory paragraphs. It gives you
extra advice you never even asked for. It equates
word count with actual value and intelligence.
It really does operate on that exact flawed logic.
When you give a short prompt, you leave empty
space. And the AI desperately has to fill those
empty gaps. It starts guessing what your true
goals might be. Let me ask you this. Why does
the AI always generate useless fluff when I ask
a short question? Well, it tries filling the
empty space by guessing your goals. It just plays
it safe. It assumes high word counts mean it's
being helpful. Right. It talks too much because
it thinks length equals value. Exactly. The optimizer
acts like a professional editor sitting beside
you. OK. It finds the structural holes in your
messy story. It fills them in automatically before
the AI answers. You avoid doing any of the heavy
lifting yourself. Two sec silence. So we know
the AI desperately needs an editor. Definitely.
Needs a rigid framework to stop the endless guessing.
Let's walk through exactly how to build that
editor. You just need a completely free Google
account. OK. The setup is way easier than it
might sound. You don't need to be a complex tech
wizard. Good to know. You need Notebook LM. Gemini,
and two simple text files. That is your entire
toolkit for this entire build. Just to be clear
on the technical requirements here, we're just
using basic text files for this setup. That's
it. Let's walk through the contents of file one.
This file contains your core system instructions,
right? Yeah. This is where you establish the
permanent ground rules. You open a blank document
on your computer screen. You tell the system
exactly how it should behave. You tell the AI
it's a world -class prompt engineer. That is
the exact phrase to use right there. You tell
it to read the user's messy input. Then you ask
it to create a JSON schema. Which is just a very
organized list that keeps the AI boxed in. That
is a perfect way to describe the mechanism. It
forces the AI to answer specific targeted points.
It stops the endless wandering through random
topics completely. It cuts out the useless conversational
fluff right away. File 1 also asks for a brief
thinking guide. We'll dig into the mechanics
of that shortly. You keep the overall tone professional,
clear, and helpful. Everything outputs neatly
inside a single formatted code block. You save
that text file to your local computer. Name it
prompt -optimizer -system -instructions for clarity.
Next, we move on to building file number two.
This is your very important example pairs file.
Ground rules are great for setting the baseline
behavior, but the AI also needs to see tangible,
good examples. This second file might actually
be even more important. It shows the AI the exact
style you want. It operates in a very simple
before and after logic. Let's look at a specific
example from our source. The before state is
a messy, raw user thought. Like you might just
type, I want to learn to code. Right. That is
a very broad, messy, and unstructured goal. The
after state is what the optimizer builds. It
becomes a highly structured, detailed JSON plan.
It specifies learning Python specifically for
an absolute beginner. It outlines a manageable
weekly plan for the user. It also adds that crucial
thinking guide we mentioned. It tells the AI
to focus on underlying logic first. It explicitly
says to avoid using complex technical jargon.
It provides small daily wins for ongoing user
motivation. You save that second file to your
computer too. Name it prompt. Optimizer -example
pairs to stay organized. Now you have your two
core structural files ready. You open a brand
new notebook in Google's Notebook LM. You upload
both of these text files as sources. Then you
do something very important for the system. You
click the Convoke to Source button for both files.
This tells the system these files are the absolute
truth. It must follow them without any deviation
at all. Whoa! Imagine turning a blank text file
into a professional engineering brain in seconds.
It really feels like unlocking a weird new superpower.
Totally. The instructions mention a chain of
thought process. What exactly is that doing behind
the scenes? It acts as a mandatory guide for
the model. It forces the AI to explicitly plan
its logic. Oh, okay. It makes it think through
the problem step by step. It does this before
it starts writing the final answer. Got it. It
gives the AI a clear thinking path to follow.
Right. And that eliminates the guessing game
completely. The AI has a concrete plan before
it ever speaks. Two -sec silence. We have built
the foundational brain in Notebook LM. Before
we connect it to our chat window to see the magic
happen, let's take a quick break. Sponsor. And
we are back. We just built our custom prompt
optimizer in Notebook LM. Now we need to connect
it to our daily workflow. This is where the real
practical magic kicks in. Linking the two distinct
tools is actually quite simple. It really only
takes a few very quick clicks. First, you open
the standard Gemini website interface. You start
a brand new chat session right there. Look closely
at the prompt box at the bottom. You will see
a little plus button sitting there. You click
that plus button to open the menu. A list of
integration options appears on your screen. You
select Notebook LM as your primary data source.
Then you choose the specific notebook you just
built. You confirm the source is active in your
chat window. From that exact second on, Geminite
checks your instruction files. It checks them
every single time you send a message. You do
not have to keep uploading the text files. You
never have to re -explain the basic ground rules.
The notebook stays permanently connected to the
chat session. By attaching that notebook as the
primary source document. You're giving Gemini
a rigid set of boundary conditions. It cannot
just pull from its vast, messy training data.
It has to filter everything through the brain
you built. That brings us directly to the two
-step workflow rule. This is the core workflow
for your daily tasks. Step one. Type a messy
idea into your new notebook LM. This quickly
gives you the highly structured smart prompt.
Step two is just as simple as the first. You
copy and paste that smart prompt into Gemini.
And Gemini then gets the actual real work done
perfectly. Let's look at some real -world examples
from the source material. These show exactly
how powerful this two -step workflow is. Let's
start with a very relatable daily fitness example.
I think everyone can relate to this specific
scenario. You are completely exhausted after
a long day of work. Your daily schedule is an
absolute total disaster right now. You do not
have the energy to write perfectly. You don't
need a mathematically perfect instruction here.
You just type out your messy, chaotic daily reality.
You type, I am tired and want to be fit, but
I have zero time and I absolutely hate running.
Without the optimizer, a standard AI just says,
try harder. It gives you a generic 5K marathon
training plan. But with the optimizer, it builds
a tailored structure. It creates a detailed JSON
list just for you. It understands the actual
underlying problem you are facing. It specifies
15 minutes of gentle, low -impact exercise. The
thinking guide tells the AI to focus on consistency.
It strictly deprioritizes intense exhausting
daily workouts completely. You just copy that
professional clean code block output. You paste
it directly into your Gemini chat window. You
get a totally realistic 15 minute daily workout.
It fits your messy chaotic life absolutely perfectly.
Let's look at a completely different technical
coding example. Let's say you want to learn Tailwind
CSS programming. If you just ask AI to teach
you, it fails. The topic is way too broad for
a simple prompt. The standard AI answer will
be incredibly confusing. It will overwhelm you
with advanced technical details immediately.
But you put that messy idea into Notebook LM
first. The system realizes you need a clear step
-by -step path. It actively stops you from feeling
totally overwhelmed right away. It systematically
organizes the topic into core foundation. concepts.
It creates small, manageable daily coding challenges
for you. The thinking guide forces the AI to
explain the underlying why. Like why use Tailwind
CSS instead of older CSS files. You take that
beautifully optimized prompt from the screen.
You paste it into your preferred AI chat tool.
A clear, professional step -by -step lesson plan
appears immediately. The final practical example
is about growing a business. Growing a weekly
newsletter or getting more Twitter followers?
Do not just ask the AI for a generic growth plan.
You type your raw, unpolished goal into Notebook
LM directly. The optimizer acts exactly like
a professional marketing consultant. It generates
a strategy tailored for your specific brand niche.
It builds a rigid content structure for your
specific niche. It clearly defines your core
daily content pillars. It maps out a realistic
daily social engagement strategy. The underlying
thinking guide is absolutely crucial right here.
It tells the AI to focus on providing real value.
It strictly avoids annoying your followers with
constant ads. You copy the resulting code block
from the optimizer. You paste it back into your
main Gemini window. You get a brilliant 30 -day
content calendar with great headlines. The real
secret is knowing which tool to use when. Notebook
LM is just the filter that cleans your messy
thoughts. Gemini is the engine that does the
actual heavy lifting. It is exactly like having
a dedicated professional coach. It stops embarrassing
mistakes before you even start typing. So I generate
this smart prompt using Notebook LM. Am I locked
into only using Gemini for the final result?
Not at all. You can copy that optimized structure.
You can paste it right into ChatGPT, Claude,
or any other model you prefer. So the cleaned
up prompt works perfectly in ChatGPT or Claude
too. Absolutely. The prompt structure is already
thoroughly cleaned up. It will work beautifully
on literally any modern AI model. Two -sec silence.
This basic workflow clearly saves hours of daily
frustration. Now let's explore leveling up this
entire system. We can tweak it to fit perfectly
with highly unique tasks. Small incremental changes
make a truly massive difference here. You do
not have to rebuild everything from scratch,
ever. You just push a few buttons to alter the
overall vibe. Let's discuss changing the AI's
underlying conversational tone. You can easily
change the personality of your custom optimizer.
You just swap out one single line in the core
instructions. You might want a patient and clear
teacher for learning. That tone is great for
grasping new, complex topics. But for daily business
tasks, that tone might feel too soft. Right.
For serious business, you want something much
sharper. You chained it to a direct and data
-driven consultant. You want crisp, highly actionable
insights without the fluff. For purely creative
projects, you try an inspiring and imaginative
coach. That one single line completely shifts
the semantic space. It changes how the AI responds
to your messy thoughts. The source material also
introduces gems inside of Gemini. If you use
Gemini constantly, attaching the notebook gets
tedious. Doing it every single time causes unnecessary
daily friction. The gems feature fixes that annoying
repetitive hassle completely. Think of a gem
as a little dedicated shortcut app. It lives
permanently right there in your left sidebar.
It saves you so many unnecessary manual clicks
daily. You don't have to attach the notebook
every single time. You just click your custom
optimizer gem and start working. You start typing
your messy idea into the box immediately. It
keeps everything you need perfectly organized
in one place. It is a massive time saver for
writing emails or planning dinner. You can also
make the AI significantly smarter over time.
You do this by adding more example pairs to your
logic file. This is where the real personalization
actually starts happening. The more before and
after stories you show the system. The smarter
and more refined its outputs eventually become.
It gradually learns your highly specific personal
preferences and quirks. It is exactly like teaching
a friend how you like your coffee. Once they
see you make it a few times, they stop asking.
They just intuitively know exactly what you want.
If the AI consistently gives you way too much
text, you just add very short punchy examples
to the file. If it misses crucial details, show
it a JSON with many fields. Every single new
pair makes the system feel more like you. This
system works incredibly beautifully for technical
coding as well. It makes absolutely sure you
include important technical details, things like
web accessibility, proper meta tags, and clean
structure. It actively prevents you from getting
sloppy buggy code back. Even if you think your
prompt is already pretty good, you should still
run it through the optimizer system anyway. The
source notes it improves good prompts by 20 to
30 percent. It consistently finds a reliable
way to add a clearer thinking path. It actively
catches the small technical details we naturally
forget. What if I want to type my messy ideas
in a language other than English? Will this system
still work? Yes. Gemini is excellent at translating,
so you can type in your native language and the
system will still automatically optimize the
logic. It takes your native language and still
optimizes the prompt perfectly. It seamlessly
handles the complex translation and the logic
cleanup simultaneously. It is an incredibly robust
system for international users. Beat! We have
covered a massive amount of ground today. Let's
zoom out and recap the really big overarching
idea. The biggest takeaway is shifting your fundamental
mental framework. You do not need to become a
professional prompt engineer. You do not need
to memorize complex, rigid conversational frameworks.
Your instruction text files are the absolute
undeniable truth. Notebook LM is simply the filter
for your messy human thoughts. Gemini simply
takes the required action. based on that clean
logic. You just need 10 uninterrupted minutes
to set up the brain. Then you just let the automated
filter do its daily job. It completely changes
the dynamic of how you work. You stop fighting
the machine to get what you want. You start actually
collaborating with it on a structural level.
I strongly encourage everyone listening to try
this out. Do not wait for a massive, overwhelming
project to begin. Start with something very small
and incredibly low stakes today. Plan a simple
dinner menu for the upcoming week. Plan a very
simple weekend trip to a nearby city. Type in
your messiest, least organized thoughts about
the topic. Just sit back and watch how the system
cleans it up. See the massive difference in the
final structured output. Once you see the power,
you're never going to go back to guessing. It
really is a fundamental paradigm shift in daily
computing. I want to leave you with one final
thought to mull over today. If two free tools
can take our messiest half -formed thoughts and
translate them perfectly into structured machine
logic, how long until we stop typing instructions
altogether and just let AI listen to our messy
conversations to run our lives for us?