You Ask, I Answer: Why is ChatGPT All The Rage?

In today's episode, Donna asks, so why is Chat GPT all the rage now? If you're unfamiliar, if you have not heard, the OpenAI Corporation released a new interface to its GP23 model. GPT stands for General Pre-Trained Transformer. It's a large language model. And for a few years now, has made it accessible to folks for very small fees to generate language, to uh to work with language.

Last year, at the end of last year, they released a chat interface to it, where instead of having the usual um playground, uh, which they is is the name of their previous interface, they just turned into a chat bot. And people were suddenly able, who had no technical skills whatsoever, but could chat, right, were discovering what the GPT family of models was capable of, writing stories, scenes, lyrics, translations, you name it. Suddenly everyone's like, wow, I can use this thing. So Donna's question, why is it all the rage now? It's because it's easy.

The previous version, Playground, is very easy to use. And it provides, you know, a writing space and then some controls to tune the performance of the model a little bit. For a lot of people, that blank box is still intimidating. That blank box and you know, some buttons and knobs that have words that you don't necessarily understand what they mean in the context of a generative AI, still intimidating. A chat interface, something that looks like a a chat bot or uh a text message, people get that.

People grok that. People look at that and go, I know what I'm supposed to do here. And so people started to to talk to this thing, asking it questions, giving it different tasks, and as expected, because the exact same model underneath that the primary uh language models use for the rest of their software, it did a great job. People were able to suddenly have it generate tweets or rewrite lyrics as parodies or write horoscopes, all sorts of fun stuff, you know, great party tricks. And it really opened people's eyes to the capabilities of what large language models can do today.

They've evolved very, very quickly. In the last four years, we've gone from large language models that kind of produced word salad to models that could produce coherent, high-quality text. And so people started using this thing uh for every possible use case, like writing term papers and assignments for school, generating blog content. Uh you name it, people have used the large language model for it. And again, none of these use cases are new.

This is stuff that's been possible with this particular model for a bit more than a year now, actually, maybe double two years now. But because it was in previously a less friendly interface that came across as a little intimidating, it didn't take off in the same way. People still saw it as a very highly technical thing. When you make a tool and you dumb it down to uh an interface with no controls whatsoever, pretty much anybody can use it. Because again, you're presented with a chat box and a chat bot, you know what to do.

And that removed a lot of obstacles for people. It's interesting in watching the reaction to uh chat GPT. You're starting to see a lot of other vendors now building chat interfaces into their AI products. It's kind of a no-brainer. When you see that, people are going, oh, I people like chatbots.

Well, they don't necessarily like chatbots. They like an easy-to-use interface. They like an interface that doesn't make them feel dumb. Um they like an interface that doesn't frighten them. That's what this interface does.

It and it does it very, very well. The next step for a lot of folks should be now that you're comfortable with what the model is capable of in a chat interface, start using it with bigger, more complicated prompts, uh, more detailed prompts that take advantage of what the model's really good at to speed up your work, to improve your work, to generate content faster. There's gonna be a whole section in the the fourth edition of AI for marketers on recipes to use with generative AI. But I'll give you a sneak peek. These models are good at generating stuff that's new, but they're really good at revising things that you give them, right?

One of my favorite use cases is to feed them a transcript of a meeting. And you know, spoken word text always comes out like a little bit of word salad, a lot of stop words, a lot of uh kind of you know, those sorts of filler text that make reading transcripts hard. You can copy and paste a chunk of a transcript or the whole thing if it's a short transcript into one of these models and then say, write me some meeting notes and action items, and it will do that, and it will do it brilliantly. It is a huge time saver, it's a way to extract a lot of value from those recorded calls that you you have on file, and miss fewer things, miss fewer details. So that would be my suggestion for one way to use these things that's productive, that is easy and that leverages their real strengths, which is transforming one piece of text into another.

Thanks for the question. Talk to you soon. If you like this video, go ahead and hit that subscribe button.