Have you ever gotten a response back from ChatGPT
and just felt, well, a bit let down? Oh, absolutely.
You know, you ask something deep, something specific,
and what you get back is, well, it sounds OK,
but it's kind of shallow. Yeah. Or the opposite.
You need something super simple, like right now,
and it takes forever. Exactly. It feels like
maybe you didn't phrase the prompt right, but
maybe that's not it. Beat. Maybe the issue isn't
the prompt, but the tool itself. Welcome to the
deep dive. I'm your host Today we're gonna unpack
that exact feeling because you see chat GPT isn't
just one single AI. It's more like a a whole
toolkit, a team of specialists, really. And our
mission today is to help you figure out what
each specialist does best. Right. Strength, weaknesses,
and critically, when to call on each one. We
want to turn that confusion into real clarity
for you. And we're absolutely going to do that.
It's like, think about a real team. You wouldn't
ask your accountant for creative writing advice,
would you? Probably not, no. Or expect your graphic
designer to, I don't know, debug complex code.
It's about knowing the role. So in this deep
dive, we'll walk you through the main chat GPT
models. The everyday ones, the heavy hitters
for research. Even ones specifically for developers.
The goal here is that by the end, you'll know
exactly how to choose the right model, save yourself
time, and get, frankly, much better results.
OK, let's unpack this then. Before we get into
the nitty gritty of each one, maybe we should
get that bird's eye view first. Yes. Definitely.
Setting the stage is key here. It helps understand
the why behind switching, right? Exactly. If
we stick with that specialist on a team idea,
well, it makes perfect sense. You need different
skills for different tasks. A creative writer
isn't doing your complex math homework. Precisely.
And your detailed researcher probably isn't the
best for like super quick brainstorming. The
real skill isn't finding the one best model.
It's knowing when to switch. That's it. Knowing
when to tap the right specialist on the shoulder
for the job you have right now, optimizing the
whole interaction. So let's give everyone that
quick guide. We've kind of put together a cheat
sheet. Yeah. Nicknames, best uses, speed, complexity.
Mm -hmm. A quick reference. OK, so first up,
GPT -4. We're calling it the all -rounder. Super
fast, median complexity, great for like 90 %
of daily stuff, images too. Your go -to. Then
GPT -4. That's the deep thinker. It's slower,
yeah, but high complexity. You use this for the
really tough analysis, the complex reasoning.
When you need it to actually think. Then you
add the web to a GPT -4 Plus web, that's the
researcher. Slow, very high complexity, essential
if you need citations, current info, academic
stuff. Can't beat it for sighted research. For
the devs out there, GPT -4 via the API, the coder,
moderate speed, high complexity, programming,
digging through long documents. Yeah, the technical
powerhouse. And finally, GPT -3 .5 TURTO. The
workhorse. Blazing fast, low complexity. Best
for simple, high -volume, cheap tasks. Speed
and cost efficiency king. So seeing them all
laid out like that, it really drives home that
there's no single best model, is there? Not at
all. And actually, the limitations of each one,
that's where the opportunity lies. You use their
weaknesses to build a workflow where they complement
each other. It's not just tools. It's an integrated
AI assistant you're building. That shift in thinking
is key. That really clicks. OK, so speaking of
everyday use, let's talk GPT -4 .0, this all
-rounder. This is the one most people will use
most often, right? Our daily driver. Absolutely.
Think of GPT -4 .0 as like, your AI Swiss Army
knife. It's fast, conversational, really versatile.
For most day -to -day interactions, this should
be your default setting. So what's it really
good at, like specific examples? OK, so quick
summaries, brilliant. Give it a 2 ,000 -word
article. You'll get the gist in maybe 30 seconds.
Wow. Brainstorming, fantastic. Ask for, say,
10 slogan ideas for a new eco -friendly cleaning
product. Boom. You get a bunch of usable ideas
instantly. Great for just generating options.
And the image analysis. That sounds powerful.
It really is. You can upload a photo of like
a whiteboard from a meeting and ask it to summarize
the key points or a chart. What are the main
trends in the sales data? It can actually see
and interpret visuals and just drafting stuff,
emails, messages, social posts. Quick, easy,
write a friendly reminder email about Friday's
deadline. Done. OK, but there's always a but,
isn't there? Speed and versatility must come
at a cost. That's the trade -off, exactly. When
you push it on really complex stuff, you start
seeing the edges. It can feel a bit superficial.
Yeah, I've definitely hit that wall. I remember
trying to get it to help outline a really complex
argument for a paper. And it just kept giving
me the basics, missing the nuance. And honestly,
I still wrestle with getting the perfect initial
output sometimes, even with what seems like a
simple task. It just... doesn't quite nail it
sometimes. And that's often because it can be
overconfident, even when it's basically making
stuff up. That's what we mean by hallucinating.
Right, making things up confidently. Exactly.
It just states things as fact, even if they're
wrong, which can be, you know, pretty misleading
if you're not careful. It also struggles with
logic that needs multiple steps. It might miss
connections or just jump to a conclusion. So
that overconfident, slightly shallow response,
that's the signal. That's your big red flag.
If you find yourself thinking, hmm, I need to
double check that, or this feels a bit thin,
that's GDT 4 .0 telling you it's out of its depth.
Time to switch. Pushing it further just wastes
your time. That's a really clear indicator. OK,
so when you do need that depth. When accuracy
and real thinking are paramount, that's when
we bring out GPT -4, the deep thinker. This is
where it gets really interesting. Absolutely.
GPT -4 is, well, it's where the AI gets serious.
It's not instant, like, 4 -0. It takes its time.
Yeah. Beat. It actually seems to process things
more methodically. Slower. But the payoff is
quality and reliability. Much higher quality,
much more reliable. Its real strength is that
multi -step reasoning. Give it something complex,
like... Develop a comprehensive business plan
for a niche subscription box service. It won't
just spit out bullet points. It'll break it down,
market analysis, pricing, marketing strategy,
a structured, well -considered response. Much
less likely to just make stuff up, too. Far less
likely to hallucinate, yes. It captures nuances
that GPT -4 just breezes past. That reliability
is crucial for anything important. It looks at
things from different angles, gives more balanced
perspectives. How would you test that? if someone
wants to see the difference. Try giving both
models a philosophical question, like comparing
core tenets of stuicism and existentialism. Or
a complex business logic problem, maybe analyzing
different sauce pricing models and their implications.
The difference in depth will be obvious. It's
kind of a time -saving paradox, isn't it? Slower
response initially. Right. But it often gets
it right, or much closer to right, on the first
try. So you save time overall by avoiding endless
reprompting. Exactly. You invest a bit more waiting
time up front, but you save potentially hours
of fixing, fact -checking, and trying again later.
For critical thinking, for strategy, for... It's
the model you trust when the stakes are high.
That makes total sense. OK, so what about when
you need information that's really current? Stuff
that happened yesterday or even today? Something
beyond GPT -4's last training update. Ah, that's
where the researcher steps in, GPT -4 with web
browse enabled. Right, this connects it to the
live internet. Precisely. It transforms GPT -4
from just a knowledge base into an active research
assistant. It doesn't just know things, it can
find things out, right now. How does that work
behind the scenes, roughly? Well, when you turn
on browse, it basically plans a search strategy.
It figures out keywords, goes out and looks for
relevant, credible sources, news articles, academic
papers, reports, whatever fits. Then it reads
them, synthesizes the information. And crucially,
it cites its sources. You get clickable links
directly back to the web pages it used. So you
get structured analysis, real citations, conclusions
backed by actual verifiable evidence. Whoa. Imagine
having that kind of power like instant sighted
research on almost anything pulled from the whole
web It's genuinely transformative for certain
tasks. Think academic papers needing footnotes,
market analysis reports needing the absolute
latest figures, deeply researched blog posts,
policy briefings, anything where timeliness and
verifiability are key. So if I really need to
trust the information and know where it came
from, this is the go -to, no question. For timeliness
and verifiability, absolutely. Web browse is
essential for that. But remember, its output
is only as good as the sources it finds online.
You still need your critical thinking cap on.
It finds the info, but you still need to evaluate
its credibility, especially for complex or controversial
topics. It's an amazing assistant, not a replacement
for judgment. Good point. OK, let's shift gears
slightly. Let's talk about writing style, getting
that specific tone or creative flair. You mentioned
this isn't really a separate model. Right. It's
more about technique. It's how you use a powerful
model like GPT -4, or even GPT -4 sometimes,
to get the kind of prose you want. So how do
you do that? How do you elicit great writing?
It boils down to giving really clear, specific
instructions. Don't just say, write a story.
Specify the tone. Is it humorous, somber, suspenseful?
The style? Is it formal, casual, poetic? The
emotion you want to evoke. Examples. OK, say
you need marketing copy. Instead of just describe
headphones, try. Write a persuasive product description
for our new noise canceling headphones. Use vivid
sensory language. Focus on the feeling of calm
and focus they bring the user. See the difference.
That's more specific. Or for creative writing.
Describe a chaotic futuristic night market. Focus
heavily on the smells, sounds, and the feeling
of overwhelming energy mixed with excitement.
You're guiding its senses, its focus. What about
like brand voice? Perfect use case. You can even
give it examples. Here are three blog posts we've
written. Write a new one about managing anxiety,
matching this empathetic, supportive, and slightly
informal tone. Ah, so giving it examples helps
it learn the style. Massively. It's like giving
a musician sheet music versus just telling them
to play something sad. The more guidance, the
better the result. It's about being a good director
for your AI writer. So it's really less about
searching for some magic creative writing button.
Definitely not. And more about us getting better
at giving clear... detailed instructions, mastering
the prompt as a guide. Precisely. The model has
the potential. Your prompt unlocks it. You're
the conductor. Mid -roll sponsor read. All right.
Let's talk about the tools for the more technical
folks listening, the developers, the people building
things with AI. We're moving into the API models
now. Yeah. This is where things get really powerful
in terms of integration and scale. Accessing
the models via the API gives you much more control.
So first up is GPT -4 Turbo. via API. You called
it the coder and analyst. What makes it special?
Two main things. A massive context window. and
incredible technical precision. Okay, context
window. Just quickly, what's that in plain English?
Sure. It's basically how much information the
AI can hold in its working memory at one time.
Think of it like it's short -term memory for
the current conversation or task. So bigger context
window means it can handle much larger amounts
of text or code. Exactly. GPT -4 Turbo can process
the equivalent of hundreds of pages of text at
once. This makes it amazing for tasks like analyzing
an entire software code base, reviewing long
legal contracts, or processing huge research
documents, it can keep track of complex details
across a vast amount of input. And the technical
precision? It's just very, very good at understanding
and generating code, following complex technical
instructions, things like that. You could ask
it to, say, refactor this large PyCon script
to improve efficiency and add comprehensive error
handling. And it can tackle that kind of complex
instruction really well. OK, so that's the powerhouse.
What about the other main API model, GPT 3 .5
Turbo, the workhorse? Right. This one is all
about speed and cost effectiveness. It's way
faster than the GPT -4 models and significantly
cheaper to run via the API. So where does that
fit in? When would you choose speed and cost
over the power of GPT -4 Turbo? Lots of places.
Think high volume, relatively simple tasks. Customer
service chatbots, for example, need to respond
instantly. 3 .5 Turbo is perfect for that. Quick
answers, low latency. Exactly. Or text classification
sorting emails into categories like sales leads,
support queries, spam. It can do that very quickly
and cheaply. Any kind of simple, repetitive language
task where you need good enough quality, but
really high throughput and low cost. Like the
efficient assistant. handling the routine stuff.
That's a great way to put it. It frees up the
more powerful, more expensive models and your
own time for the tasks that really need that
deep thinking or massive context. So for someone
actually building an AI application or integrating
AI into their software, Are these API models
pretty much always the way to go? Generally,
yes. If you're building something beyond just
using the chat interface, the API gives you the
control, the scalability, and the integration
options you need. It's the foundation for building
real AI -powered products and features. Got it.
So wrapping this all together, the big idea,
the thing we really want you to take away from
this deep dive, is that effective users don't
just pick one model. They switch. Yes. It's absolutely
crucial. Embrace the toolbox approach. That drop
-down menu isn't just a setting, it's your selection
of specialized tools. Let's revisit those analogies
quickly. Okay. GPC 4 .0 is your hammer. Good
for most everyday jobs. Quick, reliable for common
tasks. GPT -4 is the precision screwdriver. For
when you need careful, detailed work, high accuracy.
GPT -4 with web browse. That's your research
microscope. Deep investigation needing verifiable
current sources. GPT -4 Turbo via API. The power
drill. heavy -duty technical work, coding, huge
documents, needs that extra oomph and control.
And GBT 3 .5 TurboVIA API. Your speed wrench.
Fast, efficient, cost -effective solutions for
simpler, high -volume tasks. And when you understand
that, you can build really smart workflows by
combining them. Exactly. Let's take content creation.
Maybe you start by brainstorming topics with
GPT -4 .0, get lots of ideas quickly. Okay. Then
you pick one and use GPT -4 plus web to do the
research, gather facts and sources. Makes sense.
Next, switch to GPT -4 to create a detailed outline,
leveraging its reasoning ability for structure.
Then maybe you use GPT -4 again to draft the
piece, focusing on quality and depth. And finish
up. Pop back to GPT -4 for a quick refinement
checking flow, adjusting tone, maybe touching
typos. See, multiple models, one task. That's
a great example. And for software development,
using the API. Similar idea. You might use GPT
-4 Turbo for the complex architectural planning
at the start. High level thinking. Then GPT -4
Turbo again for writing chunks of complex code,
but for maybe writing unit tests or simple debugging.
Switch to the faster, cheaper GPT 3 .5 Turbo.
Right. Use the workhorse for the repetitive bits.
And then maybe back to GPT 4 Turbo for generating
thorough documentation based on the code. So
the real skill seems to be in breaking down your
bigger goal into smaller steps. Yes. And then
strategically picking the best AI tool for each
specific step. That's precisely it. It's about
optimizing the entire process by matching the
model to the subtask. Workflow thinking. So let's
just recap that central idea one more time. Getting
good at choosing the right chat GPT model. It's
not just a minor tweak to get slightly better
answers. It fundamentally changes how you interact
with AI. It moves you from just using a tool
to leveraging a whole toolkit. Exactly. You start
fighting the limitations of one model and start
harnessing the combined strengths of all of them.
So our call to action for you listening is simple.
Go try it right now. Open up chat GPT. Look at
that model dropdown menu, usually top center
or top left. Don't just leave it on the default.
That's fair, man. Try switching between GPT 4
.0 and GPT 4 .0. for the same task. If you have
plus, try the web browse feature for something
current. The difference between a casual user
and a power user often isn't about having fancier
prompts. It's knowing which model to use when.
And now you have that framework. Treat that dropdown
like the specialist team it is. Remember, treat
the model dropdown like a toolbox, not a default
setting. Think about what you need speed, depth,
creativity, current info, coding power, then
choose. What hidden capabilities are you going
to unlock now that you know how to pick the right
tool? Thanks so much for joining us on this deep
dive OTRO music