How this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes

Where do you start when you're thinking about designing and framing out a AI product for what you're working on at work? >> What's different about managing products that are powered by AI is there's the interface of how a user interacts with any product or product feature and that still really matters. And there's also a lot going on behind the scenes. There's a lot also about how do you drive good quality products because these technologies produce different results each time you use them. So we start with golden conversations. What's the experience that you're trying to drive? And so this is just a way for me to think about how to write that role playing a little bit with AI. What you're saying is actually write an example conversation that can represent what a real user might do. and you're working backwards from that example conversation which I have actually not seen anybody do before. Welcome back to how I AI. I'm Clarvo product leader and AI obsessive here on a mission to help you build better with these new tools. Today we have an AI PM showing us how to AI PM. Pria Matthew Badger is a PM at Yelp and is showing us a completely new way to think about product requirements, prototyping, and how to build effective conversational agents using conversational agents. Let's get to it. This episode is brought to you by GoFundMe giving funds, the zero fee daff. I want to tell you about a new product GoFundMe has launched called Giving Funds. a smarter, easier way to give, especially during tax season, which is basically here. GoFundMe giving funds is the DAFF or donor advice fund from the world's number one giving platform trusted by 200 million people. It's basically your own mini foundation without the lawyers or admin costs. 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I have to say, wait, are you talking about product managing with AI? Because I have some ideas about that. Or are you talking about product managing AI products? And what's really great about the conversation we're about to have is you actually do both. So what in your mind is really different about product managing products using AI? >> Yeah, I'm really excited to be here. Big fan of the show and have learned a lot about um AI, both managing AI products and how to use it in my day-to-day from the podcast. So it's exciting to be here. For me, I think you know what's different about managing products that are powered by AI is there's the you know interface of how a user interacts with a with any product or product feature. Um and that still really matters with AI products. Um and I'll show some of the tools that we use um to explore that. Then there's also a lot going on behind the scenes that determines the product experience for the consumer. So um the system prompts and how that guides the conversation flow is really interesting and I think kind of a new challenge when you're working on AI powered products and there's a lot also about how do you drive good quality products because these um technologies produce different results each time you use them. So there's a lot of um interesting challenges there too. Yeah. So, I'm really excited to myself learn from your flow because I'm building an AI powered product as well. And so, let's dive into it. Where do you start when you're thinking about designing and framing out a AI product for what you're working on at work? >> Yeah, absolutely. So, I thought a good example would be to talk about building a new feature capability into our Yelp Assistant. So that's the product I work on. And the way it works is a consumer can come in for a service need. So let's say you want to hire a handyman, a plumber, an electrician, somebody to fix your car, and you can describe the problem in your own words, and then the AI will understand what you're saying, collect some project details, and um help you get matched to pros and get quotes. And so that's how the product works. And we recently launched a feature that allowed consumers to upload a photo to help describe their need. And that just makes sense, right? It it helps for pros sometimes to be able to see a photo along with the description. But one of the things we wanted to do was because we're doing this in our AI assistant, think about, you know, how can we leverage those AI capabilities? Can the AI understand what's in the photo and customize the conversation from there? Um providing, you know, some recommendations around what the consumer should do next. as a a Yelp user, I can imagine that the variety of services that your pros are providing and um you know with I don't run consumer businesses, but I can imagine the the variety of things a user puts into these conversational or image upload interfaces could be very diverse. So I'm curious how you approach that from a product development perspective. >> Yeah, absolutely. Yeah, we certainly cover a lot of different categories of service needs at Yelp and one of the challenges is yeah, making sure that the experiences work across all those different use cases that a consumer might have. Do you want to jump in and uh I'll I'll show you my workflow. >> Yeah, let's do that. >> Okay. So, I'm going to just open up Claude. And here we're starting in a totally new window. And you know, as we talked about, like I think there's, you know, two pieces to these AI products. There's the behind the scenes part and then there's the interface. uh user interface that consumers see. Um and I like to start with thinking about what is that conversation flow going to look like when we add this new functionality. And so I'm going to show you here how you can do that with claude. Um and you can also use chat GPT or any other um of these foundational models. So here I'll say write a complete um sample conversation between the consumer and the AI assistant um where we want consumers to be able to upload their photo and then just add some scenario requirements like we want the assistant to analyze the photo maybe provide some suggested replies and uh continue that back and forth until they have enough info to submit quotes. One thing I'll call out on the prompting is I do like to give a little direction on what the output looks like. So you can see here I'm saying like use assistant colon user colon for labels, write it as one continuous conversation. I think that really helps make sure that you know you get the output that you're looking for and there's a little less back and forth with the AI. So for the folks listening, one of the things I want to call out that I think is really interesting about this approach is you're sort of using a example conversation as your first pass wireframe for building a conversational AI. So instead of saying like show me a chat window and show me messages that show up in these buttons, what you're saying is actually write an example conversation um that can represent what a real user might do and um you kind of give some some constraints about what that conversation could look like and you give it some of the capabilities that might be available during that conversation and you're working backwards from that example conversation which I have actually not seen anybody body do before. So I think it's a really unique approach that product managers out there working on conversational um AI products including myself can really take a lot of inspiration from. How did you come to this idea? I mean was this your like are you just a genius and you're like this is the first thing that we need to do or how did you come to this idea? >> No, I mean I think this is part of um our standard alumowered playbook at Yelp where we start with golden conversations. What's the experience that you're trying to drive? Um, and so, you know, I think, uh, this is just a way for me to like think about how to write that, um, roleplaying a little bit with AI. >> Yeah. And I just want to call this out. We're going to take a little side uh, detour to just some product management ideas, which is I often tell product managers to prototype their product as close to the end product that a consumer is going to consume, including the content. So when I worked in dev tools, um I would tell a lot of RPMs, don't write a PRD, write a quick start and documentation guide to the product. Write the code snippets. Um and then work backwards into what the product should look like. And so I love this idea of just from a general product perspective, work with the artifact that's closest to what the consumer is actually going to experience and then you can back into all the requirements once you're kind of inspired by what that end state is. So, what does something like this get you? >> Yeah, absolutely. So, let's go through it. So, I'm actually going to upload a real photo of a home service need. So, here's like a picture with a cracked porch. Um, >> not your cracked porch. >> It's not. No. Um, yeah. And then we'll look at what um what Claude comes back with. Um, I will say one of the pictures I'm going to test is from my bathroom renovation. So, you will see my bathroom. And one thing I'll call out is Claude now shows you your thought process. And you'll see this in a lot of AI tools. I really like to read the thought process and it's also something to do while you're waiting. Um, but I think it really helps because you can see how it's understanding you. If it doesn't come back with what you want, it also is really good for troubleshooting. So, definitely something I recommend doing. >> Yeah. One thing that I'll do while this is loading is call out, I too think that reading the reasoning or the thought process of the AI is interesting for two reasons. One, it can often help you improve your prompts because you understand what the AI is understanding or not understanding about your prompts. As somebody who likes misspelled, no sentence, low syntax prompts myself, it's good good to see where I'm misleading the AI. The other thing is the thought process is often where the AI reveals its personality. I think it is so funny >> to read like Gemini 25's thought process versus 03 versus Claude is very nice. Claude practices self-love. Um Gemini 25 does not. And so I just think it's uh it's also interesting from just like a a model understanding perspective. Okay. So we got a we got a chat here. >> Yeah. So then we can read through the chat and it's, you know, it's saying like, I can see you've uploaded this photo of a front front porch stabs with a significant crack running through the concrete. So pretty good recognition of the photo. And then it says, let's ask, let me ask a few questions, you know, how urgent is this? You know, are you looking to repair this? Would you prefer to replace the entire steps? And so I could look through this, you know, and maybe workshop it a little bit, giving it some feedback. I also find it's helpful to just create some more examples. Um sometimes like when you see a lot of examples, that's when the trends come out and that's when you see, you know, what you might want to improve or change. And so I have a bunch of images now. So now that I've tested it with one and I've seen that, you know, it works pretty well with that one. I'm now going to test it with a lot more images. And this is the prompt I'm going to use. So I'm going to say now create more examples based on these images. And to your point earlier, you know, Yelp covers lots of different um types of service needs. So, this is where you can kind of test and see how's it going to do across a lot of different problems. And so, here I have, you know, like a appliance repair issue with an error code. I have a hornet swap, a wasp nest. Um, so you can see, you know, a larger variety of things. And just because I know you really wanted to see my bathroom, I will also upload and add a picture of my bathroom renovation in progress. Um, and then I'm going to say, um, you know, label each conversation with a title and a number at at the top. So, just another example of how just that like little nudge on the output can really help you get something usable. Great. And so we're going to see here how this AI thinks about potentially framing responses to consumers on a variety of as a homeowner total nightmare scenarios. Everything from a wasp to a bathroom renovation, which I am also about to start um is just a nightmare to me whether or not I want to do it. Um and so you're getting these example conversations and what are you looking for? Are you are you looking for patterns? Are you looking for product inspiration? what's kind of the thing that you're seeking in these examples? >> Yeah, that's a great question and I think this like goes in with, you know, there's the the a lot of people talk about like evals are the new PRD and this is like the very early step of of getting getting to the eval process. Um, you know, I think you you get a sense of like what are the criteria that are important for this capability. So, you know, the first thing is like did it actually recognize the image? Well, right. So I can compare and see like in this first one like the oven door lock malfunction where I've uploaded this picture and it is actually looking and seeing that like it has the door locked and it's trying to understand that issue. You know maybe we would give it feedback to go one step further like pull that E3 error code you know look in your LM see if you uh understanding to see if you can guess what the issue is and and diagnose it better. Um but I think that's like the first step of is it um doing that recognition right and then after that you know we're we're looking through the conversation to first I just look at it qualitatively to see like does this feel like it sounds uh like it flows well is it concise is it easy to understand um and then we'd probably develop like more of a rubric around what are the criteria that we're looking for >> okay so you have these different conversations what do you do with them next Yeah, and I'll just show one example of refining these conversations and why AI is really great for this. So, you know, let's say I say I I think it's good, but I don't think it's being as opinionated as it could be about like offering the user a recommendation and maybe sometimes it's talking about budget, which we think the consumer may not know. So, I can ask it to rewrite these conversations based on this feedback and it will go through and update all those conversations for me, which I think is really nice. And um you know then you can go through and see you know do you feel like it's taking that feedback well? Is it actually rewriting it um based on that guidance? But definitely you know you can see here it's saying like this definitely requires professional pest control. Don't attempt a DIY removal of this nest. Um which I think is probably good advice. Um, and then to your other point about like how do we get um an artifact that is closest to the ex what the consumer will experience that is the next step that I'm going to show you and something I think that is pretty unique to Claude. Um, so Claude has a special functionality built in where it actually can create an artifact that uses the LM that powers Claude to produce those responses. And that's very unique to Claude. If you did this in another prototyping tool, you would typically have to set up a API key and um integration which just takes a little bit more work and with pod you can do it out of the box. So here you can see I'm asking it to create an assistant app as an artifact have a chat interface where the AI responds using the LLM that powers Claude and then also create system in uh prompt that is based on these example conversations and then analyze these upload loaded photos and include a camera um icon in the input. And then I'm actually going to upload some um screen grabs of our current Yelp Assistant and indicate that it should use these attached screenshots as an example for what the front end should look like just so that it feels a little bit more real. >> Got it. So you really are using example conversations and just reference designs as your PRD here. And then what you called out that's unique about quad artifacts is it has fully integrated quad AI. So you can actually generate artifacts that do make native LLM calls to the anthropic API. So if you are prototyping little AI product out there um check out Claude because it just makes it a little simpler and you don't have to pass it a bunch of API keys. >> Yeah, absolutely. And you can see that it's writing the code here and at the top it actually wrote the system instructions. And I think this is also a really good way to learn because you can see that based on these example conversations, how is Claude translating that into system instructions. Um so it's, you know, mirroring some of my initial prompting and redirection around providing suggested replies, um not asking the user about budget. And so I think that's um really helpful. And then you can see it gives some examples from my examples as part of how to guide the um assistant around photo analysis as well. All right. And so I'm going to test it out and we'll see if it works out of the box. Um it does sometimes require a little back and forth. Um so you can see here I have uploaded the photo of my issue and Claude is thinking. Okay, great. Um so here you can see it worked pretty well. So it said, you know, I can see it's showing F2 in red and the door locked and this is a common error code relating to the oven lock. You know, typically you want a repair technician. It's asking about the urgency. So it is, you know, simulating pretty well this conversation. And one of the reasons why I think it's helpful to simulate it in this kind of artifact is you can also get a real feel of how this would be for the user. Like you can see like sometimes a response that looks fine when you have it in a doc feels really long when you see it in like the little chat bubble and the mobile interface >> and you know that waiting period of like the three dots and then the response comes back when you play out the full conversation >> can feel very different. So I think this is also a really good step to do >> and then you can of course share this with your team or your designers or your engineers and they can also start to get a sense of how does this feel? Can we actually do this? How can we refine it or make it even operate better? So I I just have never thought of this low. I have to repeat it again for folks. You know, kind of starting inside out with a conversational agent, prototyping example conversations first, getting them um refine getting a good set of example conversations that you can then put into a um prototype generating tool in this instance claude to then back into the chat experience including the system prompt that would best serve those conversations as such a great flow. I'm so impressed. This episode is brought to you by Persona, the B2B identity platform helping product fraud and trust and safety teams protect what they're building in an AI first world. 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So, how do you take this how do you take this to that next step of, you know, really um designing out what the real product might look like? >> Yeah, for sure. And I will say like I think this is all just a starting point and it's a part of a conversation with your larger team, right? With the engineers and with the with designers like I think this is really something that helps me clarify my own thinking and ideas and like refine what is that ideal conversation look like and and also just you know be a better collaborator because I understand system instructions better um as uh as we're going through features. Um but yeah, so I think um you know, it still goes through our our our usual like design and engineering pro uh processes once we have a good idea of you know where we're headed and it really has been a collaborative process for us between design, product and engineering where we're all writing these conversations together. We're giving each other feedback on them. Um so now we're going to I'm going to talk about you know how do we how do we think about the exploring ideas on the other side? So we we went pretty deep on like what does that conversation flow look like? How can we use cloud to um explore ideas there and the other piece is like how do use what does the interface look like? What are the user flows? How does a user get into these assistant experiences? And I have seen that a lot of those little details matter as well. You know what are the prompts? How how does a user understand the capabilities of the assistant? And so here with uh I'm going to show another tool which is magic patterns. And I think magic patterns is really great for when you want to explore something visually and like kind of consider what that flow would look like. I know Colin Matthews was on this show earlier and he showed how you can recreate a you know an existing product using component library or screenshots. So I'm not going to cover that in detail. So here I've recreated our Yelp Assistant um with that kind of approach. But I'm going to show you how you can then move on um to actually explore features within u magic patterns which I think is a lot of fun. So here I'm going to actually ask it to add a prompt suggestion at the top for start with a photo which allows the user to upload a photo. And you know you can see here it's it's thinking and it's saying I will start um add this prompt suggestion for start with a photo. um this will likely require these things. Um for styling, I'm going to consider this. So again, like reading those thinking instructions, I think is super helpful. So what it's doing now, now that it has those instructions, it looks like it's sort of doing this thing that you see in a lot of these prototyping tools, which is it's creating or updating new components, updating components. It's going to kind of insert those design elements into into this design for you to give feedback and test with. And I just have to say, you've been a PM for a little bit. I've been a PM for a little bit. Have you ever had access to this kind of like ondemand design and code? Like is has this totally like changed the way you think about working through designs, wireframes, stuff like that? >> Yeah, it absolutely has. Yeah, I think my mind was kind of blown to be honest. the first time I use these like natural language prompting prototyping tools just because yeah it's just so magical for you as a PM to be like hey I can just describe what's in my head and actually have it you know come to life um in a prototype. So it really has uh you know I think the core of the of the PM job and the earliest part of the workflow hasn't really changed and that you're still trying to understand deeply the user problem figure out what to prioritize. Um but I think it really helps in the phase after that where as a team you're exploring the solution space. What can really solve that problem for a user? How do we make them aware of it? How do we make sure it's easy to use? And I feel like it's just really fun to be able to like play around in these tools and explore ideas um myself visually and and find better ways where I can communicate something that's in my head. >> Amazing. Okay. So now we have a start with a photo. >> Okay. So yeah, we have a start with a photo. As you can see here, it's got this UI where I can start with a photo. Um so you know that's you know one option. And then of course like you know we did something simple when you launch this feature where there's just a camera icon but I'm showing this example as a way that you know you can explore like what would other ways be that we could make this experience um as you're thinking about iterating. And so here I'm going to show you this really cool feature within Magic Patterns which is called inspiration mode. Um and definitely recommend digging into this menu in general. Um, they have like a lot of nice little shortcuts, but this inspiration mode is my favorite because you can quickly explore lots of different options. So, here I can say, "Give me some options on how the start with the photo flow could work to make it feel more guided for the user." And this part of the prompt I workshopped a little bit, but I think works to help have the inspiration mode come up with different ideas. I say like think expansively and make each option differentiated and then explain in in your response which option um what each option is. Um and so I'm going to go ahead and submit that and it will generate for me four different options. And you'll see that um once it goes through this process, it will actually have four different boxes on the screen. And as you want to explore those options, you can click through those boxes and it'll update what's on the left side. So you can really quickly explore and see the different ideas and you know decide what you like. Um and I like doing this because I think sometimes we come in and we feel like we need to have a whole PRD before we can start prototyping. And that's definitely one approach and use case for AI prototyping tools. But I've also found that they're helpful even earlier when you you do understand your you know your user problem, what you're trying to solve for, but you may not know really what those solution looks like and you want to explore and maybe get some ideas from AI as well. Yeah, this just makes me think I don't know if designers are going to love this or hate this. I remember this experience when I was a designer where somebody would give me a purity or a feature like this and I would give them back a design like what we see on the left and they'd be like great but can we like try it over here and try it over there and move it up there and make it this button and like make it a link and that like manual iteration where it wasn't really um moving the product forward. It was kind of getting our own minds around what the problem space and the solution space could be so that we could move the product forward just took a lot of time and so I think it's really interesting to compress the time for ideiation so that you can get to the ultimate product a little bit faster. >> Yeah, absolutely. And like some of our designers are also using using magic patterns or even other AI prototyping tools like Figma has it Figma make and and so I think it's really just part of the conversation. you know, I'll ping a designer, hey, I was thinking about this and, you know, was thinking maybe we could go in this direction and send them a link and they'll be like, oh, I was, you know, exploring something similar and we'll just trade notes. So, to me, it's a replacement for what I was doing before, which was really hacky Figma mockups and like not so great wireframes. Um, and so I I think it's an extension of that like wireframing hacky Figma prototype process where it just is easier for someone to understand because they can actually click through and see the flow. >> Yeah, it's just more interactive I think is really it might not be higher fidelity, but it's a richer kind of prototype experience than you would get from sort of a flat design. >> Okay, we at least have three successful generations. We can click through >> with with with all AI, you know, sometimes you get errors, but you know, here it says it's like a guided category selection flow. So, we'll click through and see what they did. So, you can see here it's like kind of customizing it a little bit for the category of um of the service. So, I'm going to go back and maybe select another category and see how it's different. So, it's like, you know, kind of customizing some of the tips um in this one. Let's see. I might need to actually select a photo to see what it does. Um, so you can see it's like going through an analysis. You know, this is not using the LLM behind the scenes. So, you can see it's not uh not making sense, but I think the idea here makes sense where it's like, okay, it's going to do this like kind of real time detection. Um, and then in this one, it looks like it's like multiple photos. So you can see here it's you know showing like you know you could um prompt the user to maybe take multiple pictures. I will just click on this to show that you know this is how AI works or sometimes sometimes you get errors and you need to fix them. Um you know usually there's that like shortcut to like try to fix it. Um, if it doesn't work, um, there is also like a debug command within magic patterns, which I found pretty useful, which just tells it to like look through your code, try to come up with what's wrong to fix it. >> Um, let's see if it did fix it. For our listeners that are not wa not are not watching, I will spare you reading the uncaught react errors about um incompatible React versions. But that is what we are looking at right now, which is we are looking at a compatibility issue between 18 and 19. >> Yeah. >> All right. So like all good AI demos, this one did not work. But I do want to say just stepping back what I wanted to just call out is you have demoed for us a completely new way of thinking about product management prototyping and product requirements in a way that is very different than I think what classic product management has looked at. And so you're starting from a kind of example consumer experience first. you're backing into kind of a rough prototype of what could support that experience. You're using a AI prototyping tool, in this instance, magic patterns, to then put that experience in your brand and design guidelines. And then you're using that as a jumping off point to fork and inspire a couple different versions of what that ultimate user experience could look like. And then I'm presuming you're going to take one of these and you're gonna say I think we want to start here for our MVP or our V1 and then that you know you get the team together and then and then that's where you start. And so I think for the product people listening what I like about AI is it's not just multimodal and that you can put any sort of um file type or data type in. It also allows you to approach problems from the front door, the back door, the side door, the window. Like, you know, you can come at your product problems in a much less linear way. And in fact, you can start at the end, go back to the beginning, come to the middle, fork off, go back to the beginning, and reprototype. And it's not expensive, it's fast, and it's interesting. And so I think what you've inspired me to do is actually think a little bit differently about what the starting point of product management could be not just for AI products but for product in general. And then of course you showed some great ways that AI can help with that. >> Yeah, absolutely. Um and I will say yeah to your point you know you can pick which one you like the best um which you think fits your you know where you are um in your in your product journey and your user needs. Um, you can also like if there's one that feels like, hey, this like AI assisted one seems really interesting or this multifoto one seems really interesting, but maybe not like where we're going to go right away, you can fork this design and it will create um a totally separate window and chat for you um of just that variant and then you can just run off with that, you know, maybe on the side um while you're continuing down the original path you were in. >> I I love that. So we have seen your AI powered AIPM process and usually I would bump us to lightning round but part of our lightning round is going to have a couple demos in it. So as my first lightning round question can you do a quick world tour of a couple nonworkreated AI use cases that you think our listeners would really get a lot of value from? >> Yeah absolutely I can share a few personal examples also. Um so um one is you know I have started this um you know talk AI channel that was at Yelp which was actually inspired by a talk AI channel in Lenny's community and um I wanted to create a monthly newsletter that gets sent out that just summarizes all the great discussion and content that was being created there. And so um I'm just going to show an example of how to do that using Lenny's community. Um, and so here I have this um, set of project instructions that say, you know, I'm a community manager writing a weekly newsletter. Um, use these Slack conversations and format them just like the community wisdom newsletter. And then I think what's really cool is I can just come in here and I can say, you know, I want to just make a version of this community ver uh wisdom using this slack chat and I can upload the file of all those slack chats and I did randomize the names or um replace the names for privacy also using GPT. Um, and then you can see here it's going to make a version of that community wisdom newsletter just using those Slack chats and um, reuse that same format. And by using a project, I can, you know, save myself some time on the prompting. >> Great. So, you're copying and pasting um, like a week's worth of Slack conversations. M >> you're putting it into this cloud project which you've been given a um you've given a template and then you're having it generate on a weekly basis or whatever kind of a summary of what's going on in that community and other kind of like content that's being shared. >> Yeah, absolutely. And then you can see, you know, kind of follows that community with some uh format and pulls out what the top threads are. And so you might want to make some edits to this afterwards, but it really, you know, gets a really good first draft that you can then edit. >> Amazing. And you're probably everybody's favorite community member. >> Yeah, it's definitely a lot of fun um to yeah, see what people share. And then I'll show a couple other examples. So, you know, I showed the example of creating the Yelp Assistant and I actually used the same workflow to create this parent pal to explain how artifacts work to my husband and he was really excited about it. He was like, "Hey, like let's try it out with, you know, Tommy where Tommy throws toys down the stairs." So, you know, I did like, you know, my two-year-old um throws toys down the stairs and uh it's some the same kind of artifact where it's powered by Claude's LLM and it's going to ask me some clarifying questions like what's the trigger and it's like always at dinnertime when we are cleaning up. Um and then you can, you know, see how the AI will provide some parenting guidance. And I think the really fun thing for this is that, you know, you can build something that's just really for your own personal use case. Um, and it's a a really fun process to do that. I'll show one other one, which is um my siblings and I like to play this board game, Settlers of Katan. But the bad thing is it kind of takes a long time, especially if people don't go fast. So, I'm working on this Settlers of Katan timer where um I actually have a timer for me and my siblings and both for the setup and the main game play. But this one I actually built in Lovable because my siblings had a lot of feature requests about tracking the future uh you know who who's won over time and having a leaderboard and handicaps and all sorts of other ideas. So, I definitely think it's a lot of fun to prototype with AI for your personal use cases. And I know some PMs are like, "Hey, I really want to work on AI products, but I don't have that opportunity right now." I think the fun thing about these prototyping tools is you can build a use case that's just for you or just for you and a family member. Um, and learn a lot as you're doing it. You just gave me such a good idea because I don't play a lot of board games, but my kids get like 10 to 15 minutes of Minecraft every day, but we only have one >> like uh time timer. Um, and so so I need an iPad where they can like both click their button and have it have it countdown. And then they're also really worried about fairness. So I will also use a uh relational database to store all their time >> and say I promise every week you are getting an equal amount of Minecraft. There is no no lack of fairness and then when they fight about it I'll use your parent pal GBT. >> I love it. Yeah, you can just direct them to check the dashboard. >> Amazing. Okay, last question and then I will get you back to all your prototyping and all your AI building. >> When AI is not listening, other than clicking that debug button in magic patterns, what is your tactic? What do you do? >> I I think that when AI is not working and you've already tried some of the debug um methods, I think it's helpful to actually think about the ways that AI is different than a human. Like often we just get in this chat and we're like, this is just like talking to someone. Um, but when you're hitting the wall, it it helps to like take a step back and be like, "This thing is actually not a human. Like, what could be going wrong?" And think about AI's limitations. And, you know, the ones that I try to keep in mind are it tends to lose context as you go through many different turns. And it has a limited context window. And so, when you start having a really long conversation with AI, sometimes it just goes haywire. And so the um methods I um recommend are if you're doing AI prototyping, you can use that fork or you know a remix to start a new chat with the context of that code and that actually resets the context window. Um so that's a good idea if you're going really far and deep with a prototype. Um and the same thing applies to a chat. Like if it's going haywire and you've had like a hundred back and forths, you can ask the AI to summarize the chat and the context and start a new chat. >> You gave me such a good idea with your last two answers because I am going to prototype a parenting pal for the relationship between me and my a my AI. >> Be like, AI parenting pal, >> my my 4-second old AI is no longer listening to me. What do what do I do? Um, that's that's really great really great feedback. And yes, reminder, AI is not human until the AI overlords take over and then you can be whatever you want. >> All right, Priya, this was such a practical, super useful, inspirational conversation. Where can we find you and how can we be helpful? >> Yeah, you can find me on LinkedIn and then I also have a Substack called almostmagic.substack where I share some prototyping tips and other tips about building AI products. >> Amazing. Well, thank you for sharing and joining How I AI. >> Awesome. Thanks so much for having me. 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