This content provides a practical guide to leveraging various AI tools by categorizing them and highlighting their unique strengths, enabling users to select the most effective tool for specific tasks and avoid months of trial and error.
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I use around 10 AI tools for 90% of my
work, [music] and each one excels in one
specific area. But figuring out which
tool works best for what task usually
takes months of trial and error. So,
I'll share the one thing each tool does
better than alternatives, so you walk
away with a clear mental model for when
to use what. I've grouped these tools
into four categories across a two-part
series. There's just too much to cover.
This video covers everyday and
specialist AI, while part two covers the
remaining two categories. Let's get
started. Kicking things off with
everyday AI. These are your general
purpose chatbots. Chachi, Gemini, and
Claude. And while they seem
interchangeable, their quote unquote
moes, the specific things they do best
have actually become quite distinct.
Starting with the OG Chachet. While
Gemini and Claude are arguably just as
capable in raw power, Chachib still
holds the crown in one area. It's the
most obedient model. [music] In plain
English, Chachib drops fewer balls when
you hand it a complex checklist. Other
models might be just as smart, but give
them a lengthy set of instructions, and
they'll sometimes skip a step or decide
they know better. If you want proof of
this, just ask each model to optimize a
rough prompt for itself. Chacht will
generate a noticeably longer and more
detailed prompt because it knows it can
handle the complexity. And if you run
that optimized chachib prompt through
both chacht and gemini for example,
you'll notice two things. First, chachib
thinks longer because it's actually
checking every requirement and it
follows each instruction to the letter.
Gemini on the other hand often takes
shortcuts. Pro tip, I share the exact
prompt optimizer in the essential power
prompts template linked below, but you
can test this yourself with something as
simple as optimize this prompt for
Chachib insert model number here. Here's
my rough prompt. Diving into a real
world example, I gave both Chachet and
Gemini the same complex prompt, a hiring
rubric with a dozen requirements. Chachi
delivered every single one. Gemini's
output looked right at first glance, but
when I checked it against my original
list, it had quietly dropped a few
rules. That's the key difference.
Chachib doesn't decide which
instructions matter. It just follows
them. Here's a second simpler example.
Sometimes when you explicitly tell
Gemini to search the web, it just
doesn't, which is wild since Gemini and
Google search are both Google products,
right? Whereas with ChachiT, when you
enable web search, it performs the web
search every single [music] time. I know
this is a small example, but it's
downstream from Chachib's core
superpower. Obedience means you can
trust the behavior you ask for. So, as a
rule of thumb, if a task has a lot of
moving parts, and getting one wrong
breaks the whole thing, start with
Chachib. Next up, Gemini. Where ChachiT
wins on obedience, Gemini wins on
multiodality. In plain English, Gemini
is able to process a massive amount of
mixed media, video, audio, images, and
text natively. Taking a look at this
table, we see that only Gemini can
handle all four types of media natively.
It's able to quote unquote listen to
audio and quote unquote watch videos,
while Tragic and Claude use roundabout
ways to access that information. What's
more, Gemini's massive 1 million token
context window means it can handle large
video recordings, hour-long audio
recordings, full slide decks, all
together that would literally choke
other models. If you watch my latest
Gemini video, you'll remember the use
case where I screen recorded a messy
walkthrough of myself completing a task,
uploading that video onto Gemini, and
asking Gemini to turn it into a
readytouse SOP with perfect formatting,
which is an example of Gemini ingesting
video and turning it into text. Now,
let's take that a step further. Imagine
you just finished a weekly meeting. You
have a video recording of the call, a 20
slide deck, and a photo of a messy
whiteboard session. You can upload all
three and ask Gemini to summarize what
was discussed, pull out the key
decisions, and draft the follow-up
email. Gemini is the only tool that can
synthesize all three in one go. All that
said, I have to point out that Gemini's
raw reasoning capabilities sometimes
feels slightly behind CatchBT. But when
the task involves video, audio, or
massive files, the trade-off is
obviously worth it. Speaking of matching
the right tool to the task, today's
sponsor HubSpot put together a free
guide called the AI productivity stack
that covers 50 tools organized by use
case. Here's why I like it. While this
video focuses on my personal favorites,
your workflow probably needs something
different. Maybe you're in marketing and
need SEO specific tools or you manage a
team and want to build automated
workflows with reliable AI. This guide
breaks down tools across business
functions like research, design, and
marketing. And for each tool, it shows
you the best use case, key features,
pricing, and a step-by-step workflow.
What I found most useful is the decision
logic at the end of each section. So,
for example, the research category tells
you exactly when to use Perplexity
versus Claude versus Humatada based on
what you're actually trying to do. It's
a great way to quickly understand what
each tool does. [music] Well, I'll leave
a link to this free guide down below.
Thank you, HubSpot, for sponsoring this
video. Rounding out the everyday AI
category, Claude. Claude superpower is
producing higher quality first drafts
than the other models. In plain English,
that means Claude's first attempt is
usually closer to done. This superpower
shows up in two areas. First, coding.
Here's a fun fact. The latest version of
Gemini beat the older version of Claude
in every single benchmark score except
for the coding one, which is crazy. So
obviously Anthropic has figured out
something related to coding the others
haven't. And in practice, developers
universally agree that Claude writes
functional code on the first try more
consistently than alternatives. Here's a
real world example. I needed to bulk
export conversations from a customer
service platform, but their support team
said only developers could do it. I
described the problem and Claude not
only gave me step-by-step instructions
but also wrote a script in Go that
worked on the first try. I don't even
know what Go is nor can I write code.
Another example, I asked all three
models to turn a static image into an
interactive chart and Claude performed
the best on the first try. So basically,
anything that requires generating
working code tends to favor Claude. Pro
tip, when it comes to diagrams, you can
ask Claw to generate mermaid code, which
you can then paste directly into tools
like Excaliraw to get clean visuals in
minutes. Area two, polishing copy.
Beyond code, Claude produces written
drafts that sound human and need fewer
revisions. When you need to tighten an
argument or match a specific voice,
Claude just gets it. Put simply, it's
exceptionally good at style matching.
Once you share examples of your existing
work, it replicates your tone almost
perfectly. When I was in corporate, I'd
shared previous documents so Claude
could replicate that voice across
presentations and performance reviews.
And now, as a creator, I feed it my
existing YouTube scripts to help refine
new drafts. At this point, you might be
wondering how I use all three everyday
AI tools together. In a nutshell,
Chachip or Gemini usually handles the
beginning of my work, ideation,
research, drafting the outline of a
presentation. Claude then handles the
last mile, turning that rough output
into something I'm ready to present or
publish. Quick note on Grock. A lot of
people ask why I don't use it. It's
actually very simple. Uh Grock's
superpower is its direct access to the
Twitter/x fire hose, right? So it's the
best option for people who need to
analyze breaking news events in real
time. I never needed that. And as a rule
of thumb, we should never use tools just
for the sake of using tools. We should
only add them to our toolkit when they
solve an actual problem we have. Here's
a quick recap of the three models and
when to use them. And if you're
wondering whether you need all three,
the short answer is no. Most people
should stick with the paid version of
ChachiBT and get really good at it. But
if you can afford multiple subscriptions
and your workflow can take advantage of
their individual superpowers, mix and
match as needed. Fun fact, according to
this study on open router data, models
from different labs like Chadypt and
Gemini expand the pie of AI use cases
precisely because they excel at
different things. Onto the second
category, specialist AI. Before diving
in, let's clear up a very common
misconception. Tools like Perplexity are
not foundational models. Here's a simple
visual. OpenAI, a Frontier AI lab,
develops the GPT family of models. They
also created ChatGpt as the userfriendly app
app >> [music]
>> [music]
>> layer. Perplexity is different. It
fine-tunes existing foundational models
for speed and accuracy and is optimized
for search. Their own sonar model, for
example, is just a fine-tuned version of
Meta's openweight llama model. So, on
that note, Perplexity superpower is
finding accurate information fast. In
plain English, the general purpose
chatpots are built for reasoning. You
use them to help you think, brainstorm,
or write a draft. Perplexity is built
for fetching. You need a specific fact,
and you need it now. Starting off with a
simple real life example, I used chachib
to plan a trip to Japan with my brother
because that is a creative task. It
requires weighing trade-offs, building a
narrative, and for that kind of task,
I'm happy to wait while the model
thinks. But when I need grab-and-go
information, like whether a specific
restaurant is foreigner friendly because
we don't speak Japanese, I'd want
Perplexity to give me accurate and
update information within seconds.
Second example, going back to how I use
the three everyday AI tools, let's say
Gemini or Chachet helps me brainstorm
and structure my newsletter. Claude
produces the final draft. Perplexity in
this case is the search scalpel that
verifies information like whether
Gemini's contact window is 1 million or
2 million tokens. In case you're
curious, consumers get 1 million,
enterprises get 2 million. Pro tip, you
can use Google style search operators
like [music] site colon reddit.com to
narrow your results to a specific
source. [music] I have an entire video
on the most useful Google search
operators, so I'll link that down below.
As a rule of thumb, think of perplexity
as a replacement for Google AI mode.
They're both for fetching information
and not as a replacement for general
purpose chatbots. Actually, let me know
if you want an entire video breaking
down the AI search apps like Perplexity,
Google Search, Google AI overviews,
Google AI mode, because they're all made
for different things. Rounding out
Specialist AI, Notebook LM superpower is
that it only answers from the sources
you give it, meaning it won't make
things up. Think of it like a walled
garden. You upload your sources and
Notebook LM answers questions using only
those documents. It can't really
hallucinate because it has no outside
knowledge to draw from. Going back to
the visual around how perplexity is
optimized for search, Notebook LM uses a
fine-tuned Google Gemini model that
minimizes hallucinations. For instance,
when I was at Google before publishing
marketing materials, I would upload the
final draft alongside the source
documents and ask Notebook LM if the
draft made any claims that contradicted
the sources and it would catch these
tiny discrepancies other AI might have
missed. I use a similar workflow today
for my videos. Before I start filming, I
upload my script and all my research
into Notebook LM and ask it to flag
anything not directly supported by the
source material. The obvious caveat here
is that the output is only as good as
the sources we give it. So if the
sources are incorrect, Notebook LM is
going to be confidently incorrect. So as
a rule of thumb, if the accuracy matters
more than creativity and you have source
materials to check against, use Notebook
LM. There are a few more specialist AI
tools I use but didn't make this list
because I don't use them every day. But
to quickly go through them, Gamma for
presentations, 11 Labs for voice
cloning, Zapier and N for automation,
and Excaliraw and Napkin AI for quick
visuals. As a reminder, I'll cover the
remaining two categories in part two, so
keep an eye out for that. See you on the
next video. In the meantime, have a
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