This content outlines a three-level progression for professionals to effectively integrate AI into their workflows, moving from curiosity to literacy and ultimately to an "AI native" state where AI is a collaborative partner in redesigned processes. It provides actionable strategies to achieve this advanced level of AI integration.
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Working with AI comes in roughly three
levels. First, we have people who are AI
curious. This group relies on the free
tier of AI tools and only uses chatbots
when someone reminds them to or when
they're stuck. Level two, we have the AI
literate. These people pay for AI,
maintain a prompts database, and they
know when to use which AI feature
[music] and model. Level three is AI
native. And in a nutshell, these people
have redesigned their workflows assuming
an AI collaborator exists. Most
professionals are stuck at level two.
So, in this video, I'll share the
specific strategies ordered from simple
to advanced that will get you to level
three. Let's get started. Kicking things
off with the easiest habit to adopt,
leave AI breadcrumbs. What this means is
instead of treating AI chats as
disposable one-off threads that become
almost impossible to find again, you
want to create a hyperlink to the
conversation and paste it directly into
the document where you're actually using
the output. I know this habit sounds
silly and insignificant, but it works
great thanks to the core productivity
principle of always organizing your
information by where you will use it,
not where you found it. Diving into a
real world example, here's the actual
Google Doc I used to prepare for a
recent work presentation. The final
outline tab has all my content and the
helpful hints tab has hyperlinks to my
AI conversations. Taking a step back,
let's say I'm building this presentation
from scratch. I would first ask AI to
rewrite my initial rough prompt so that
it's optimized for the model that I'm
using. And once I press enter, you'll
notice the URL transforms into a unique
link. Right? And this is where I'd press
command or control L to select the
entire URL, copy, come back to the
Google doc, type out Gemini, command or
[music] control K to hyperlink, and
paste that link. I then copy the
optimize prompt and paste that into a
new chat. Make adjustments as needed.
then go back and forth with the AI to
brainstorm and refine my presentation
outline. And of course, I save this new
chat link in the Google doc as well, so
I can easily pick up where I left off a
day or even a week later. Pro tip, add
context next to each hyperlink so you
remember why it matters. For example,
here, this Gemini conversation was
helping me brainstorm my outline. This
one was around applying storytelling
principles to that final outline. And I
use Claw to refine my final talking
points. And just to be clear, I like to
test multiple frontier models because I
do this for a living. Most people should
just pick one AI chatbot and get really
good at it. Here's another real world
example. Whenever I create a new project
page in Notion here under the op center
section, I would add links to the
corresponding CHP and claude projects so
I can jump into those AI workspaces
immediately. Put simply, leaving AI
breadcrumbs means organizing your AI
chats by work context and not by date or
chronology. This beats trying to search
for that one specific thread from days
or weeks ago. And the rule of thumb here
is simple. If the AI conversation took
more than 10 minutes or produce
something you'll reference again, anchor
it to your workspace immediately. [music]
[music]
Speaking of systems, I'm actually
building an entire course on evergreen
AI skills that teaches universal
principles for any platform. So, if you
want a framework that never goes
obsolete, click the link below to join
the weight list. Moving on to habit
number two that requires a bit more
effort, build an AI swipe file system.
In a nutshell, instead of prompting AI
with basic instructions like write a
business proposal, you provide a
specific example from your curated
library aka your swipe file and ask the
AI to first analyze what makes it so
effective. Then apply those patterns to
your new content. For instance, let's
say you work in OpenAI and you have this
brilliant idea that you know users
around the world will absolutely love
pumping chat full of ads. Instead of
starting a proposal from scratch, you
open up your AI swipe folder to find
examples of business proposals you've
previously saved. Share them with the AI
and say, "Analyze the business proposals
I've attached, list the key patterns in
structure and tone, then apply those
patterns to my content below." And you
paste your advertising product idea
designed to maximize shareholder value.
Jokes aside, I guarantee you that
initial output will [music] be stronger
than any initial draft you could have
come up with yourself, not to mention
the massive time savings. Funny story,
when I was at Google, I used this
technique for all my important
presentations by uploading slide decks
from Mackenzie Bane and BCG. And senior
leaders who came from those firms would
ask if I had also worked there before.
And I'd be like, "What? No, these
frameworks and principles come so
naturally to me. Does it not come
naturally to you?" And that's why I'm
not at Google anymore. No, just kidding.
But as you can see, this technique is so
effective because it gives the AI a
clear picture of what good looks like,
allowing Chachi or Gemini to produce
output that matches those standards
instead of generic slot. To close the
loop, the actual habit you want to
develop is whenever you encounter
excellent work in your field,
immediately save it to your swipe file
system so you can reference it the next
time you face a similar task. Pro tip:
start narrow and expand gradually. Begin
with just two to three use cases you do
repeatedly like presentations, emails,
or reports. And organize your folders by
use case, not by source or date. By the
way, this is also the first step in
making your Google Drive AI ready, which
is something I dive deeper into in the
course I just mentioned. And number
three, we have AI first task planning.
Heads up, this habit is probably the
hardest to maintain consistently, but I
[snorts] promise you, just like going to
the gym, it will make a massive impact
over the long run. Put simply, this
habit involves planning your AI use
before you start a big piece of work.
This means breaking down complex
projects into small concrete tasks, then
marking the ones AI can and should help
with. Diving right into real world
example, I used to be responsible for
sending these uh weekly newsletters to
our Google Ads customers uh with the
goal of driving adoption of new product
features, aka getting them to spend more
money. Basically, before writing
anything, I'd break down the work into
steps and microtasks. Then I decide
whether to do each microtask manually or
use AI. If I use AI, I specify the exact
tool that's best suited for that task.
In this case, there are three main
steps. Step one is to clarify the goal
and audience. Step two is to draft the
newsletter. And step three is to refine
the copy for Google's brand voice. [music]
[music]
Now, step 1.1 would be to brain dump key
information like what the new feature
is, the benefits, who should use it, and
so on. This task is manual because there
are details I know that AI doesn't have
access to, and I also want to inject my
point of view. Step 1.2 is to fact check
my notes from the previous step. Here,
it makes sense to use AI, specifically
Notebook LM, since it has the lowest
hallucination rates. And so I upload my
brain dump and source documents onto a
notebook to verify rollout dates,
feature names, policy details, etc. Step
1.3 is to turn those fact check notes
into a structured brief. This is
obviously also a perfect task for AI,
but this time I use the standalone
Gemini app instead of notebook LM
because the Gemini app is much better at
creative writing. In the interest of
time, I'm going to skip over steps two
and three, but the process is exactly
the same. List out all the microtasks.
decide if AI should help and if yes,
pick the right tool for that specific
job. At this point, the benefits should
be pretty clear. First, you cut decision
fatigue and context switching because AI
usage is already preddecided at the task
level. Second, you increase quality and
speed by matching the right AI tool to
the right kind of work instead of
forcing one tool to do everything. And
the rule of thumb here is for any
project that will take more than an
hour, spend 5 to 10 minutes mapping the
steps and tagging which ones are AI or
manual. For the productivity nerds out
there, this is a classic example of
sharpening the axe, where spending a few
minutes on planning up front saves hours
[music] of work later. Pro tip, create
templates for recording workflows, like
what I have here for my weekly
newsletter, so that next time you can
focus on executing instead of having to
plan from scratch. This brings us to a
bonus habit that ties everything
together. Maintain a prompts database.
I've talked about this many times
before. Whenever you write a prompt that
works well, save it to a central library
organized by use case so you can reuse
that prompt whenever you face that task
again. The worst feeling is writing a
perfect prompt 3 weeks ago that
generated a perfect output. But today
you can't find it. So you try to rewrite
it from memory and the result is just
eh, which is also my girlfriend's
reaction shortly after we started
dating. Huh. A lot of you have been
asking for my go-to prompts. So, I spent
quite a bit of time putting together a
set of essential prompts that everyone
can benefit from because I genuinely
believe you don't need a thousand random
prompts. You need 10 to 15 battle tested
ones that you can use [music] every day.
I've been using and refining these ever
since Chachi PT first launched. So, if
you want to skip the trial and error,
I'll leave a link to this down below.
See you on the next video. In the
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