So What? How to do prompt engineering

Well, hey everyone. Happy New Year. Happy Thursday. Uh welcome to So What The Marketing Analytics and Insights Live Show. I'm Katie joined by Chris and John.

New Year, same us. Uh uh in today's episode, we're gonna be covering prompt engineering. And so the hot topic for the past at least month or so. I know, Chris, it makes you cringe because you're gonna say for like the past few years, but people have only just started, you know, playing with it and understanding, even wrapping their heads around it, are AI assisted content writers. And so specifically, we're gonna be talking about uh chat GPT and stable diffusion.

Now, I don't know about the two of you, but I'm seeing posts and threads and you know, people everywhere saying that they have figured it out, and here's the top 10 ways that you can make this work for you in your business and your writing and your marketing, and you know, it's everyone thinks they have the solution to it. And you know, what we're gonna do today is talk about how individuals can come up with their own prompts, basically prompt engineering for their own personal use. There's no personalization there. Like, what if none of those scenarios apply to the business that I run? I'm still sort of stuck.

So, what we want to do today is talk through the correct way to approach prompt engineering. And John, I know you're gonna have like your top five fails. Yes, we have the list. We can probably we can probably uh we'll figure it out. I have some ideas.

All right, so Chris, this is something that you know a lot about and have been working on actually for quite a long time and have been experimenting with it. So, where where would you like to start today for this to be the most valuable? Let's do a little myth busting first. Okay, and then uh dig into the mechanics. So all these AI systems, they're not sentient, they're not self-aware, the Terminator is not anytime soon.

These are all based on what are called large language models. And a large language model is really simple to understand conceptually. Given a uh enormous amount of text that has been digested down and categorized, you then ask these models to create something from that text. There was a great expression. I was listening to uh this week in Machine Learning and AI, the podcast, which I highly recommend if you're a very technical person.

Um and they were interviewing um some folks from uh the Allen Institute about the evolution of natural language processing in these models. And the one guest whose name I can't remember, and I apologize for that. Um said, a word is known by the company it keeps. A word is known by the company it keeps. And this is the essence of large language models.

If you were to scrape the entire Trust Insights website, right? What words would be associated with Katie Robert? That those two words, right? Because the good chance it'd be words like podcast, insights, live stream, Christopher Penn, John Wall, and so on and so forth, because these words appear in proximity to each other in a block of text. So when companies like OpenAI and the Allen Institute and Google and such build these large language models, essentially what they're doing is they take a whole bunch of words, put them on a given topic, put them all in a blender, um, and and create a sort of word suit.

But within every word and phrase are mathematical associations for the words and phrases that appear nearby. That's why these tools can generate things like grammatically correct sentences. We have not taught them the rules of grammar, but they know statistically from a probability perspective, you're probably not gonna say happy new Rutabega, right? That's not probably not a phrase you say nearly as often as happy new year, right? And so that construct is is is appropriate.

It will understand based on frequency. Think different is is a phrase you're supposed to use, even though it's grammatically wrong, right? It Apple's Apple intentionally did it wrong. It's think differently, right? The adverb version, but think different was the name of the campaign because it's been used so much and appears in so much of the text out in the public, the tools understand that that is a thing.

So when we're working with these large language models, that expression, a word is known by the company it keeps, guides how we do prompt engineering. The more words that we use in our prompts, and the more specific we are, the better the results we get. The less detail we provide, the less fewer guardrails and associations that we provide in a prompt, the worse these things perform. And if you're unfamiliar, a prompt is nothing more than just putting a piece of text into one of these machines asking it to do something. So this is the chat GPT interface.

Um, OpenAI's other interface, the playground is similar. And a prompt is simply the instructions that you want it to do. So I think Katie, that's the the first place that it started is helping people understand what it is that you're doing. Well, and so you know what I'm hearing you say is if you put in a prompt, which is basically a set of instructions, uh, a prompt that is very vague that says, you know, write a 500-word blog on marketing. It's gonna do its best to come up with something, but if you're more specific to say, I need to teach, you know, my users about the best practices for you know digital marketing on small budgets, you're gonna get a very different set of results.

And so, really, what you're saying, Chris, is be as specific as possible when generating your set of instructions. And in this case, it's a prompt. Exactly. And that's the thing about a lot of these tools is users don't realize how detailed you can be. So let's do a uh some couple of examples.

I'm gonna do um you are a social media manager for a podcast. Write five promotional tweets for your podcast. And so this is a very simple, straightforward prompt. And you can see, like it doesn't really know what to do. There's not a lot of detail there, right?

It says podcast name, topic, guest name, give it a listen on a platform, and so on and so forth. This is not. If you were going to put this in the the fails category, I think this is kind of not great. So while it's thinking about that, let's rephrase that. Let's reframe that.

Well, and I think that it's interesting that you started with you are a social media manager for a podcast. So you're already introducing a new kind of prompt that I don't know that users are thinking about. You're telling the system the persona that they're supposed to have when they're writing these prompts. So that's step one. It almost sounds like, hey, if you created a user story, you might have all the pieces that you need to create a proper prompt.

That's correct. Um, and so for those who aren't familiar or just want a refresher, a user story is a simple statement with three parts. As a persona, in this case, it would be the social media manager. I want to write uh five promotional tweets for the podcast so that people sign up for the podcast. So that would sort of become your guideline for creating your prompts in this instance.

And so, Chris, you're definitely uh getting more detailed with the prompt the second time around. That's right. Now, what makes chat GPT interesting compared to other versions of the same model is that it has a feature that other versions of it don't have, which is memory. When you have a chat that you're engaging in, it does have the ability to remember what has previously happened. So in this case, I've said the name of the podcast is Marketing Over Coffee.

The hosts of the podcast are John Wall and Christopher Penn. The podcast has been on the air since 2007 and airs weekly. The podcast URL is Marketing Overcoffee.com. Um the podcast is a weekly conversation about marketing and current events. Rewrite these tweets with these new details.

So now the the question I have is do you need to be setting an intention though with the purpose of getting people to listen or with the purpose of getting people to subscribe? You absolutely can. So it again, this is weird. You should be as specific as possible. So specificity in prompt engineering is the way to happiness.

As it is with many things in life. So, John, in terms of your top five fails, uh, it sounds like you know, write five promotional tweets for my podcast is probably the wrong way to approach it. Yeah, definitely. You know, in fact, let me I'll just throw this out. So the idea with this was that there are actually queries you can make that are so horrible that chat GPT just won't even refuses to do it.

So number five, write 500 tweets encouraging entrepreneurs to increase their hustle. Number four, generate blog posts of VCs congratulating themselves for their morning routines and vision boards. Number three, make a convincing argument why we need more TikTok influencers. Oh my god. Number two, write a children's book about conceding elections with class and dignity by a Republican.

And number one, create a podcast about branding from C level agency executives. Those are the five worst uh prompts that you could throw out there that refuses to take. So that was my my big brainstorm this morning in the shower to throw those out. So I hope people are fantastic. But I I think you make a good point, John, because I feel like that's the misunderstanding of how a system like this is going to be used.

People are going to try to use it that way when you really need to be more thoughtful about the approach. Um so thank you for sharing that, John. That was fantastic. Exactly. Do you see that that's that iterative approach all the time though?

I mean, have you actually had times where you're going five, seven, eight times to continue tweaking the thing? Oh, absolutely. And in fact, you should, right? Part of the the benefit of a system that has some memory is that you can continue refining it. So now I've said, hey, be sure to include the URL in each tweet.

Right? The tweet should encourage the user to listen in. Um, and now it's including the URL in there. There's still some placeholders and stuff in here, but it's it is getting better. And so this is one of the things that's really nice about a system like this is that you can get it to improve its results.

And then what you do is you go back and you consolidate all the different prompts that you've put in and create sort of a master prompt that has all those details, that specificity up front. So you can and should start with a user story, but if you forgot, or you just are messing around, you can go through it and iterate until you get a result that you're pretty happy with, then you save that that prompt somewhere in a notebook and you know, uh a text file, whatever, and you ultimately have it um available to reuse when you need it to. Now, the nice thing about these tools is you know, you we're just talking about plain text here. So this is this is very straightforward stuff, and there's a lot of use cases that people have done some fun stuff with. Um, they've you know, there's ones like you know, pretend I'm a per your personal chef, I'll I'll give you my my uh dietary preferences.

Uh, you're gonna give me some healthy dinner ideas and things like that. That's a kind of a fun one. But from a practical perspective, there's a lot of things that we can do with this software that I think have more utility. So let me give you an example of one. Well, before you move before you move on to the practical utility, I want to ask the double's advocate question here.

So, you know, John, you would ask, you know, do you go through like five, six, seven, eight rounds of iteration with the prompt? You know, what do you say to those who are going to push back and say, well, by the time you got you know the refined prompt and the responses you want, I just could have written the thing in the first place. It's the same as why would you use R instead of Excel? Because you want reproducibility, want it to be able to be reused. So because this is the GPT 3 language model under the underneath, what you eventually end up doing is use these playgrounds, use these tools to sort of iterate, be your your R and D lab.

And then once you are ready to be to really um build, you know, build something with it, you go in and you start writing code, right? You start writing code that can it can that can take this data and turn it into something that is you would put into production. So um, I'll give you an example. For the longest time at Trust Insights, we have been creating uh keyword forecasts, right? These keyword forecasts uh essentially we give uh uh our software uh list of keywords, search volumes, and and past search history.

And we say, hey, let's build a forecast for the next 12 weeks of the content that you know is the search trends that are likely to trend. And we've we've done that, we've used it for ourselves, we've used it to hand off to clients and stuff like that. And it's it's been, I would say reasonably well received, but we've not always gotten people to do as much with it as we want, including ourselves, right? Including ourselves. So we started playing with the open AI system and then start incorporating that into our code.

And so now instead of just giving you a list of keywords uh each week uh for a year, we have said, okay, what if we start giving you the actual blog topics, right? Given our keyword list. Maybe you're like uh, you know, Katie, something you've said on our podcast is ah, I don't know what to do with this keyword. I mean, this is like the 16th time I've seen this keyword. Well, here's a different way of approaching this where you now have uh a starting point to create some new content.

And this is the automation part of AI. And this is the this is a critically important. The tools like chat GPT and stuff are fun, they're a lot of fun. You can spend a lot of time. In fact, we talked to folks in our analytics from marketers Slack group about how have you been integrating this.

And people are still at the at the I call it the party tricks stage. Like, hey, we we made this thing and it did this thing, it was fun. But to graduate from party tricks to production requires now thinking, how do I want to use this thing and bring it to life in a meaningful way? And I think that that's where, you know, and as sort of I've as I mentioned at the top of the episode, I'm seeing so many people posting the you know, top 10 ways to get chat GPT to work for you. And it still feels very hacky, very party tricky, because it doesn't it doesn't have that repeatability, it still feels like that one and done, and it's not very instructional, it's just more of ideas like yeah, I would love to have you know AI write blog posts for me.

I could put that out as a top 10 way to use this thing, but I'm still not telling people how to make it repeatable. I'm still not telling people how to actually train it to do the thing. I'm just saying you could do that. You you could also switch from tying your shoes to using Velcro. It's also a thing that you could do.

Elastic shoelaces, I'm telling you, elastic shoelaces is the way to go. Shout out to the nursing home. And and that's because these tools as they are now are intended for humans to use, right? And they're intended as one offs, they are intended for people to get a sense of what's possible, but these tools are not the production uses of these tools, and there's a big gap between prototype and production. These are prototypes, and even in the you know, the slightly more sophisticated playground, this is still a prototype, right?

This is still just a uh a web interface to the model. This is not what you need to do to take it from um you know what it is into something that actually does the work behind the scenes. So that's that's where the gap is, and that's why it still feels like up, you know, party tricks and and and fun things. And uh, yes, a lot of people are prognosticating right now. Oh, this is gonna change everything.

Yes, it will, but it not in this form, it because at some point you have to take it into production. They have to make it be able to scale, and you know, having humans copy pasting all day long is not a particularly scalable way of doing things, particularly when the tools do have things like APIs that allow you to do this. Oh, they do have an API. I was gonna assume that they didn't have that yet. Oh god, yes.

Um, that they've had the APIs since the very beginning because that's how they make their money, right? If you look at the billing on the back end, um, an API call, they I think it's like a thousand tokens is is a penny. Um so when you write a prompt, in fact, let's do this. Let's do something here. Um let me take an episode of one of my shows, and I'm gonna say I want to summarize in a bullet point list the key points of this episode.

Now, I'm gonna give it a maximum token length of a thousand, that's not 800 words, give or take. Uh uh token uh tokens are slightly less than a word. When I hit go, it's gonna create this thing. It's gonna start spinning these things out, and every time it's generating a word now, it is incurring a very small cost, right? So this probably used it looks like it used about 300 tokens, so it cost uh our account a third of a penny to generate that.

If this goes into a piece of software like a word processor or something like that, where now hundreds of users or thousands of users is using it, that third of a penny ramps up real fast, and that's how OpenAI makes its money, right? You know, you're having 10,000, 20,000, 30,000 worth of compute time because they've taken it and put it into production. So as individual marketers, these tools are actually one of the most cost-effective tools for doing stuff like this at the scale at human scale. Once you take it into production scale, that's when it gets expensive. But again, because you're probably using software developers and stuff, you already know that.

It's like any other API calls, you know, sending stuff to AWS, sending stuff to Google Cloud, you know how that works. But for marketing to take a leap forward, it's got to be integrated to the tools we use. The these you know one and done interfaces are good RD facilities. So let's go back to the question of prompt engineering. And so, you know, obviously people are still, you know, doing the party tricks, playing with it, you know, create a you know, samurai version of me riding on the back of a unicorn in space.

Like, yeah, that is a thing. I don't know how that's useful. Uh, personally, it wouldn't be useful to me. But so, what is how do we approach writing prompts? Because ultimately, if I'm understanding you correctly, that's gonna be the key to making this work for you in a repeatable way, is having a solid structure, which I do think would start with a user story of why you're doing it in the first place and what you want the outcome to be, so that when you're getting the text back, you can say yes, it did the thing, or no, it didn't do the thing.

Purpose is uh is the first part, and yeah, having that purpose be very clear is essential. The second part is what is the language that you would use if you were directing a human being to do it, right? So, uh, or what is the language you would use if you were instructing a human being to do it? So if I said summarize this, that's not super helpful, right? I mean, yeah, yeah, you can summarize it.

But if I handed this to a virtual assistant said, summarize this in a bullet point list. Okay, cool. What am I summarize? Summarize the key points of this episode in a bullet point list. Okay, that's a little bit more clear, right?

That's that's pretty clear as to what it is that you want me to do. And then if I said summarize this in a um at a fourth grade readability level, now I'm starting to think about the people who are involved, right? Who are the people that would be using this output, right? Um we have obviously a very different result because this is now scaled down to a different level of readability. So you've got to think about who the people are that are going to be using the outputs and and what they're gonna be using the outputs for.

You think about how is the output going to be used? So here's um uh an example. Let's see, write a series of three multiple choice quiz questions from this text. Question for you. College age readability level.

Yes. So you just wrote write a series of three multiple choice quiz questions. Would you get the same result if you just said write three multiple choice questions? Maybe it depends on the it depends again. Remember, a word is told by the company, it keeps.

So in this case, I use that terminology because there's a concept called weighting. And the weight is how many times you use a word or related word in the prompt gives it additional weight. So you'll see this most with image prompts, for example, with Dolly 2 or stable diffusion or mid-journey, where you might say, Do a rendition of Katie Robert riding on a horse in a samurai outfit in outer space, highly realistic, photorealistic, highly detailed 8K resolution. Those last four words would give additional weight to the image to say this is it should be a photo, it should be photo-realistic, should be very detailed, as opposed to saying watercolor painting in the style of Henri Matisse, right? That would be a very different, uh, a different set of weights that the model would use to make a decision about how to render it with this by saying I want a series of three choice questions.

Okay, sometimes the model, because we are ambiguous writers, is unclear what what we're asking. So if we use words and phrases that are very specific, we can get better performance out of the model. So you know what's interesting is this is very much akin to delegation. And so one of the questions that I get a lot as a manager is how do I trust my team? Like, I don't I don't know how to delegate.

How do I get all this stuff off my plate? And my first piece of advice to them is to set appropriate expectations of what you want them to do. So, Chris, to your point of, you know, write a summary and then they give it to you back, and they didn't produce what you want. Well, guess what? That's your fault, not theirs, because you were not clear enough about what it was that you wanted.

So write a summary at a fourth grade level, including five bullet points, telling me what someone's gonna learn from listening to this episode. That's a very clear set of instructions. And then if the person, or in this case, the machine doesn't give you back what you asked for, that's when you can start to change things. But until you are clear about those expectations, it's still your fault, not the machine or the persons in that case. And so I see this as very similar to if you think about it like delegating to someone, and you are very clear about what you want from them, then you're going to get that thing back.

Exactly. That's exactly it. It is it is the probably the most underpaid virtual assistant you can ask for. But so basically prompt generation is akin to delegating a task. Exactly.

Prompt engineering is is the delegation of tasks. And when you think now about the different tasks that you might have somebody do, this is where the tools start to become useful. Right. So if I take the same thing, and I'm going to go into the into the chat one because I like to have the the memory a bit. 4,000.

Um you will be creating social media content to promote a piece of larger content. Why ready when you are ready to begin? Okay. The goal of the promotion is to encourage people to consume the content by watching or reading it. A piece of content is this transcript.

So it's interesting. It almost appears as though you're having a conversation with it this time around. Like you've totally switched up, you know, the prompt engineering, the way in which we've been talking about it. So you ended your first prompt with reply ready when you are ready to begin, versus here's the here's the purpose of the thing. Exactly.

And again, that's one of the things that makes this interface to the model interesting, is that it has the ability to behave in in a more conversational way. So for people who maybe are not clear about how to engineer good prompts, this is maybe the thing. So here's the here is the content. So as it's pulling that up, so to John's question about APIs, do you can you connect this interface to different data sources? Or are you sort of stuck with here's the data sources we've connected open AI to good luck?

No, so think about it the data sources is essentially just being it's a pipe. So whatever pipe you put into the pipe uh is one way, and then and then you know, whatever however it is you want to choose to get stuff out of it. So for example, what um when we're doing it with Google Trends and Google and and SEMRush data and and hrefs data, uh you're just connecting the code from from one system to another. Uh let's take a look here at see if I can open this up in Visual Studio Studio Code just to give you a sense of what that looks like. So in our code, we just have a single function here that sends the text of our choice into the system and returns the results.

This is the this is the code equivalent of typing in that window, right? Of copy and pasting stuff. Except now I can connect it, you know, any data source. Uh I could pull data out of a database, I could pull scrape data from the web, I could do any number of things to put data into a text prompt, feed it to the API, and then get the response back. And that's how that's how we do it for our stuff.

So you could integrate this into your a call system if you're having real-time voice transcripts. You could integrate it into um social media monitoring software. You could even take it off, you know, live closed captions off of a video if you wanted to and be feeding the data in. But no matter what it is you're doing, that's you you just get the data in. So now we've gotten this um in based on the transcript, write five promotional tweets for the content.

So, and then this goes back to could you not have just posted the transcript and say write pro five promotional tweets based on this transcript? I could have, but I enjoyed giving it extra context. Um, I could have put all that in one big prompt, but in this case I didn't because I would have had to have thought about that beforehand. And I and again, this is one of the things that a lot of people are thinking of uh is so different about this tool, is that you don't need to have that. You you can have a conversation to your point, as if it were an employee of yours, and say, okay, here's here's these these tweets.

Okay, great. That looks interesting. Uh, give me some video ideas for TikTok about what kind of videos I should make for TikTok to promote content. Now, this is all text-based, it can't do anything generally anything except generate text, but it's going to go ahead and give me some conceptual ideas for the types of TikTok videos I could make. So if you are one of those folks who's like, uh, I don't know what to do with TikTok.

I uh well, ask a machine, right? Ask a machine. Uh and here we are. Here we have behind the scenes create a series of TikTok videos to give viewers a behind the scenes look at the process of writing creating content, highlighting the unique quirks and imperfections of human written text. When we think about, we've talked for a long time about the um uh transmedia content framework, the idea that you can take a piece of media like video, you know, split it into all these different formats.

Now, the next evolution of that is the transformation uh framework where you say, okay, given this content, asking machines, just like you would a person, what else can I do with this? What are some things I could do with? So we have some TikTok video ideas here. Um I would say, uh, what are some instances album ideas to promote this content? So I guess, you know, back to my question about you know APIs and usability.

Um, you know, could someone like John, who's responsible for business development hook up a system like this to their CRM and say, give me a list of, you know, and whatever the qualifications are, like, you know, contacts who haven't, you know, participated in anything or engaged in anything, and write them a brief email to help them get re-engaged. Like, is that a use case for something like this? Absolutely. Again, would you ask it? Is it a language task?

If the answer is yes, then this is the appropriate tool for it. Would you ask a human employee to do that? The answer is yes. Is it repetitive work? The answer is yes.

Those three criteria make this a good candidate to do that. Well, yes, the writing the email is the language task, but the looking for accounts that are no longer engaged, yes, that's a language task in terms of my instruction, but that's more of a database piece. Is that something that a system like this could do? I would not trust a system like this to do that because that's too vague. What I would do is the example from the code I was showing earlier, you would have a piece of code that would go through your CRM and say, you know, make a list of only accounts that are aged past X days, right?

And then you look at the criteria in them. Uh, based on that criteria, you could then have it create some content. So let's start a new thing. Uh you are a top performing sales executive. We're talking about you, John.

Yeah. Given a list of lapsed accounts. And you will take existing call notes and use them to craft an email to each account, trying to win them back. So that's sort of our our table setting. Now let's uh let's put together uh something that is uh not under NDA, so account name uh acme mechanics contact on wall last contact date April 1, 2021.

All notes. John said he didn't have the budget right now. He's probably lying. ACME sold a billion dollars in widgets last order. Follow up with an email.

Now let's see what it does with this. Write a persuasive sales email based on this information. What's interesting is uh, you know, instead of out responding to your question earlier, it's sort of uh for lack of better term, it's sort of like machine splain to you how to deal with your sales problem. Exactly. And it was like, no, that's not what I asked you to do.

I asked you to write the damn thing. And so now I have to be mansplained on the left and machine splained on the right. Gosh. Look at the bright side though. It's machine splitting to me.

So it's it's it's equitable. Everybody gets machine splitting. Unbiased machine splitting. Exactly. Um it's created a response, right?

It says, Dear John, I hope this finds you well. I recently spoke to you on April 1st. Now to be fair, it didn't say John, I think you're lying. Yeah, it basically calls him out. But it does say, huh?

Um but now we can also see this needs some refinement. Sure. Refine this email. The comp our company is trust insights. We sell analytics and management consulting services.

Let's see how it does now with a bit of refinement. It still calls them out that's poking. Oh, I love that. Dear liar. I know you said you don't have the money, but I see that huge pile of money right behind you.

But yet your new yacht tells me something different. Exactly. So to your question about is this a valid use case? Yes. Again, is it text?

Yes. Is it language? Yes. Is it a repetitive task? Yes.

We would just need to be very specific in our prompts and our prompt engineering to provide all the information needed. So we can still see here this one is not quite right yet because it says sincerely your name. You would not want this email going out. Right. No, and I think that that's actually, you know, the number one piece of advice is that you still need to review and edit all of the things that come out of a system like this.

Like if you are just taking it and publishing it going live, that's that's a mistake because these machines are flawed. Just like humans. I mean, one is you don't know how it could respond to a new query, and then how often does the corpus get updated too? Like when would this change underneath you without you knowing it? So, at least in the case of you know the open AI models, they actually have do publish when they're changing the underlying um model itself.

So uh this year, I believe sometime in Q1, they're gonna be releasing GPT 4, which is going to be the the largest model of its kind. It's about 8x larger than the GP3 model, which means better performance, uh which means more natural use of language and things like that. Um so now we can see we're finally at a point now where okay, this is actually coming from somebody, right? It's not your name, it's coming from Katie. Um and we uh we told it all in addition to that new fact to change the tone to be warm, conversational, and professional, right?

And so it said warmly exactly. So let's see. I want to see if it doesn't do that. Still poked him in the eye. Yeah, and it's still way nicer than I would have been.

Old hostile professional. Ah, there I am. Dear Mr. Wall. Mr.

Wall, that's awesome. We're gonna see if it ends with up yours, Katie. It totally calls him on his billion dollar of widgets again. Mm-hmm. It just won't let go of that.

I suggest that you reconsider your channel. Sincerely, oh yeah. And then you throw the title out there. Sincerely is like good afternoon. Yes.

So um again, these are uh some of the things. One of the use cases that uh I use these tools uh the most for is meeting notes and action items. So I will take the transcript of a call, put it in here and say, give me a list of the action items from this call because you forget stuff. And reading through an unedited transcript is better than listening, re-listening to the same 45-minute call again, but you still miss stuff because your eyes kind of glaze over about the third of the way through. The machine when it spits out your 10 bullet points, great.

Okay, copy paste that goes straight into your task management system, and now you're off to the races. Um that's a very practical use. Um, other things you can do with it is having it do a lot of rewrites. Um, there are a lot of things that frankly, the these systems are good at creating content, but they are great at re-processing existing content. And I think that's one of the things that's that's most overlooked.

So let's start a new one. You are a social media marketing expert. Wise in crafting patchy content for audiences online. Rewrite the following biography in the first person. Appropriate for a LinkedIn bio.

So Katie, I just pasted your uh bio from the Trust Insights website, and now it's going to refraise this into something that will be good for your LinkedIn bio. So for folks who are like, uh, I don't know what to put for my LinkedIn bio, take the data that you've already got and and re-spin it. So there's a a nice you know decent LinkedIn bio. Um let's say rewrite this in a more casual, fun yet still professional tone. Hey there.

Hey there, that's me. Hey there. Finger guns. Hey everybody. Oh boy.

It's not all work and no play for me. I'm a Google Analytics certified professional. Oh yeah, that's screen. I thought wasn't enough. I have a master's degree.

Exactly. But so this is again this is a good idea. This is all just language manipulation. It's taking the data that it's been given and re-spinning it. And so that's where it's really good at.

Um one of the things that we've seen some examples of this already, but um let's take this hang on here. I'm gonna take this transcript from episode. Let's do I'm just gonna go with a shorter version now. Rewrite this transcript um correctly. We joke about that every week.

Like no one can explain it. Grammar syntax spelling punctuation and again, same exact thing, but we've seen at the bottom of our blog posts, you know, we paste in the the unedited transcripts from our our live streams and things like this. If we wanted to get that content rewritten to be more clear, uh more readable, but still preserve the language that we're using. This is a fantastic tool for doing that. Um for when we're driving around, and one of the things I like to do is I like to have a little voice recorder with me, either on my phone or something like this, and just foam at the mouth.

I I do this when I'm driving uh to like conferences and stuff. And if I have ideas, we know that sounds like word salad uh half the time. And yeah, Katie, you've even said I do not want to listen to six you know, 16 minutes of you rambling. Nope. I can feed it to this and say, okay, give me four paragraphs or summarize my main points.

And now I've taken that and turned it to something useful. So there are any number of applications. Again, if you would hire a virtual assistant to do it, if you would uh direct an employee to do it, it's a text-based language-based task that is repetitive. These tools are good candidates. And if you engineer your prompts well, as you were giving the way you were giving them to an employee that was or contractor that just started that day with all the details they need, you can make these tools very successful for them.

So I will share a quick anecdote of what I fear is going to happen moving forward. Uh so what's gonna happen is Chris is gonna start speaking to me and John as if we were the uh prompt box. Um, and I say this because this has happened before. So many, many moons ago when Chris and I worked uh elsewhere, we were traveling, and Chris had gotten so used to speaking into his phone to respond to emails that he started to speak to me as if I were the phone taking the dictation, including stop, new paragraph. And I was like, hey, dude, I'm right here in front of you.

Like, you don't have to be like that. And so I can imagine that as people get more in the weeds with prompt engineering, it will change the way that we communicate. Hopefully for the better, maybe more specifically, maybe more directly. Who knows? Exactly.

The other thing I'll caution people is that right now chat GPT is in a research preview. Um, it will not be forever. Uh, it will at some point come with a cost. So you should be prepared to deal with that, you know, when it can. But more than anything, use it as a testing ground so that you can integrate it into production level tools that will let it scale far beyond what one person typing prompts in a machine can do.

And that's that's where the real value is going to be unlocked. These things. It's uh we are seeing the human interface to a very, very powerful tool. Agreed. John, final thoughts.

That's all good. Just remember it's built on the average human's ability to write. So when you put in, you're gonna get back. All right. Thanks for tuning in, folks, and we will catch you all next week.

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