WEBVTT

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Have you ever gotten a response back from ChatGPT

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and just felt, well, a bit let down? Oh, absolutely.

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You know, you ask something deep, something specific,

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and what you get back is, well, it sounds OK,

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but it's kind of shallow. Yeah. Or the opposite.

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You need something super simple, like right now,

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and it takes forever. Exactly. It feels like

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maybe you didn't phrase the prompt right, but

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maybe that's not it. Beat. Maybe the issue isn't

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the prompt, but the tool itself. Welcome to the

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deep dive. I'm your host Today we're gonna unpack

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that exact feeling because you see chat GPT isn't

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just one single AI. It's more like a a whole

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toolkit, a team of specialists, really. And our

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mission today is to help you figure out what

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each specialist does best. Right. Strength, weaknesses,

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and critically, when to call on each one. We

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want to turn that confusion into real clarity

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for you. And we're absolutely going to do that.

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It's like, think about a real team. You wouldn't

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ask your accountant for creative writing advice,

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would you? Probably not, no. Or expect your graphic

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designer to, I don't know, debug complex code.

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It's about knowing the role. So in this deep

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dive, we'll walk you through the main chat GPT

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models. The everyday ones, the heavy hitters

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for research. Even ones specifically for developers.

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The goal here is that by the end, you'll know

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exactly how to choose the right model, save yourself

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time, and get, frankly, much better results.

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OK, let's unpack this then. Before we get into

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the nitty gritty of each one, maybe we should

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get that bird's eye view first. Yes. Definitely.

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Setting the stage is key here. It helps understand

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the why behind switching, right? Exactly. If

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we stick with that specialist on a team idea,

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well, it makes perfect sense. You need different

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skills for different tasks. A creative writer

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isn't doing your complex math homework. Precisely.

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And your detailed researcher probably isn't the

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best for like super quick brainstorming. The

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real skill isn't finding the one best model.

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It's knowing when to switch. That's it. Knowing

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when to tap the right specialist on the shoulder

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for the job you have right now, optimizing the

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whole interaction. So let's give everyone that

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quick guide. We've kind of put together a cheat

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sheet. Yeah. Nicknames, best uses, speed, complexity.

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Mm -hmm. A quick reference. OK, so first up,

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GPT -4. We're calling it the all -rounder. Super

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fast, median complexity, great for like 90 %

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of daily stuff, images too. Your go -to. Then

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GPT -4. That's the deep thinker. It's slower,

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yeah, but high complexity. You use this for the

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really tough analysis, the complex reasoning.

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When you need it to actually think. Then you

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add the web to a GPT -4 Plus web, that's the

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researcher. Slow, very high complexity, essential

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if you need citations, current info, academic

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stuff. Can't beat it for sighted research. For

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the devs out there, GPT -4 via the API, the coder,

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moderate speed, high complexity, programming,

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digging through long documents. Yeah, the technical

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powerhouse. And finally, GPT -3 .5 TURTO. The

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workhorse. Blazing fast, low complexity. Best

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for simple, high -volume, cheap tasks. Speed

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and cost efficiency king. So seeing them all

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laid out like that, it really drives home that

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there's no single best model, is there? Not at

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all. And actually, the limitations of each one,

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that's where the opportunity lies. You use their

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weaknesses to build a workflow where they complement

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each other. It's not just tools. It's an integrated

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AI assistant you're building. That shift in thinking

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is key. That really clicks. OK, so speaking of

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everyday use, let's talk GPT -4 .0, this all

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-rounder. This is the one most people will use

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most often, right? Our daily driver. Absolutely.

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Think of GPT -4 .0 as like, your AI Swiss Army

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knife. It's fast, conversational, really versatile.

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For most day -to -day interactions, this should

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be your default setting. So what's it really

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good at, like specific examples? OK, so quick

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summaries, brilliant. Give it a 2 ,000 -word

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article. You'll get the gist in maybe 30 seconds.

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Wow. Brainstorming, fantastic. Ask for, say,

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10 slogan ideas for a new eco -friendly cleaning

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product. Boom. You get a bunch of usable ideas

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instantly. Great for just generating options.

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And the image analysis. That sounds powerful.

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It really is. You can upload a photo of like

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a whiteboard from a meeting and ask it to summarize

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the key points or a chart. What are the main

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trends in the sales data? It can actually see

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and interpret visuals and just drafting stuff,

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emails, messages, social posts. Quick, easy,

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write a friendly reminder email about Friday's

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deadline. Done. OK, but there's always a but,

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isn't there? Speed and versatility must come

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at a cost. That's the trade -off, exactly. When

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you push it on really complex stuff, you start

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seeing the edges. It can feel a bit superficial.

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Yeah, I've definitely hit that wall. I remember

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trying to get it to help outline a really complex

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argument for a paper. And it just kept giving

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me the basics, missing the nuance. And honestly,

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I still wrestle with getting the perfect initial

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output sometimes, even with what seems like a

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simple task. It just... doesn't quite nail it

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sometimes. And that's often because it can be

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overconfident, even when it's basically making

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stuff up. That's what we mean by hallucinating.

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Right, making things up confidently. Exactly.

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It just states things as fact, even if they're

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wrong, which can be, you know, pretty misleading

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if you're not careful. It also struggles with

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logic that needs multiple steps. It might miss

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connections or just jump to a conclusion. So

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that overconfident, slightly shallow response,

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that's the signal. That's your big red flag.

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If you find yourself thinking, hmm, I need to

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double check that, or this feels a bit thin,

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that's GDT 4 .0 telling you it's out of its depth.

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Time to switch. Pushing it further just wastes

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your time. That's a really clear indicator. OK,

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so when you do need that depth. When accuracy

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and real thinking are paramount, that's when

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we bring out GPT -4, the deep thinker. This is

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where it gets really interesting. Absolutely.

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GPT -4 is, well, it's where the AI gets serious.

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It's not instant, like, 4 -0. It takes its time.

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Yeah. Beat. It actually seems to process things

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more methodically. Slower. But the payoff is

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quality and reliability. Much higher quality,

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much more reliable. Its real strength is that

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multi -step reasoning. Give it something complex,

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like... Develop a comprehensive business plan

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for a niche subscription box service. It won't

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just spit out bullet points. It'll break it down,

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market analysis, pricing, marketing strategy,

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a structured, well -considered response. Much

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less likely to just make stuff up, too. Far less

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likely to hallucinate, yes. It captures nuances

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that GPT -4 just breezes past. That reliability

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is crucial for anything important. It looks at

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things from different angles, gives more balanced

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perspectives. How would you test that? if someone

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wants to see the difference. Try giving both

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models a philosophical question, like comparing

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core tenets of stuicism and existentialism. Or

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a complex business logic problem, maybe analyzing

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different sauce pricing models and their implications.

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The difference in depth will be obvious. It's

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kind of a time -saving paradox, isn't it? Slower

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response initially. Right. But it often gets

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it right, or much closer to right, on the first

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try. So you save time overall by avoiding endless

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reprompting. Exactly. You invest a bit more waiting

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time up front, but you save potentially hours

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of fixing, fact -checking, and trying again later.

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For critical thinking, for strategy, for... It's

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the model you trust when the stakes are high.

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That makes total sense. OK, so what about when

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you need information that's really current? Stuff

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that happened yesterday or even today? Something

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beyond GPT -4's last training update. Ah, that's

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where the researcher steps in, GPT -4 with web

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browse enabled. Right, this connects it to the

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live internet. Precisely. It transforms GPT -4

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from just a knowledge base into an active research

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assistant. It doesn't just know things, it can

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find things out, right now. How does that work

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behind the scenes, roughly? Well, when you turn

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on browse, it basically plans a search strategy.

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It figures out keywords, goes out and looks for

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relevant, credible sources, news articles, academic

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papers, reports, whatever fits. Then it reads

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them, synthesizes the information. And crucially,

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it cites its sources. You get clickable links

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directly back to the web pages it used. So you

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get structured analysis, real citations, conclusions

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backed by actual verifiable evidence. Whoa. Imagine

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having that kind of power like instant sighted

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research on almost anything pulled from the whole

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web It's genuinely transformative for certain

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tasks. Think academic papers needing footnotes,

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market analysis reports needing the absolute

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latest figures, deeply researched blog posts,

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policy briefings, anything where timeliness and

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verifiability are key. So if I really need to

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trust the information and know where it came

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from, this is the go -to, no question. For timeliness

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and verifiability, absolutely. Web browse is

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essential for that. But remember, its output

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is only as good as the sources it finds online.

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You still need your critical thinking cap on.

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It finds the info, but you still need to evaluate

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its credibility, especially for complex or controversial

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topics. It's an amazing assistant, not a replacement

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for judgment. Good point. OK, let's shift gears

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slightly. Let's talk about writing style, getting

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that specific tone or creative flair. You mentioned

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this isn't really a separate model. Right. It's

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more about technique. It's how you use a powerful

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model like GPT -4, or even GPT -4 sometimes,

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to get the kind of prose you want. So how do

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you do that? How do you elicit great writing?

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It boils down to giving really clear, specific

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instructions. Don't just say, write a story.

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Specify the tone. Is it humorous, somber, suspenseful?

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The style? Is it formal, casual, poetic? The

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emotion you want to evoke. Examples. OK, say

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you need marketing copy. Instead of just describe

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headphones, try. Write a persuasive product description

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for our new noise canceling headphones. Use vivid

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sensory language. Focus on the feeling of calm

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and focus they bring the user. See the difference.

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That's more specific. Or for creative writing.

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Describe a chaotic futuristic night market. Focus

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heavily on the smells, sounds, and the feeling

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of overwhelming energy mixed with excitement.

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You're guiding its senses, its focus. What about

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like brand voice? Perfect use case. You can even

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give it examples. Here are three blog posts we've

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written. Write a new one about managing anxiety,

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matching this empathetic, supportive, and slightly

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informal tone. Ah, so giving it examples helps

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it learn the style. Massively. It's like giving

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a musician sheet music versus just telling them

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to play something sad. The more guidance, the

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better the result. It's about being a good director

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for your AI writer. So it's really less about

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searching for some magic creative writing button.

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Definitely not. And more about us getting better

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at giving clear... detailed instructions, mastering

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the prompt as a guide. Precisely. The model has

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the potential. Your prompt unlocks it. You're

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the conductor. Mid -roll sponsor read. All right.

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Let's talk about the tools for the more technical

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folks listening, the developers, the people building

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things with AI. We're moving into the API models

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now. Yeah. This is where things get really powerful

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in terms of integration and scale. Accessing

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the models via the API gives you much more control.

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So first up is GPT -4 Turbo. via API. You called

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it the coder and analyst. What makes it special?

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Two main things. A massive context window. and

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incredible technical precision. Okay, context

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window. Just quickly, what's that in plain English?

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Sure. It's basically how much information the

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AI can hold in its working memory at one time.

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Think of it like it's short -term memory for

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the current conversation or task. So bigger context

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window means it can handle much larger amounts

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of text or code. Exactly. GPT -4 Turbo can process

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the equivalent of hundreds of pages of text at

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once. This makes it amazing for tasks like analyzing

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an entire software code base, reviewing long

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legal contracts, or processing huge research

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documents, it can keep track of complex details

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across a vast amount of input. And the technical

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precision? It's just very, very good at understanding

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and generating code, following complex technical

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instructions, things like that. You could ask

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it to, say, refactor this large PyCon script

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to improve efficiency and add comprehensive error

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handling. And it can tackle that kind of complex

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instruction really well. OK, so that's the powerhouse.

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What about the other main API model, GPT 3 .5

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Turbo, the workhorse? Right. This one is all

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about speed and cost effectiveness. It's way

00:12:15.820 --> 00:12:18.320
faster than the GPT -4 models and significantly

00:12:18.320 --> 00:12:20.980
cheaper to run via the API. So where does that

00:12:20.980 --> 00:12:23.259
fit in? When would you choose speed and cost

00:12:23.259 --> 00:12:27.029
over the power of GPT -4 Turbo? Lots of places.

00:12:27.350 --> 00:12:30.470
Think high volume, relatively simple tasks. Customer

00:12:30.470 --> 00:12:32.450
service chatbots, for example, need to respond

00:12:32.450 --> 00:12:35.129
instantly. 3 .5 Turbo is perfect for that. Quick

00:12:35.129 --> 00:12:38.289
answers, low latency. Exactly. Or text classification

00:12:38.289 --> 00:12:41.049
sorting emails into categories like sales leads,

00:12:41.370 --> 00:12:43.929
support queries, spam. It can do that very quickly

00:12:43.929 --> 00:12:47.230
and cheaply. Any kind of simple, repetitive language

00:12:47.230 --> 00:12:49.970
task where you need good enough quality, but

00:12:49.970 --> 00:12:52.429
really high throughput and low cost. Like the

00:12:52.429 --> 00:12:54.559
efficient assistant. handling the routine stuff.

00:12:54.759 --> 00:12:56.519
That's a great way to put it. It frees up the

00:12:56.519 --> 00:12:58.799
more powerful, more expensive models and your

00:12:58.799 --> 00:13:01.139
own time for the tasks that really need that

00:13:01.139 --> 00:13:03.539
deep thinking or massive context. So for someone

00:13:03.539 --> 00:13:05.960
actually building an AI application or integrating

00:13:05.960 --> 00:13:09.590
AI into their software, Are these API models

00:13:09.590 --> 00:13:11.649
pretty much always the way to go? Generally,

00:13:11.710 --> 00:13:13.570
yes. If you're building something beyond just

00:13:13.570 --> 00:13:16.049
using the chat interface, the API gives you the

00:13:16.049 --> 00:13:18.149
control, the scalability, and the integration

00:13:18.149 --> 00:13:20.629
options you need. It's the foundation for building

00:13:20.629 --> 00:13:23.470
real AI -powered products and features. Got it.

00:13:23.809 --> 00:13:26.529
So wrapping this all together, the big idea,

00:13:26.610 --> 00:13:28.409
the thing we really want you to take away from

00:13:28.409 --> 00:13:30.830
this deep dive, is that effective users don't

00:13:30.830 --> 00:13:34.350
just pick one model. They switch. Yes. It's absolutely

00:13:34.350 --> 00:13:37.970
crucial. Embrace the toolbox approach. That drop

00:13:37.970 --> 00:13:40.129
-down menu isn't just a setting, it's your selection

00:13:40.129 --> 00:13:42.509
of specialized tools. Let's revisit those analogies

00:13:42.509 --> 00:13:45.409
quickly. Okay. GPC 4 .0 is your hammer. Good

00:13:45.409 --> 00:13:48.429
for most everyday jobs. Quick, reliable for common

00:13:48.429 --> 00:13:51.269
tasks. GPT -4 is the precision screwdriver. For

00:13:51.269 --> 00:13:53.870
when you need careful, detailed work, high accuracy.

00:13:54.129 --> 00:13:56.250
GPT -4 with web browse. That's your research

00:13:56.250 --> 00:13:58.889
microscope. Deep investigation needing verifiable

00:13:58.889 --> 00:14:02.590
current sources. GPT -4 Turbo via API. The power

00:14:02.590 --> 00:14:05.279
drill. heavy -duty technical work, coding, huge

00:14:05.279 --> 00:14:07.840
documents, needs that extra oomph and control.

00:14:07.919 --> 00:14:11.360
And GBT 3 .5 TurboVIA API. Your speed wrench.

00:14:11.700 --> 00:14:13.840
Fast, efficient, cost -effective solutions for

00:14:13.840 --> 00:14:16.519
simpler, high -volume tasks. And when you understand

00:14:16.519 --> 00:14:19.500
that, you can build really smart workflows by

00:14:19.500 --> 00:14:22.080
combining them. Exactly. Let's take content creation.

00:14:22.920 --> 00:14:25.700
Maybe you start by brainstorming topics with

00:14:25.700 --> 00:14:29.000
GPT -4 .0, get lots of ideas quickly. Okay. Then

00:14:29.000 --> 00:14:32.820
you pick one and use GPT -4 plus web to do the

00:14:32.820 --> 00:14:35.240
research, gather facts and sources. Makes sense.

00:14:35.480 --> 00:14:39.039
Next, switch to GPT -4 to create a detailed outline,

00:14:39.440 --> 00:14:41.720
leveraging its reasoning ability for structure.

00:14:42.980 --> 00:14:45.419
Then maybe you use GPT -4 again to draft the

00:14:45.419 --> 00:14:47.679
piece, focusing on quality and depth. And finish

00:14:47.679 --> 00:14:50.450
up. Pop back to GPT -4 for a quick refinement

00:14:50.450 --> 00:14:52.429
checking flow, adjusting tone, maybe touching

00:14:52.429 --> 00:14:55.710
typos. See, multiple models, one task. That's

00:14:55.710 --> 00:14:57.730
a great example. And for software development,

00:14:58.149 --> 00:15:00.750
using the API. Similar idea. You might use GPT

00:15:00.750 --> 00:15:02.850
-4 Turbo for the complex architectural planning

00:15:02.850 --> 00:15:05.250
at the start. High level thinking. Then GPT -4

00:15:05.250 --> 00:15:07.750
Turbo again for writing chunks of complex code,

00:15:08.350 --> 00:15:11.090
but for maybe writing unit tests or simple debugging.

00:15:11.320 --> 00:15:14.700
Switch to the faster, cheaper GPT 3 .5 Turbo.

00:15:14.779 --> 00:15:17.159
Right. Use the workhorse for the repetitive bits.

00:15:17.299 --> 00:15:19.759
And then maybe back to GPT 4 Turbo for generating

00:15:19.759 --> 00:15:22.360
thorough documentation based on the code. So

00:15:22.360 --> 00:15:25.120
the real skill seems to be in breaking down your

00:15:25.120 --> 00:15:27.820
bigger goal into smaller steps. Yes. And then

00:15:27.820 --> 00:15:31.679
strategically picking the best AI tool for each

00:15:31.679 --> 00:15:34.129
specific step. That's precisely it. It's about

00:15:34.129 --> 00:15:36.490
optimizing the entire process by matching the

00:15:36.490 --> 00:15:40.450
model to the subtask. Workflow thinking. So let's

00:15:40.450 --> 00:15:43.399
just recap that central idea one more time. Getting

00:15:43.399 --> 00:15:46.399
good at choosing the right chat GPT model. It's

00:15:46.399 --> 00:15:48.279
not just a minor tweak to get slightly better

00:15:48.279 --> 00:15:51.279
answers. It fundamentally changes how you interact

00:15:51.279 --> 00:15:53.519
with AI. It moves you from just using a tool

00:15:53.519 --> 00:15:55.759
to leveraging a whole toolkit. Exactly. You start

00:15:55.759 --> 00:15:57.679
fighting the limitations of one model and start

00:15:57.679 --> 00:15:59.879
harnessing the combined strengths of all of them.

00:16:00.000 --> 00:16:02.220
So our call to action for you listening is simple.

00:16:02.279 --> 00:16:05.399
Go try it right now. Open up chat GPT. Look at

00:16:05.399 --> 00:16:07.519
that model dropdown menu, usually top center

00:16:07.519 --> 00:16:09.059
or top left. Don't just leave it on the default.

00:16:09.080 --> 00:16:11.299
That's fair, man. Try switching between GPT 4

00:16:11.299 --> 00:16:13.960
.0 and GPT 4 .0. for the same task. If you have

00:16:13.960 --> 00:16:16.220
plus, try the web browse feature for something

00:16:16.220 --> 00:16:18.460
current. The difference between a casual user

00:16:18.460 --> 00:16:21.139
and a power user often isn't about having fancier

00:16:21.139 --> 00:16:24.840
prompts. It's knowing which model to use when.

00:16:25.720 --> 00:16:28.679
And now you have that framework. Treat that dropdown

00:16:28.679 --> 00:16:31.899
like the specialist team it is. Remember, treat

00:16:31.899 --> 00:16:34.139
the model dropdown like a toolbox, not a default

00:16:34.139 --> 00:16:36.179
setting. Think about what you need speed, depth,

00:16:36.480 --> 00:16:38.860
creativity, current info, coding power, then

00:16:38.860 --> 00:16:41.909
choose. What hidden capabilities are you going

00:16:41.909 --> 00:16:44.149
to unlock now that you know how to pick the right

00:16:44.149 --> 00:16:45.730
tool? Thanks so much for joining us on this deep

00:16:45.730 --> 00:16:47.149
dive OTRO music