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