Neha Monga === ​[00:00:00] Neha: And has always been an integrating function. So you bring product together by getting design, engineering, sales, marketing everybody together, and the quantity of product management is all about the decisions you made and how fast you ate them. This involves lot of reading, lot of communication, lot of writing, and if you're not using AI tools to get better at. Each one of these, you're just missing up. If you're a PM who's still taking notes and sending out manually, or you are not using Chat G pd, or whatever tool that you prefer to improve your status reports, decision report, you're just falling behind. Welcome to Launch Pod ai, the show from Log Rocket where we sit down with top product and digital leaders to talk real practical ways. They're using AI on their teams to move faster and be smarter. Today we're talking with Neha Manga, former head of product at Lattice, and has LED product teams at Meta, Amazon, Expedia, and more. In this episode, we'll discuss the AI workflows that are now table stakes for PMs and how to adopt them so you don't fall behind. [00:01:00] How NE ha's team used Figma make and Claw to spin up high fidelity prototypes, gather customer feedback faster and slash validation times with design and engineering and. The PM intern, they built inside Slack to instantly surface insights from their massive customer feedback library. So here's our episode with Neha Monga. Jeff: Hey Neha. Good to see you again. It's been a little bit since we had dinner in Seattle and a whole bunch of folks were there. We got to sit across from each other and you had so many cool AI use cases that your team had been using that had to get you on the show. So thanks for coming. Neha: . Thank you for having me. And there's so much to talk about with ai, how it's changed the way we all work, and kind of a new operating system emerging for. Jeff: The dinner we were at was in, uh, June, I think beginning of June. I already feel like so much has changed, you know, a month and a half later. So. I'm excited to hear about the new things that I've missed that you've done in like literally 30 days. Neha: It's going at neck break speed, right. So, yeah, like I have to keep on it and keep learning and just be [00:02:00] having a very curious mindset at all times. Jeff: Exactly.. And that's, that's hopefully what we're here to do is, you know, the point is at that dinner and, and, you know, everywhere else I've been around the country talking to product leaders like yourself, , that's the big question is how are people actually using this stuff to get more productive, to be smarter, faster. And so that's what we're gonna talk about today. I'm, I'm really excited, you got. Pretty amazing pedigree here, right? Most recently, head of product at Lattice. , Before that, it was, , meta, Amazon, Expedia, Zynga, like you're just checking off great companies, one after another. And given that, I'm excited to see kind of how you found. This stuff is useful. I think in our pre-call we kinda broke it down into two things, right? This far into the game. There is just the kind of like table stakes, gimme use cases and, , you're doing well at this stuff and you've got a few things down pat. Let's maybe just go through like, what does that look like and, and how you kind of operating in those areas where it's just these are the things you have to do to even just keep up at this point. Neha: , I think that is such a great question because many of these AI tools have become like table stakes. If you're not using those tools to actually do your PM [00:03:00] work in a day-to-day basis, you're just falling behind. And the best way to learn AI is actually by using it and seeing how other products are being built. To me, like, you know, PM has always been an integrating function, right? So you bring product, together by getting design engineering. Sales, marketing everybody together and the quality of product management is all about the decisions you make and how fast you make them. This involves a lot of meeting, lot of communication, lot of writing. And if you're not using AI tools to get better at each one of these, you're just missing up this one at a time. Because if you're a PM who's still taking like notes and sending out, right? Like manually, or you are not using like Chad GP or whatever tool that you prefer to improve your. Status reports, decision report outs and all those different things, you're just falling behind. Like it should really not be taking any time. You should be using tools like whether it's your Zoom ai, whether it's your granola or favorite meeting recording tool and making sure everything's being captured and spread out. , So I think that is just the table stakes. You should not be spending as a PM time on doing these things because they [00:04:00] can be so easily and efficiently done by tools that are to us right now. Jeff: What's the tool stack you found that you kind of really like to do the, you know, meeting recording to distill out what needs to get done or what, what did your team, , find useful? Neha: , I think the two answers to this one is definitely like a tool stack that your company uses. And fortunately, companies are moving fast too, right? So if you look at, uh, zoom, they have Zoom AI that can take notes and things like that. So try and use these AI functionality that already exists in your company, right? But sometimes the companies don't have those and it's still like valuable. For example. I personally find it valuable even for my personal meetings. I do a lot of like advising and coaching CEOs and things like that, and I use granola as a tool, which is not something that kind of intrusive meeting another attendee that's joining your meeting, things like that, and takes really high quality notes. So research, find the best tools that you can use, but they're also like existing tools like Zoom, et cetera, that already have these built in and making sure you are using [00:05:00] them and getting your IT teams to enable these for you, right? And working with the manager to make sure , you have access to these. Jeff: Yeah, I think that you, you nail, we haven't touched on yet on this show is like you have this speed of, of moving forward and wanting to try things and at the same time we've run into enough companies where they have all this kind of worry about is data gonna get into the wrong place? Is there sensitive stuff can be exposed? There's a balance there. But hopefully companies are working in a good way to allow people to the freedom to move and to try. But also you have to be aware of, of what you know, you might be exposing on your end. Like if you are. Working at a medical software company and you have patient records that you are emailing back and forth and, and dealing with, maybe be a little bit more aware about using some kind of AI email tool if you are just kind of, doing like software for some kind of B2B, you know, normal thing, probably less sensitive. Right. I haven't actually heard of granola. What is that one? I, I assume it's, you know, to do with recording phone calls, Neha: Yeah. So GR is, uh, like it actually records everything, which is what I love about it. It can be like a zoom. Google meet meetings [00:06:00] or sometimes just phone calls, right? So it does recording across everything. Does not join, gives me the transcript. If I don't like the summarization, sometimes I can copy paste the transcripts between chat GD and I think the point you make around, sensitive data and data protection, I think that's super pertinent. Actually kind of reminds me of time that we were using mobile phones. And corporations will just block email on iPhone or Android, what have you. But then in the end they realize they just need to catch up on that, right? Because this is what's being used and it's somewhat some way of like consumerization of it and like trends on people gonna use AI and how can IT departments get ahead of that. Fortunately, the difference that I'm seeing with the AI wave versus a mobile wave right now. Enterprises really wanna use, like something like CEOs and companies like top Down, they want their people to use these tools and they're in some ways, you know, getting the IT teams, recruitment teams to like move fast on these things and try a bunch of different things out. Jeff: This is one thing that I've personally struggled with and [00:07:00] I think lots of people have, is it's easy to record the calls. There's a million solutions now to transcribe them, whether it's in the tool or outside of it. Um, and then you can put it into like a chat GPT or you can put it into Gemini or whatever your thing is that you want to parse it with to do all the post stuff. But that, that also takes time. Like my dream is just, I do a phone call. It magically, you know, is transcribed. It goes to. Wherever I want it. All the salient things I like done on my phone. Calls are done and I just magically get out. Like things are outta my to-do list. Someone gets shot an email, like, Hey, here's what we went through. , How did you kinda work that workflow, on your end when you were kind of going through calls, whether you were advising or, or at Lattice or other places? Neha: You know, you are absolutely right. Like, you know, this is where like, you know, all these point solutions are emerging, but they don't work together really well, right? For example, Gmail has, Gemini works beautifully in Gemini, like, you know, uh, in Gmail context. Same thing, zoom is amazing. Like it actually notes down like every single thing, tags, people, and , can even create a agenda for next meeting. But right [00:08:00] now, stitching these tools together is very painful, right? but even then it saves you just so much time. Like you can be fully present in a meeting without having to worry about like, oh, let me write this down, or. Who's actually having list this, right? And remembering things. my dream is where like there's a little bit more consolidation, the tool set so that, , I don't even have to pay for like, oh, here's my meeting recording tool. Here's my meeting setup tool. Here's my. Gmail email where I purchase Gemini, and then I use Clark for like, you know, prototyping. And then I also have charge GBD because I love custom GBDs. And this kind of really adds up and I would love to see where things start consolidating a little bit more. And people, and products become more end to end in achieving scenarios versus the, point solutions. So that's still useful, but it'll be great to have something more. Jeff: It definitely seems like there's gotta be a network effect somewhere here, because all of these workflows are great if you have like this agent, this agent, this agent, you know, and every, every every company on Earth is announcing that their agent right now, or the [00:09:00] AI strategy or something like that. And they're all really good. Like I, I've, I've seen, you know, there's obviously some bad ones, but there's a lot of really good solutions coming out. But the thing we always run into like. At Log Rocket, right? Like what we do is, uh, digital experience analytics. We have session replay analytics, and we launched like 18 months ago, the capability to have agents watch your sessions and kind of pull out what are the actual areas of user friction and how do you kind of find what's really important versus false positives? that was good, but the problem with that was. There was a, just so much other data in the product stack that people had to deal with still, right? Like you had, I don't even just run through, you had AB testing tools, you had user feedback in maybe the app store or G two or, , interviews. You'd done, you know, UX interviews, you had,, linear tickets or Jira tickets or intercom tickets, you know, support on the other end. And what we realized was. , The best path forward is, this world where basically software kind of heals itself or, or helps you run itself. And so that's, you know, the, the thing we just announced pulls a lot of that together. It will go into your [00:10:00] interviews, it'll go into your app store reviews. It'll go into your user feedback and support tickets and all the way back to your linear or Jira tickets to see what launched and pull it all together so that you can write the equivalent of, I wanna do my phone call. Have the notes sent, the email sent, the follow-up sent. I wanna have stuff added to my task list. It will kind of do all those things. And that's, I think, a future where there's probably going to be value accruing in certain pockets.. Neha: I remember when I was a kind of more early career PM we would launch something, we look at the actual reviews, we'd look at like,, all the usage data in a different dashboard, engineering dashboard than the product dashboard and like trying to like connect the dots and it'll take so much time what you're proposing, the blog rocket, that's just sounds magical. Jeff: I think that's the model going to be in a lot of places is right, either there's going be a central part of an ecosystem and you're gonna have all these other tools around it, I feel like. Or you're gonna just have kind of one thing, acquire a bunch of other pieces. I'm curious to see how that part shakes out. Neha: Yeah. Or like the whole MCP server thing, right? Like I'm actually really excited about the [00:11:00] connectors MCPA two A protocols. Obviously the adoption's not where it is, but I would love to like, okay, could we just create like these different like contact servers that we can easily Jeff: Exactly. One thing I do wanna talk about is for the longest time PMs would talk to users and talk to prospects and they would kinda come up with the ideas and the, the use cases. Then you'd have to go and sit down with a, a UX designer, a product designer, maybe an engineer, maybe a team of engineers, and like just translate the needs. And what you've talked about is being able to move farther down that stack and coming forward with, you know, prototypes that you're able to generate from these AI coding tools to really showcase. What this product could be and, and help kinda move it down faster and, you know, help companies move faster. , Can you maybe talk with us a little bit about what that looked like and, and how you kind of got the team there and, and how you guys use those tools? Neha: I think there are two aspects to it. One is like, you know, what tools exist, what can we even do, right? Like understanding the capability. And second is like, how does this become a habit for the team? And they don't feel like, oh, it's like, it's not for me, or it's like, alien, no, I'm not gonna do that, et cetera. Right? So I think there's definitely like, [00:12:00] uh, the habit for that needs to happen like I mentioned earlier, the, you know how PMs are judged is by the quality and the speed of decision making, right? When it comes to product stuff and with AI that is so much faster, like you said, like, Hey, like will I have an idea? Then maybe we'll work that to put some pictures together, maybe put in front of the customers, or worse, like you try to describe it to customers and they can't imagine this thing, but now you can go to like CLO and like, okay. I'm gonna like give you like three or four prongs and within three or four prongs I can have something I can put in front of customer and a picture stays a thousand words. And they can understand, oh, so this is my business data. This is hard to look, here's what I can manipulate or like, you know, work through. And I think that's just magical being able to do this. Like really quick discussions. Like imagine I'm preparing for a customer meeting. I can spend just less than half an hour to even clip something that I can show, throw it in front of them and get quick feedback, validation. That validation cycle is just getting so much more accelerated and eventually like helping, making [00:13:00] like better decisions for the product teams. I think that is magical in my opinion, right? That's game changing, how fast it can happen. Actually just, just a few days ago I was trying how Figma ISS make tool. So they announced that maybe a month ago in beta if you use Figma as an organization, like you have your design, , style guide and things like that. Now it actually really does look like your product, which is not something you can do easily in cloud, for example. Right? . Even more easier for customers to see themselves using a certain thing and being able to tell like, no, not this, this, how we envision this, uh, that whole validation cycle is so much faster. So I think that's what I definitely want like PMs to do. And it's also great communication tool. You know, a lot of times teams are remote, right? So you're sitting in front of the computer trying to like, you know, work with the designer, engineer, Hey, this is what I'm thinking. Like for example, we had this like analytics. Page. And what we wanted to do was like, can AI show the things that are like, you know, not the, not the norm, right? Like what is the abnormality in the data? And we are discussing, you know, when you were in the same room [00:14:00] and everybody was in person, you like, go to a conference room, draw things out, ugly drawings. I, I have the most ugly drawings, so I'm grateful for these tools actually. And now I can like share screen like, oh, let's just like, like, you know, create this thing like super fast and like this is how I want interaction. That saves hours for even like internal communication of the teams. And another game changer in terms of like accelerating. Not just the validation process, but also internal communication process because seeing is so much faster than, you know, just talking about describing things. I wonder if PR is gonna become just prototypes that PM just sent over. Jeff: I don't know if I know a single person who would not be happy about that, like not having to write PDs. But it seems like so far any of those tools, you still have to communicate pretty strong to it. Being able to, rather than have someone read this big memo of requirements and use case and like visualize it, having it, there is, like you said, it's just so much more powerful and you can really tell the story and show the story versus just kinda like talk around the story a little bit. Neha: That's exactly right. I actually reminds you of funny joke when I was, I started my career in Microsoft. [00:15:00] And they, there were a couple of PMs who were really, uh, notorious for doing this. They will take a print out of their PRD 50 pages, no less, and like literally take it to Joe and like, put a note there, like, have you read this yet? So before the engineers come to the office, like, have you read the PRD? Because the answer that you're looking for is like a PE. It's really hard to consume information from a 50 page document, but here you can see exactly, the kind of the behavior and like the storytelling is so much easier to consume and visualize. Jeff: The solution to writing 50 page PRDs is kinda the same as the solution to dealing 50 page PRDs. 'cause now you can just put it into Google Notebook, LM and just ask like any question of a 50 page doc and I, I use that thing religiously. That thing is great. I think Figma you brought up is an interesting one there because that is just a company that like continues to find ways to, to be incredibly powerful, right? They explosive growth into that, you know, almost acquisition by, um, by Adobe, that whole thing, torpedo, they came back and basically just said. Watch this, Watch what we do now [00:16:00] and continue to grow in value. Then everyone said, oh, you know, AI's gonna kill this thing, all the, you know, ai, uh, prototyping and stuff, goodbye to Figma. And then they launched make, and it's right back in there. And they just continue to show they, they understand that audience and know what they're doing. You can't tool your way out of a problem. But also, if you have great people and, and smart people in the drive to solve the problem and you understand the problem, tech is not gonna kill you. There's ways to make it a part of what you're doing, but you have to be smart and just get ahead of it. Neha: That is exactly right and they're, they're doing. Killer stuff. By the way, I remember like maybe two years ago, they had two products, right? Figma Design and Fig Fig Jam. And now if you go to their website, there are eight products. Four of them are like within the last six months in beta, right? , And they're all like, AI forward, like Figma Dev, Figma make, I'm just blown away by the speed of innovation they're driving. Like they're, they're back in a big way. And, , good luck for the IPO too. Seems like they're setting themselves up really well with that. Jeff: Yeah, I would, I would not count out Dylan and that team ever. One piece I do wanna hit on is . There is this idea of prototyping and all these things you can push forward, , to make [00:17:00] things more real. I talked to of CPOA couple weeks back who was saying that he actually had product people shipping code into production. We kinda got inspired by that. So now, like you've worked with analytics tools and all those things where you have to kind of have your engineering team build, , the custom event into the software for you. Right. It's a pain in the butt and like you forget something, you gotta wait a sprint or two sprints and you're like a month out before you get that, that metric you want. , We figured out how to integrate and just made a little bot in, slack that you can just, , ask and it will, it integrates with our cursor, , instance and it will build the custom event for you, , and just release it. And so. We've, we've never really operated off of custom events too heavily where we're mostly auto capture, but you can use it for precision in some cases, and it's been great. I've used it a couple times now I have like three little tiny lines of code in the log rocket production instance, so I'm gonna, I'm gonna claim that until the day I die. Neha: No, that's awesome. Amazing. Yeah, like I do think about like, can PM instead of writing like bugs into Jira and they're like, okay, go fix this. I'm actually a bigger fan of self-giving code. [00:18:00] Like, you know, it receives Jira from whosoever, but it's customer success, but is PM and like self use though. And I wonder if. Develop. Actually don't want kids to write anything, even if it's AI generated, they just want AI to write it, which is fair too, but like, this is amazing. Like, you know, it's kind of democratizing like, you know, don't have to like, uh, for smaller things, faster execution. Could you just like, fix things directly versus like the whole Jira process, prioritization, Jeff: Oh yeah. Neha: grooming, all good stuff. Jeff: I feel like there's a lot of cases where you have this world where everyone thinks it's like, engineers don't like product managers and, and they're always like button heads that's, you know, that's true some places. Okay. That that is true in some places. Um, but that's also not like in a lot of places they agree on the things they don't like. Right. Like no one wants to do the little paper cut fixes to code. No one wants to spend their time racking up. Little one point. You know, tickets to just fix a bunch of, a bunch of crap that accidentally made it through unit testing or, all the testing protocols and stuff like that. You guys launched. I, I think one of the cooler things I've ever heard of, uh, in Slack, and it's such a great way to do it [00:19:00] because people are already there. Like one of the big problems in general, I feel like with AI has been, there's all these tools, but you have to change all your workflows and there's so much inertia to how people operate. If you can get the productivity and the capability where people already are, you can create magic and , you and the team built basically like a PM intern into your slack. Let's talk about that. 'cause , I am still like my hair is still blown back by this Neha: yeah, no, this is actually one of the most powerful and most fun things ever. I love custom GPEs, right? So I write my custom GPEs for everything, and I was like, Hey, we have this amazing data. Our sales team captures this amazing data in Salesforce. We have the customer success team, we have these gong calls, and like all these different things. Can we just throw them and create custom GBT to like, just give us data about, oh, what are the biggest requests in analytics from our. Things like that, being able to answer some very simple questions, so I created. Custom GP that was like PM intern. Super, super, super useful. Then my head of product operations came up with an even better idea. I was like, Neha, could we, uh, do [00:20:00] something like, uh, and I think he used Zapier to hook this up in a custom slack channel where you can just go in like, Hey, you know, PM intern. Like, what are the biggest requests from X, Y, and z? So and so customer just said this. , Are other customers also asking the same feature or you know, we have a product called engagement. Like for example, in engagement, were the top customer requests that we get from smb, small, medium business customer. that has been game changing. It's such a powerful way of Democrat data and putting this into your fingertips and making like really good decisions. And especially when it comes to roadmap planning or even in between, right? Like you get some customer escalation. They're like, Hey, we want this feature. Like, oh, who else wants this feature? Let's learn more about this. And instead of waiting through five different data sources. all in one place, super accessible to anybody in the organization. I think that is very, very powerful. Obviously, you know, still have to use a lot of spec sheets, for example. It doesn't connect directly to Salesforce or it doesn't connect to like some of the systems we use. So we are doing some kind of automated Exports out to like a folder [00:21:00] from which GBD reads from, and this is where like, I love like MCP connectors, all those things to be coming true so that like all this content can be pulled in from multiple sources and create this very useful picture for that is roadmap, planning, big bets, small fixes, and making it super accessible to everybody. Jeff: OpenAI just launched a chat GBT agent, right? Maybe very soon. It will just be the things that you can't get through some native API is just ask the agent to go fetch. Specific data you want and it, it adds 30 seconds, but it, it's fine. It's way faster than what the old world was. You had, like you said, five, six tools that you were manually going through, pulling data out, correlating it, figuring out is it relevant, is it not? Does this support this, you know, thesis and, and do these match up and, you know, God help you. If you found a difference between data across a few tools, like now you Neha: That is true. Jeff: again. Neha: One of the companies I'm advising right now, they're also very big in Latin America, and some of their customer feedback comes in a different language. Sometimes Portuguese, sometimes Spanish. It's like, I don't understand this and I wanna see some [00:22:00] of these things. And just having like, oh, this can be automatically translated. I don't have to worry about pasting data and Google Translate or some of the tool and kind of figure it out. I think it's just so fast and Jeff: Yeah, Neha: the way it's emerging. Jeff: that is I, I think a drastically underrated. advantage that AI has, has just quietly solved and no one talks about there's things that would've been revolutionary on their own that could completely buried under the bigger things that they do. It's, it's nuts. . So looking at the team and using this, agent how, how did that kind of play out? Were there surprising areas where , that showed value? Neha: they were not surprises because the product team has been really good at like, just looking at 15 data sources. But I also know if I asked like my principal be, I'm like, Hey, I'm looking at this. Can you gimme a report out? And, and the amount of time she used to take, for all the right reasons to pull everything together, it was just massive. And now it's just at finger tips. Like, it's just like within seconds. That has been just a game changer. And the best part is it's not just a product team, right? Anyone can use it. So it's like a marketer and you know, they are, or like a salesperson trying [00:23:00] to go into a customer meeting and they wanna pull out a few things. I think that is like, you know, just democratizing access to product feedback from everyone. My goal is like just making sure everybody in the organization is think very product minded and customer minded. So I think this definitely helps achieve that. Jeff: We had a woman named Sierra on who is over at Apartment List, and she was talking about kind of this exact use case where they had hooked. All these kinda feedback sources together. And they were running interviews through, you know, parsing in GPT and using that to kind of make sure they were kind of lining it up with all the feedback and all the details they had. And one output that they had from that was they realized that kinda what they were really gungho on building, it wasn't that there wasn't product market fit. There was like everyone, everyone really liked the feature capability. It was just. It existed like everywhere. And there wasn't really a big demand for it. 'cause it, it was really strong, strongly done elsewhere and it didn't fit in the model of kind of what they wanted to be as a company. , And they were able to, not just their speed of going through this data, but they were actually able to check the humans and provide the kinda a second set of eyes to [00:24:00] make sure and, and rationalize their thought. And they, they saved a lot of time on potentially working on something that ultimately would've probably been a, you know, bad road to go down. Neha: Yeah, that is true, right? Because I think humans, we suffer from emotions, which is also strength, but also sometimes a weakness and having a more data driven approach, which, uh, I can kind of second set of files, like you said. , Providing the perspective can be Jeff: Yeah. so what went into building, the intern here? Like was it just uploading a ton of all your kinda customer context, uh, from your end or was there more to it where you were kinda training it on what the output looked like? How complex was it to actually build what sounds like a really, really useful tool? Neha: It's actually super simple and I'm surprised like first of all, that it's very simple. Secondly, people still don't build enough of their like personal GPTs to make themselves more productive. It's just giving the GPD like instructions on like. Hey, here's, uh, here the inputs I'm giving you. Here's the output I want. This is what I want. And funny enough, adding things like, don't hallucinate, don't give answers, don't make up stuff, works really well and [00:25:00] testing it because it's almost like using a rag model. So it's not like training, training. It is kind of like retrieving from the source of truth, which is what I want actually, and actually turning off like, Hey, don't search internet. Search only this data. This is the truth. The search function is also very useful if you're doing deeper market research with some folks do, but not in conjunction with this was less than half an hour to set this up. And I don't know how much time they , used using Xavier to kind of hook this up in, slack. I remember him asking like, Hey, Neha, can I, can you shoot access with me and I'm gonna, uh, hook it up? I was like, okay. That was morning. And by after he's like, oh, by the way, he's a Slack channel I created. I said, okay, great. Jeff: the one inquiry that you were talking about earlier that your director did into like how something went and having to go across five or six different tools, you probably saved enough time in just that one, research mission to pay back all the time it took to like build it, it sounds like. Neha: Yeah. Or not even like, it'll not even take us like one day to ever like look through like five different data sources and Jeff: Right. Neha: yeah. Jeff: But that's, that's the thing is it's quick [00:26:00] to do that. But you've done kind of all the previous work we've talked about to understand how this stuff works I think this came up once I was talking to people before and someone's asking like, well, how do you get going? How do you get started? And it's literally just. Pick a problem you wanna solve. That sounds good, or even doesn't sound good. Just try. Just try and solve things. Just everything you do. Take a little bit of time and go like, could I, how could I solve this using ai? Or how do I approach this with that? And try and it's gonna suck the first couple times. Like, you're not gonna have good outputs the first couple times, but keep at it and you'll just get better at it, right? Neha: That is exactly right. So, funny enough, like I, I do not wanna underestimate how hard it can is for some people. So I advise this, investor firm and, , the CEO of the company, like I was talking, was like, Hey, why don't you just write a GPD for this? It's like, oh, how do I write this? I showed him and he's like, so what instructions can I give it? And it was just so hard for some folks to fathom, like how to tell AI to do what they wanted to do. Uh, so I think that's just been, that was mind blowing for me to see like, wow. To me this tool is so simple and yet some folks find very hard to like [00:27:00] fathom imagine how they can use it. think that has been kind of a trend, like, you know, where like people over time will start learning how they can tell AI to do the things they wanna do, um, and, uh, just get used to this fact. Jeff: There's so much cool stuff that you've touched on and this is just such a deep topic and I clearly, I kind of enjoy talking about it. Thank you Neha for coming on. If people wanna reach out and, you know, pick your brain or ask questions about anything a little bit deeper, uh, what's the best spot? Is, is LinkedIn good or is there a better spot? Neha: Yeah, LinkedIn is amazing. Yeah, that's a great place to start with and like just drop me a note. Or like my email address, personal email address is neha gmail. And I, I don't mind people reaching out to me directly as well. And honestly, Jeff, thank you for this wonderful conversation. Jeff: . Thank you so much for coming on. It's great. Have a good one. Let's keep in touch and, uh, we'll talk soon. Thank you so much. Neha: Yeah. All Jeff: Bye. Neha: Bye.