A Non-Programmer's 10-Minute Guide to Using GenAI

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0:44Ethan Mollick:The thing about using generative AI like ChatGPT is the first way you try and use it is almost always going to lead you into frustration. So the first thing people do is they treat it like Google. They try and Google something and find out a fact, and actually it makes up a lot of facts, and it's not a search engine. Very unsatisfying people often walk away. Maybe they try and ask the system to have a personality. They'll banter with it. They expect it to be like Amazon's Alexa, and it doesn't have a real personality, and they walk away disappointed. Maybe after that, they start trying something maybe related to their job.

1:16Ethan Mollick:Maybe they ask for something really specific, and they get like a paragraph that's okay, but not very good, and they're kind of like disappointed. Maybe they walk away then. All of these things are actually misleading. They're not the way you actually want to use the system. To use the system, you have to treat it like a person in some ways, like an infinitely helpful intern that also happens to lie sometimes and get confused and really wants to please you. And if you start thinking about it that way, it unlocks a whole realm of possibilities about how to use the system to make your life better.

1:46Ethan Mollick:So what can this thing do for you? Let's do a couple of examples. Let's say we want to do some market research. common common thing to do is let's just tell it look up the market for running shoes give me a chart of the major players and their advantages and their market sizes and this is now using bing so bing is a version of chat gpt4 which is the most advanced model but it only works that way creative mode which you see I've selected here and the advantage of Bing is it's connected to the internet so it's actually looking at results. Now you can see that it's actually creating the entire chart that I might have wanted to have for my market size on here and not just that it's giving sources and information for each of these things.

2:32Ethan Mollick:Because the system doesn't actually know what's true what isn't you do want to fact check everything that you see. What I would do is check each of these sources and this is where Bing makes it nice because I can actually click the links and see what the sources are. Fact checking though takes me a lot less time than if I was trying to write this all from scratch. So we're getting a whole report here about the future of shoe brands with all kinds of really neat insights and stuff you work from. I would never just send this to someone, but as a starting place, it's probably saving me hours of work right now.

2:59Ethan Mollick:That's the way to start thinking about using the system. Hallucination is a fancy word for the AI makes something up with complete confidence, right? It doesn't know things. It will tell you stuff and its goal is to make you happy. So it's going to tell you all kinds of things. It also makes stuff up if you ask it to explain its own reasoning. It doesn't have reasoning. It'll just make up an answer that sounds good based on the answer it gave you. So it's easy to get this thing to lie, right? And a lot of people do that and then they're like, oh, this is useless. But if you're not trying to maximize lying and you learn what these rules are, it's very easy to have it produce mostly accurate answers.

3:30Ethan Mollick:You still want to check the results. So there's a mindset you want to have when you're working with these generative AI systems. And that mindset is based around the idea that it's an interaction. You've brought an alien mind to the table. How are we going to interact with that alien mind to get the most results out of it? All right, so let's say we want to make an argument on behalf of continuing a hybrid work policy at our workplace. And I'm going to use Bing here. So the first thing I often want to do is get the system to learn some stuff. You want to provide context to it about who it is and what it's trying to accomplish.

4:00Ethan Mollick:How do we provide context? Well, a few different ways. We can tell it who it is, and we can also give it a kind of context of knowledge. So let's first ask it to get some information. Look up why hybrid work is good and the arguments for hybrid work. We're kind of seeding it with context that we want it to understand. Now the next thing I'm going to do is give it a persona. I'm going to say who it is. So I'm going to say you are the manager at a Fortune 500 company and you want to write a memo to the senior leadership team about why hybrid work is good. Propel Fitness Water with Gatorade electrolytes, zero sugar, and vitamins.

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5:30Ethan Mollick:So now we're going to see what happens. If I don't like this answer and it looks like it's going to be okay so far, I would actually just stop the response, either ask it again or do a clean sweep and start over. One of the ways you're iterating is on the whole conversation. If you get to a dead end, just restart. It's memorizing stuff, it's getting stuck on a path, that path could continue. Now you can see this actually is a pretty high quality kind of memo piece it's already doing. Dear senior leaders, I'm writing with you with my own perspective, it's pretty good. But I would be reading this over carefully and saying, okay, are the facts correct?

5:57Ethan Mollick:And I might wanna make some corrections. Like I think some more specificity might help. I would say, we are a manufacturer of batteries. redo it with that information so now i want some specificity we'll see if it's better it might be better it might be worse the point is my it's almost costless for me to do this well if i sat down to write a memo to senior management it would take forever even if i don't use a single bit of the text here right it still has pushed me to think about topics i wouldn't have thought about otherwise i'm gonna end up being more creative i'm gonna have less problems in the way here and you'll now notice it's talking about the whole idea of it's now starting to weave this battery perspective into this i might want to go one step further i'm going to stop this here but i could say now tell me the arguments i would likely likely hear against this point in a meeting and how to answer them right and so now we'll go one step further let's figure out what the counter arguments are and what that would happen here.

7:03Ethan Mollick:So here's some possible arguments and it's saying it's giving the argument and the answer. So now we're prepping for this whole thing. And by the way, I've got a fictional case here, but I could give the names of the people. It would look them up and then tell me about the individuals involved and who would likely make that argument. Not everything here is going to be right, but we're working through a process of working with us. There's a whole bunch of different kind of ethical implications. On one hand, it's trained on the entire internet, which is a mix of amazing and sort of cesspool of nonsense, right?

7:32Ethan Mollick:People aren't being paid for the training data. Everything we've ever written is feeding the model. How do you feel about that is sort of an open question, and legally we're not 100 % sure of how that all works. The second ethical level up is that the models probably learn various biases about the world as a result of all of this. So if you ask it to talk about a story about an entrepreneur, it's most likely to talk about a male entrepreneur. And you see those kind of biases sneak in, and they can be very subtle and difficult to detect. The third level is on top of all of this, companies like OpenAI have worked pretty hard to create guardrails.

8:00Ethan Mollick:It's why the system won't do certain things or sometimes will refuse to do things because it will not let you make an offensive statement, right? Or it'll try to prevent you from doing that. Some people chafe against those restrictions, but that's an attempt to make the thing be ethical. And then there's a final level, which is called alignment, which is the idea that as these AIs get more powerful, are they doing things that help humankind as a whole or hurt humankind as a whole. There's a lot of levels of ethics to deal with. The truth is that when you're using the system, it's hard to sort of separate out what's happening.

8:28Ethan Mollick:And you kind of have to make a choice about whether you're going to embrace this or not. But there's a lot of complex ethical discussions happening. So let's, let's like create a product. Let's do a watch. We want to create a new watch. So let's say the first thing, Bing, look up the trends in mechanical watches for 2023 and list some. So it'll do that for us now. So again, I'm giving context. Because the process has a lot of randomness, by asking the AI to go step by step, and you can even just say step one, do this, step two, do this, step three, do that. That is the most effective way to prompt.

9:09Ethan Mollick:So it's talking about some watches for various trends. So we could say, create personas for five different buyers of watches. Make a table describing them and what they are looking for. Propel Fitness Water with Gatorade electrolytes, zero sugar, and vitamins. Propel hydrates better than water to help you get the most out of your workout and get back to your best self. What propels you? Propel with Gatorade Electrolytes. When you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more.

9:57Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a$75 sponsored job credit at indeed.com slash podcast. That's indeed.com slash podcast. Terms and conditions apply. Need a hiring hero? This is a job for Indeed sponsored jobs.

10:15Ethan Mollick:So now you can see we've got Beth, Carol, Dana, and Eric and their budget for watches and their motivations. Let's say conduct a fake focus group with people in the Carol category. And again, we're taking it step by step. And the Carols like this one better. I like that it's a collective set of Carols that are working on this problem. And then we could say, okay, now describe the watch, you know, a prototype watch that would make Carol happy and pass that detailed description to Dolly, which is the image maker, to show me a picture. We can also say, while we're doing this, come up with the proposal for a website.

11:12Ethan Mollick:Oh, it's not bad. Selling this watch to Carol. And so here, those are not bad pictures. And so let's do that here. So now it's going to write a proposal that we might want to give to our website designer. It's creating a whole timepiece thing called vintage timepieces. And here's what we're going to do with this. we're going to take this information which is giving us and we're going to paste it into gpt4 and ask gpt4 to create us the home page so we'll just take this here and i'll copy this over the context window on bing it only takes a little bit of space and so it's not as good for coding right so we don't need the internet anymore so we will say we will ask it to create this website create the html and css okay so now it's going to create the website for us and we can just literally paste this in um and here we go so coding up for us right nice nice way of skipping website design if you don't know how to use this by the way just ask it it'll tell you how to create a web page and how to host it and where to host it and do all that work for you so all of this is novel and we created a product in three or four minutes right with a web page and done fake market research on it it's pretty cool to be very honest almost everyone i know who has used these systems you are you kind of bounce off it until you it kind of clicks for you and then you have a very sleepless night or two and i don't have an answer for the sleepless night right i think you kind of like that seems to be what happens right you're like oh actually this isn't a toy this actually does matter.

12:50Ethan Mollick:This is actually going to affect my job or jobs people I know. And then you're like, what does it mean? And we don't have an answer. So what you do is you double down and you learn how to use these tools. And then you think about what is it? What capabilities does that give me that I didn't have before? What parts of my job that are boring and awful can I offload? What things are tedious that I don't have to do anymore? And that model can kind of get you through that period.

13:48We'll see you next time. Sweet Green App.