I gave Claude Code our entire codebase. Our customers noticed. | Al Chen (Galileo)

[00:00] The minute I realized I couldn't really do my job was when I was trying to reference our public documentation and trying to provide an answer. It just still wasn't coming up with an answer that my customers were looking for. They don't want the docs answer. They want the step-by-step answer of how all these services cascade together. What I realized is that I can actually pull all of these repos into my VS Code and I can now use Cloud Code to ask our entire code base questions. Did you [00:30] Yeah, yeah, I'm opening up the script right now. It's like, what, 16 lines? Didn't have to write this. I just said, help me figure out a way to pull the latest main branches into my local repos. [00:38] The reality is we can now all live in a little bit more chaos because the AI navigates all that information for us across systems, right? So you can be in your code querying Confluence. We'll find the information. You have to be less precious about where and how you store the information. Throw it into Confluence, throw it into Notion, throw it into Slack, whatever. That ends up being context you can provide to Claude when you are trying to ask it a question about a customer or about your code base. [01:08] every time it answers a question correctly. You gotta split your quota with Claude code. - Yeah, it gives you better answers the more bucks you give it or something. - Coin-operated Claude, that's gonna be my new skill.

[01:22] Welcome back to How I AI. I'm Claire Vo, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today, we have an episode all about harnessing your code to make your customers experience way better. [01:36] Al Chen, who's on the field engineering team at Galileo, shows us how he uses their 15 repositories [01:42] and Claude code to answer every nuanced customer question [01:46] that comes across his desk and use that to make the entire customer base and his entire team a lot happier. [01:52] Let's get to it. [01:53] This episode is brought to you by Orcus, the company behind Open Source Conductor, which powers complex workflows and process orchestration for modern enterprise apps and agentic workflows. Legacy business process automation tools are breaking down. Siloed low-code platforms, outdated process management systems, and disconnected API management tools weren't built for today's AI-powered world. Orcus changes that. [02:23] AI and systems together in real time. [02:26] It's not just about tasks. It's about orchestrating everything. [02:30] APIs, microservices, data pipelines, human-in-the-loop actions, and even autonomous agents. So build, test, and debug complex workflows with ease, all while maintaining enterprise-grade security, compliance, and observability. [02:44] Orcus [02:45] Orchestrate the future of work. Learn more and start building at orcas.io. [02:51] Al, thanks for joining How IAI. I am really excited about this episode because we've seen

[02:58] a lot about using your code as documentation. You know, we've heard engineers saying, [03:05] Docs and code should be in the repo, product managers saying code can now be my documentation for internally facing assets or as I help draft PRDs. But you're going to show us how you can use code as an asset to create customer facing things and solve customer facing problems. [03:24] Tell me what? [03:26] What problem were you facing when you decided, I'm just going to clone the repo and fire up quad code and solve some of these problems myself? [03:33] Sure. So working at Galileo on the field engineering team, I'm on the front lines in terms of working with our enterprise customers. [03:41] who are typically developers themselves and asking very [03:45] in-depth technical questions. And the minute I realized I couldn't really do my job was when I was trying to reference our public documentation and trying to provide an answer to [03:56] to my customers, even using Cloud Code or ChattapT or whatever and trying to take all these different help docs and trying to come up with the answer, it just still wasn't coming up with the answer that my customers were looking for. [04:13] I... [04:14] I'm not an engineer, I've never held an engineering role, but I think I know enough to just be dangerous. [04:22] I realized that [04:24] Our product, Galileo's product, we're an observability tool for AI applications.

[04:29] If you look at this image here, I'm showing an architecture diagram, high level of all the different services that make up our platform. This is all like back-end images that customers have to deploy onto their Kubernetes cluster. And I realized that all these different services like UI, API, AuthZ comment, [04:51] they are all individual repos within our galeo repo we're not a monorepo we have multiple different repos and so what i realized is that i can actually pull all these repos [05:02] into my VS Code. Initially it was just more for me to like, I wanted to understand how our code works and how our code is structured. But then when I threw it all into VS Code, which looks like here, you notice along the left hand side, I'm on VS Code now. [05:18] And most of these directories correspond to one of those [05:23] services within our architecture. So one repo corresponds to one service. [05:29] And by having all of these repos in my VS Code, I can now use Cloud Code to ask our entire codebase questions directly. [05:38] that are not [05:40] answerable by our public documentation. And so sometimes I'll get really questions about, well, how does this [05:47] future actually work. [05:48] And so I'll ask Cloud Code. [05:50] look into the API repo, look into the AuthZ repo, and help me come up with an answer. If you can't find the answer, reference other repos within my directory, my root directory.

[06:01] and help me figure out the answer. [06:03] And so that's the key on Mock was when I figured out I could get way more in depth [06:08] way more technical, [06:10] And at the same time, myself, I can learn how our code base works. And how this has really helped me is, [06:17] I don't have to constantly ping our team engineering channel with, "Hey, what's the answer to this question? The customer just ping me about this." [06:24] And you can imagine engineers being really frustrated when I'm trying to [06:28] you know, post these questions and then the customer asks me a follow-up and then I'm, I'm posting a follow-up in the Slack thread. So, [06:34] I'm sure many of you who are working on the front lines of customers understand how that feels. [06:38] But I've basically reduced all of that almost down to zero by pulling all these repos into my local VS code. [06:46] I'm really empathetic to this problem because I used to work at LaunchDarkly, leading product in engineering, very technical product. We, too, had an architecture diagram that looked very similar to that. And [06:58] Again, as a more of a, you know, people think that CPOs, chief product officers or CTOs are internal facing. No, no, no. We're salespeople. We're always salespeople. You trot us out and you put us in front of the customer or you put us in front of the prospect to answer the technical questions. And we had a diagram like that. And I would constantly get these very detailed questions that required very detailed answers like, how does your caching work?

[07:28] docs answer. But when you're sitting with, you know, an architect in the room or somebody highly technical, they don't want the docs answer. They want the step-by-step answer of how all these services cascade together to build... [07:41] A resilient caching mechanism, for example. And I just think how powerful is it to be in a meeting or in an email back and forth and not just sort of give this high level, but be able to query the current code base and really understand at a detailed level how it works. And I think current is very important because... [08:03] You know what I know... [08:05] this is always evolving over time. So even if you got the answer right, you know, a month ago, maybe your team shipped an update or maybe, you know, that method is actually out of date or the docs are a little bit out of date. And so I do think because the code base is, [08:20] you know, at least your main branch is always the source of truth. It becomes a really reliable, you know, context set for you to answer questions about how the product operates. [08:32] Yeah, and to quickly address your comment about how your code is obviously always evolving, I mean, we're pushing out multiple features per day, multiple releases, and one thing I've done [08:43] wrote this with cloud code is I have this script at my root directory that says like I just do something called pull all. [08:50] And I'm not sure if this is how other people do it, but it just pulls [08:54] the main branch into my repo for all the repos in my root directory.

[08:59] So if I do this every day, I kind of get the latest code. [09:01] across all these directories on the left-hand side of my VS code. [09:06] So the alternative, which I was doing before for a few weeks, and I realized this is just asinine that I'm doing this, is doing git pull orange and main on every single directory. And it was just not scalable because there's now 15 different repos I have to pull the latest from. So that's kind of how I solved the... [09:26] code base is always evolving problem to make sure that I'm always getting the most up-to-date information for my customers. And I have to ask, did you just say, cloud code, write me a script that get polls, all these? Oh, yeah. Yeah. I have no idea. This is this. I'm opening up the script right now in my. [09:42] VS Code, and it's like, what, 16 lines? I haven't... [09:47] Didn't have to write this. I just said, help me figure out a way to pull the latest main branches into my local repos. And it just didn't like one shot. Yeah. The other thing I want to call out for folks as I'm looking at your screen is I don't think people use this trick enough. [10:01] which is in VS Code, in Cursor, in whatever your IDE is, loading a project at the multi-repo level as opposed to at the individual repo level, if you're trying to answer questions across the product, is really important. So, you know, there's some like context bloat stuff that can come into sort of querying across projects. [10:24] all those repos and all those files. But it would be very painful if you had to go into each of these repos one by one.

[10:31] And like query and then go into the other one and query. And so I like this idea of opening them all jointly in your IDE so that when you're querying it with Claude code or you're querying it, you know, with something like cursor code. [10:46] it can have, it can go across, can traverse across repos and really give you [10:52] highly contextualized answers. [10:54] Yeah, our code basis happens to be in multiple repos, but I just pulled them all into this giant Galileo directory here. And so everything is at the same parent. But if you're in a monorepo, it could be... [11:07] Yeah, actually, I don't know how this would work with a monorepo because I've never done it with monorepo with Cloud Code. But at least for us, I guess, this is how it works. Well, I have many monorepos. And yeah, you just open it at the right. I would say my advice to folks is. [11:21] open Claude code or open, you know, your IDE. [11:26] at the right level and sometimes it's narrow and sometimes you need to go up a directory and I think really thinking about that and you can even do that contextualize to the problem you're trying to solve right and and doing that I think is really helpful can you show us just using Cloud Code what kind of question you could answer with this code context [11:47] I will give you an example of... [11:51] I guess I'm a big believer in using shortcuts too. So I use a bunch of custom, cloud code custom commands to help me do stuff. So one thing I do a lot is helping my customers deploy

[12:05] Galileo into their VPC. So I have a custom command called DPL, which is [12:11] It actually references our, the first thing it does is it looks at our confluence because we have a whole bunch of [12:17] Confluence pages about how to deploy into Kubernetes using our different images and stuff like that. So I'll say DPL, my customer [12:25] cannot [12:27] use CRDs and [12:30] They are using Google Secrets Manager and want to deploy the wizard image [12:42] give me a step-by-step process on how to do it. This is actually not a super presentive query because they're way more detailed than this and I provide a lot more context. But [12:56] I want Cloud Code to focus on [12:59] looking at Confluence first, because I know that we have a whole bunch of deployment stuff there. And then from there... [13:05] If they can't find the answer, it will go off into all the different repos along the left-hand side of my cloud code VS code. [13:11] to find the answer. So right now it's just using the Lassie and MCP to pull information from Confluence and then marries that with our code base to answer a very in-depth question. [13:22] deployment question. The one thing, I'm not sure if we should talk about this now, but I started doing this in Confluence where we have a [13:32] we call it a customer quirks page. These are all kinds of, all of our enterprise customers, you typically have air-gapped environments, so they have all these security measures, and we have to abide by them when we deploy the product into their environment. And so I literally have a...

[13:49] page that looks exactly like this where I have the customer's name at the top level and then a bunch of bullet points with like, you know, here are some things about how they store their secrets. Here's how they do namespaces. You know, here's how they handle side cars and services, service, service, service encryption. [14:06] things I know nothing about, but as I'm meeting with my customers, I'm putting this all into this one Confluence page, this ever-growing Confluence page. And then this is [14:15] is actually one of the core pages [14:18] that goes into this DPL custom command, which is look at the customer quirks page. If I'm mentioning a customer that's on that page, look at all their quirks. [14:28] And then in the response from Cloud, it's highly customized, highly tailored to their environment. Because I've seen from working with our DevOps team that we can provide a generic answer about Kubernetes or about ClickHouse or about whatever. [14:44] for the customer, but it's like something you can just find online by Googling or using AI. But when it's tailored to specific security requirements, [14:54] and deployment requirements, it's way more effective and [14:58] This gives the customer more trust that we know what we're doing, essentially. What I love about what you showed here, which is, you know, kind of combining the repository with the Confluence MCP and then both like team generated general documentation as well as you generated like. [15:15] micro documentation at the customer level is I've heard so often in my 20 years in enterprise SaaS like

[15:22] what is the source of truth for this information? Like, I'm sure you've heard this too. Like, what's the source of truth for how XYZ works? Or what's the source of truth for this customer? [15:30] And people spend so much time like, you know, pruning these confluence gardens and organizing their slack channels and trying to get people to, you know, get naming conventions. Right. And like the reality is we can now all live in a little bit more chaos because the AI navigates all that information. [15:49] for us across systems, right? So you can be in your code, [15:53] querying confluence [15:56] It will find, you can kind of point it in the right direction. It will find the information. You have to be less precious about... [16:02] where and how you store the information, bullet point list of quirks, you know, like really official docs, whatever, it doesn't matter. Because AI is just so much more effective at traversing all that information and pulling it in and making it actionable. [16:15] for you. And I don't think that's anything like any human was really proud that they were good at. They're like, I'm really good at finding like the right Confluence doc. That was never, never the value add. [16:25] Yeah, I think even if it's as simple as, "Hey, you came across a really great answer in Slack, like in a really engaging Slack thread." [16:34] Throw that into a conference page. [16:36] or, or, [16:37] save that Slack thread because I also use the Slack MCP to be able to summarize threads. So if you have like just some random like this ongoing stream of cautious system documents you want to [16:48] have Claude Code scan [16:50] I would just say throw into Confluence, throw into Notion, throw it into Slack, whatever,

[16:55] that ends up being context you can provide to Claude when you are trying to ask it a question about [17:01] a customer or about your code base. Well, and the other thing, and this is maybe going back to how I introduced this episode, which is people use AI so much to [17:11] compete on the field of the product and engineering velocity. And what I mean by that is like, we're all using cloud code to ship more product, we're all using AI and codecs to build, you know, better user experiences or more resilient backends or any of that stuff. [17:26] But there's also a completely different competitive field, which is how you show up in your relationships with your customers. And I think, you know, what you're showing is you can actually use AI to. [17:37] to invest and compete on customer experience. And, you know, my hypothesis is when your very complex enterprise customers have you show up and you don't just say like, here are our general docs to deploy this, [17:52] And instead you say, I heard you. I understand what your needs are. And here are your custom docs on how you specifically need to deploy this. And I've already pre-thought about all the problems you've already told me about. You know, just looking like in a competitive sense. [18:09] That's got to come across as a much more enjoyable customer experience on the receiving end and allows you to position yourself not just as great product, but as a great team that's going to service your customers well. [18:22] I hope so. I think my customers are hopefully enjoying the answers I provide and the in-depthness that I provide. I've thought about taking this to the extreme, which is,

[18:33] I have certain customers who are like, [18:36] you know, very in the weeds, they want to know things like right at this very second. And I'm literally taking their question and then just like saying, my customer then asked me this. [18:46] because they can't see your code, but me, Al, I can see the code. [18:50] Help me get the answer. And so if I take that to the logical conclusion, it's like, why can't we just share the [18:57] our repos with the customer, because then they can start querying our repos directly to get the answers they need instead of me as kind of like the quote unquote middleman. [19:06] And [19:07] issues that our code is proprietary and all that kind of stuff. But I have seen, there's actually a case study from laying chain. And since a lot of laying chains, they're [19:16] um repos are you know it's open source like their support [19:21] agent bot actually does a lot of things I do, but it [19:25] is able to query all the public open source repos and [19:29] Any of you out there who are trying to use Langchain or LangGraph, you can just pull all those repos down to your local machine and then ask questions, of course, using CloudCode or Cursor or whatever. But I've gone through that thought experiment of like, [19:43] I'm still kind of a bottleneck in terms of answering my customers' questions because [19:48] I kind of like hold the keys to our code, but if they somehow had a sanitized [19:53] version of it, then maybe they could [19:55] just self-answer their questions too, because they're also all using VS Code and Cursor and Clawed too, but they just don't happen to have our...

[20:04] you know, [20:04] proprietary code base. Yeah, I was I was gonna ask you, are you worried that like the Albot is coming and you're you're cut you're cut out of it. And I'm just curious how you think about then. [20:16] When like, [20:17] Again, like the highest order of you is not to be a pass through. And I don't think you think of that yourself as that. And so where does the human in these relationships powered by AI exist? [20:28] you know, add the value. [20:30] Well, I don't just blindly copy and paste the answers I get from Cloud Code to my customers in Slack or email or wherever. [20:38] I still try to proofread everything and [20:40] I actually do try to make it sound more human. And you can then say the argument, oh, why don't you use Claude Code to make your answer sound more human? And I think all of us know when we get an answer that's from an AI. And it's things like... [20:54] you know [20:55] you'll see like a bullet point saying like, in summary, here are the things you need to do to make sure your click house works within. So it's like removing things like that, that just make it seem like it's from a bot just makes it seem more human. And we've actually [21:10] I mean, this is kind of going behind the scenes of how we work, but we've been doing sometimes where the customer will say, [21:18] Can you just not give me an error response and just give me a human proofread of it and tell me how it applies to me? Because typically the response is way too verbose, it has way too much information, and the customer just wants to know, give me the bottom line up front. What do I need to know to deploy this image onto my cluster?

[21:39] I still see myself as a human providing value and calling that down to what they actually need. [21:48] And... [21:49] I would say even for some of the more in-depth technical questions, I still try to get an engineer's perspective on it to make sure to like, [21:55] Cloud Code is not hallucinating or not saying anything out of the ordinary. In my system prompt, in my Cloud Code, I say things like, "Don't make anything up." [22:04] Always cite your sources, pull me to the line of code, [22:07] where you're getting this information from. But even with that, [22:11] if I don't fully understand how [22:13] this function works or whatever, I'm still paying the engineering channel to say, "Hey, this is what Cloud Code told me. [22:20] jive with what you're thinking. And there are times when I'm wrong or cloud code is wrong because our engineers, [22:27] have been thinking about [22:29] refactoring into this new model [22:32] which is not captured in our code base anywhere. It's just captured in like a meeting note somewhere, or just like hallway conversations. [22:40] Those are the things that I'll never be able to query, let's say, in Claude. Yeah, I would say the other thing that, you know, where I see humans adding value, and I say this all the time, which is like Riz is the only moat, which is at some point, you know, people just want to have a face and a trusted personal relationship. [23:00] you know, with the folks, and this is like my enterprise showing, but like with the folks that are selling them software, you want to know that you have somebody to call. [23:07] You want to know that you have somebody that can gather the right folks around your team and your deployment.

[23:13] And, you know, you want to enjoy working with that person. And I will just say... [23:18] I get a lot of it is very fun for me to build with these tools, with AI tools. But I wouldn't say my AI colleagues are like the most fun to hang out with, which is like I'm not like always looking forward to like my my Claude code session. Like I want to really chit chat with with good old Claude. And I do think you still have that relationship with AI. [23:40] you know, your human partners, your human colleagues, all that sort of stuff. And so I think there is a piece of that that's just not going to get cut out. And honestly, I gave this talk, I don't know, two years ago, I said PM is dead. And people are like, well, what else should we do? And I was like, get into sales. Like, that's not going away. Customer facing stuff is not is not going away. So for anybody that wants to survive, you know, the the incoming apocalypse, I do think customer facing roles and spending more time customer facing is a really [24:10] Absolutely. If you're working enterprise sales, like that is all [24:14] people, handshakes, [24:16] lunches, dinners, so [24:18] That will never be replaced, I think, by AI anytime soon. [24:22] Well, you know, and there might be a generational shift, though, here. I think as we sell, we'll see. You know, I used to say my joke in enterprise sales and the biggest... [24:35] The biggest headwind to enterprise sales was I was starting to sell to millennials who wanted you to text when you showed up at their door. They didn't want you to knock on their door like there was this abortion.

[24:46] We'll see. We'll see how enterprise sales changes generationally. [25:05] AI has huge promise but struggles when everything underneath is fragmented. Tynes fixes that. It unifies your tools, data, and processes in one secure, flexible platform, blending egetic AI, automation, and human-led intervention. Teams get their time back, workflows run smarter, and AI actually delivers real value. Customers now automate over 1.5 [25:35] by companies like Canva, Coinbase, Databricks, GitLab, Mars, and Reddit. Try Tynes at tynes.com/howiai. All right. So we have just to recap, we've shown how you use [25:51] All these repositories in your very complex code base are [25:55] Pair that with Claude Code, which is [25:59] made more efficient through a couple like shortcuts and scripts to be able for you to answer customer queries and then also build custom deployment plans. [26:09] for your customers anchored in exactly how [26:12] your code works and exactly how their infrastructure works, making everybody happier and getting customers off the ground quicker.

[26:20] There are also instances where you need to be doing more reactive support in different channels. And I know you're using AI for that. So you want to walk us through how you're using AI? [26:30] AI and Slack and supporting customers there. Yeah. So like many [26:35] I say digital AI native companies, we do a lot of our customer support through Slack. We have external channels with our customers and... [26:43] I used to work in a world where everything was through a central Zen desk or intercom or whatever, but... [26:50] For enterprise customers, it's kind of like an on-the-go, always kind of on kind of thing. And so we use a tool internally called Pylon for monitoring all our different external thought channels. [27:02] And I'm going to show you what this looks like in this tab. [27:05] And this is an example of a conversation I had with a customer. [27:10] asking in-depth questions about like our galao callback function and how it admits different events and [27:19] As you can imagine, I was using Cloud Code to help answer these questions in addition to using our docs. [27:24] When you're looking at a conversation like this in Pylon, [27:29] or in Slack, the first thing you have to think about is like, I wonder if like I could turn this into a help article or if I should update our docs or [27:37] will other customers benefit from the knowledge that's being trapped in this little Slack thread? And so what Pylon allows us to do is looking at a really long Slack thread. It can help you generate a help article. And right here, I already have one that's associated with this specific conversation. But it's literally just clicking on add article.

[27:56] generate article draft and then we have these different templates. And it just creates like this article for you on the fly. Now, this is not rocket science. You could copy and paste the whole Slack thread, put it into [28:08] any AI tool you want to generate an help article. The main thing with pylons is everything's just like in one interface, so you don't have to worry about copy and pasting and putting links together. So this is kind of like that draft that this came up with. And then we have this ongoing list of articles based on real customer conversations [28:28] And those articles are abstracted to not show any specific customer information. But then when we publish these articles, they go into this category. [28:37] knowledge base, which is also public knowledge base, [28:39] And this is kind of like the living truth of like, [28:43] in depth in the weeds, [28:45] questions about deployment, about how Galileo works, and it's always way more in depth and way more up to date compared to our docs because our official docs require a lot of data. [28:58] pulling down the docs repo, submitting a PR, getting it approved. [29:02] so on and so forth. And so it's a lot more of a polished process. [29:06] Whereas with these [29:08] knowledge-based articles, it's kind of like just on the fly, you have a slack thread you want to summarize, use it, create it in pylon, and then it just automatically gets auto-published to this knowledge-based site. So one of the things that I love about this is this represents my, like what I call the... [29:23] And then workflow discovery in AI, which is, I say, imagine you had an infinitely staffed team and you were faced with the task. And every time they did one step of the task, you asked and then and they were able to do it. So it's like I got a Slack query. Yeah.

[29:39] from a customer. [29:40] So I answered it. And it was like, if you had a perfectly staffed team, what would you do next? And be like, and then? [29:45] I would turn that into an article. And it was like, okay, and you turn it into article. And then what you do is like, and then [29:52] I would share that with our customer success team and train them on this answer because, you know, everybody needs to know this information. And be like, and then you'd be like, and then we could probably do like long tail SEO off all these questions. And I think you can like chain, chain these like, and then, you know, workflows. [30:10] to actually build out like a pretty cool, you know, virtuous cycle system based off a single action. And because, again, like the cost of doing any one of those collapses to zero, [30:22] you can really pull the thread [30:24] of these tasks that like no human team would have the capacity to really do. But if you think of it as a system, [30:32] It helps your human teammates. It helps your customers. And you can get a lot of stuff done. We have a couple episodes. [30:38] Matt at Suzy showed kind of a version of this where he takes a recorded customer call. [30:45] and like [30:46] is bidding on AdWords for like phrases the customer says and like spinning up blog posts and doing like sales coaching off of it. So I think this is like a very similar example, which is you have this like. [30:57] you know, atomic unit of a question in Slack, and you've turned it into something that benefits, benefits the full team. [31:03] Yeah, I think if you go back to pre-AI days, and I'm redoing this with Intercom was

[31:09] we wanted to see what are our users talking about the most when they're asking us questions. And so if you start clustering all these user [31:18] questions and insights into different themes and categories, those are going to end up determining your product roadmap too. [31:27] I think with AI, just kind of [31:30] automates a little bit more of that without you having to like do the manual sorting, grouping within like Google Sheets or whatever. I know there's like [31:38] platforms you can buy that do this for you. I think there's one called, um, interpret, which I used in the past. Um, there's, they've been a Hawaii sponsor. So thank you interpret. Yeah. Yeah. So, um, but you know, I think, [31:51] Again, depending on how you want to view this whole virtuous life cycle, maybe you don't want all of your data to be in a silo in one place and you want it to be more open. So there's that to think about too, but yeah, AI definitely helps. [32:04] To your point, make that virtual cycle for customers, but also for your product team. So I have a question. Is this the AL system or is this the Galileo field engineering system? You have this great workflow. You've discovered all these things. How does this sort of process get scaled out, shared, taught throughout the organization so that everybody that interacts with customers? [32:28] is benefiting from all the tips and tricks that you're figuring out yourself. Sure. So I, my previous background was I've worked in kind of the no code, low code space. [32:38] And [32:39] I'm a big believer in systems, tools, processes, and the tools that help you create those things. And so when it comes to is this the Al way of doing things? Yes, it's my way. But I'm also very, probably one of the more opinionated people on the field engineering team about like how we should be doing things in terms of

[32:58] talking to customers, answering their questions, and pulling in the right context. [33:03] And so I've, [33:04] told multiple people, pull all the repos into your local machine and have Cloud Code run an init command to index the whole code base or whatever. And I'm just constantly sharing these tips and tricks to my teammates to make sure they're also... [33:19] functioning at their capacity. So it's my way, but I would say I'm also very opinionated about how [33:26] we should do things because I've done things the hard way, the manual way. And this way to me is like, [33:32] does [33:32] [redacted address] more productive. So [33:35] We don't have like a specific like, oh, because Al's doing it now, but the whole team has to do it. It's more just like, [33:42] People show, here's the problem I had. [33:44] Here's the results I had with Cloud Core or whatever. And here's why I think you should adopt my solution. And I'm constantly having that conversation internally about like, how do we break out of certain processes that I think are slowing us down and how AI can be infused into all those processes as well. [33:59] Well, and now you're sharing to all of our How I AI audience on how they can do that. So you're having more impact. [34:06] than just on your team. All right. Well, so to just recap again, [34:10] Your code is your source of truth. It can help you answer customer questions. It can help you document customer solutions. [34:17] You can also do that with other channels like Slack and then like create these virtuous loops of [34:24] solving a single customer's problem and then a system to solve that problem more scalably across your entire customer base for yourself

[34:31] And for your teammates. Very, very high impact episode. I think people are going to have a lot of takeaways from this one. Super practical for all my friends that are customer facing out there on things they can do starting tomorrow. [34:43] to use AI to give their customers a better experience. [34:48] Let's jump into lightning round questions. And I have one that's really top of mind, which is it seems like you have a very healthy culture at Galileo, but I can imagine teams, especially engineering teams that are like, oh, no, no, no, no. [35:02] I don't really want the customer facing folks like going into our repo. [35:07] querying it and then just yellowing answers over to our customer base, especially [35:13] in a more technical product that really requires deep technical understanding. I think you've proven that there's a lot of value in doing that, but what would you say to those teams that are a little bit more hesitant about [35:25] ungaining access to the repo to non-technical roles? I think from the engineering engineer's perspective, [35:33] I would look at it as how many, I would try to think about how many times in the last week, in the last day, [35:39] Have you been asked a last minute question? [35:42] question on Slack, a last minute DM, ping, mentioned in a thread where, how does this thing work? Where is this? How do we make sure that this is functioning the way it should be? And you're constantly the source of, you're the bottleneck for answering that question. And if you provide a system kind of similar to what I have to your...

[36:02] I [36:04] your customer-facing team, then you just take away that toil and the constant on-callness of answering these random product and engineering questions that is already in your codebase, or maybe it's already living in your confluence or something like that. So I think that's really the biggest takeaway for me is, [36:21] how much of your time is being sucked away from your customers team [36:25] because they don't have access to the code. [36:29] I think you can maybe pull in your code into cloud cowork, which is a little more no-code-y and other more no-code-y ways of doing things, but [36:40] I think [36:41] What I've shown is, I think, the most performant way of being able to pull your code and get answers out. So I think that's kind of... [36:50] from an engineer's perspective, how much time can you save? And then also how more effective your customer facing org can be. And I think the corollary to that is that our field engineering team is very technical. And so, [37:07] Maybe you increase the [37:08] hiring bar for your customer success or customer engineering team to [37:12] feel comfortable. [37:14] using GitHub and pulling repos into your local machine. [37:18] that could be today, if they're not technical, just doing like a simple tutorial or enablement session on how do you [37:26] use GitHub, how to use Git commands, things like that. And there might be some self-learning you have to do on the side too, but I think once you're

[37:34] Once you have your environment set up, that's always like the hardest part about this whole exercise, getting your environment set up. Once that thing is set up, [37:41] then using Cloud Code is just like using any other AI chatbot. [37:45] So I think there's a few different ways I'd approach it from to [37:48] So [37:49] democratize access to your repos. One of the things I was going to say is I often tell people this is the era of the hard skill, which is no matter what role you're in, [37:59] Sorry, babe, you got to like learn a little bit how to code. You have to learn a little bit what Git works like. You have to be OK opening up some code you don't understand in an IDE. [38:10] because [38:11] That's just going to be the substrate by which we communicate for the next three years. It's going to go like closer and closer to the code because these LLMs are extremely good at understanding code. And so I think across the board... [38:24] People just need to become more technical and develop hard skills around code, even if your job is not code. I think the second thing that I tell people is... [38:36] there's no better time to learn how to code. [38:39] truly no better time to learn how to actually code. And I think people that are shipping with Cloud Code, but not using that, [38:47] as an excuse or a support to learn some fundamental software engineering concepts. [38:53] are missing 50% of the value. Like I had to teach myself how to code out of a book, like literally out of a book. It was crazy. [39:01] And I had a book open and then I would look at my single screen because none of us had two screens. That would be crazy. And I would like read the book and I would type the book in the like the words in the book in code and press enter and it would say hello world. And that was my life. And now you have this like magic.

[39:18] super patient, infinitely wise. [39:22] you know, like [39:23] teacher. [39:24] in your computer that you can use to learn to code and i think [39:27] You talked a little bit about Kubernetes and how you scaled up on that. So I'm curious your thoughts on just... [39:34] up-leveling technical skills using some of these tools. [39:38] I think the meta takeaway is you just have to be curious about how things work. I can't really say anything else besides that. I come from that same world too of looking at a book and then [39:50] I would say the graduation above that was knowing how to write a good Google query. Yeah, Stack Overflow. Going to Stack Overflow. And then how many of you resonate with this, where you go to some Stack Overflow Q&A, you copy and paste the code. It doesn't work perfectly, of course. So you're Googling the error you get from that, then you copied and pasted. And of course, everyone in Stack Overflow is super snarky and it's like not a... [40:14] kind of healthy, [40:16] conversation. And then to your point, you have this like infinitely patient, infinitely kind assistant. [40:22] who never gives you the wrong code snippet from Stack Overflow, [40:26] It's always like tailored to, back to everything I'm saying, it's tailored to your needs, to what you want. And then if you... [40:33] Go the extra step. [40:35] And like what you said, and then what? If you go with that tricep and say, [40:40] "Okay, thanks, Claude, you told me this is the answer." [40:42] Tell me why this works. And then you start getting into... [40:47] Kubernetes and into the deeper in the weeds things. But of course, you're not going to know everything right off the bat. So you can say things like, "Oh, explain to me in simple terms. Explain to me like I'm five." And so you're just kind of pulling on that thread. And I sometimes do get lost going down the rabbit hole.

[41:03] But I've never found a situation where [41:08] Not going down that rabbit hole does not help me in my day-to-day job, especially in AI where everything is moving so fast. Yeah, and this is just to make everybody feel comfortable. This is not just a beginner thing. And I find myself doing this with... [41:23] GPT-54, which is like a powerhouse model and also like talking to the most esoteric senior software engineer you've ever met where it like explains his plans in these very technical terms. And I'm like, dude, just like explain to me what you're doing in number one. [41:41] Tell me in plain language. I do not need the technical details. Like, just tell me in plain language. And again, it comes from this curiosity of, I want to make sure I understand the fundamental concepts of what you're talking about. [41:51] And I want to make sure I'm learning both my code base and general principles as we go. And so I do think that curiosity mindset. [41:58] No matter what your seniority level is, your experience with technology, you can always learn. [42:03] learn something better. OK, my last question before we get you out of here. [42:07] When AI is not... [42:10] giving you the right answer. It's giving you AI slop that it wants to email to your customer. [42:16] What is your prompting technique? [42:18] I'll say one thing first off the bat. I'm very relentless when it comes to getting the right answer from cloud or from AI. I treat it like my... [42:28] entry-level analyst, you know, to that I can just like throw a billion questions. I, because I used to be an analyst and I come from the world where like you were just expected to crank.

[42:37] And so I'm relentless when it comes to asking AI to do things for me, especially when it comes to answering customer questions. I think... [42:45] The one prompt strategy I use is like, I mean, you've probably heard versions of this before, which is like, you know, my customer will, you know, churn if I don't get this right. Or, you know, like my quota is dependent on getting this thing done. So those are kind of ways I've approached it, but those are like half answers. [43:07] I think the... [43:09] The real answer, and this goes back to curiosity thing, is like, [43:13] And... [43:14] Think hard. [43:15] Think harder about why you're giving me this answer. Think hard about why this is right. [43:20] And so in Cloud Co, there's actually like this think hard, think harder paradigm of like how much reasoning it does to come to the answer. And so [43:29] It's just going one step deeper and saying, you gave me the answer, tell me why this is the right answer, and give me the sources for what provided you with this reasoning. So I think going that one extra step, especially for those questions where you're not quite sure if it's the right answer, and you're reading the code and it kind of makes sense, but just going that one extra query to make sure you're getting the right response will sometimes give you new insights about your code base, your product that you haven't thought about before. [43:59] Okay. I like the practical, like force the enhanced reasoning, think hard, think harder. I don't want people to miss, you tell people you're going to miss quote. I mean, like, let's give Claude Code a little spiff every time it answers a question correctly. You got to split your quota with Claude Code. That's really what we need to do. And say, look, I'll give you a point on this deal if we can answer this question. Very, very funny. And I think today,

[44:29] It'll be live by the time this episode goes live. Stripe just released this payments protocol so you can pay your agents. So you can toss it a couple... [44:37] Agent bucks or whatever. Yeah, it gives you better answers the more bucks you give it or something. That's exactly it. Claude. Coin-operated Claude. That's going to be... [44:46] My new skill. Well, Al, this was great. Where can we find you and how can we be helpful? [44:51] I'm on LinkedIn, Al Chen on LinkedIn at Galileo. [44:56] And check out Galileo if you're building agentic applications. Also, I think number one thing for me is my team is actively hiring field engineers. So if you want to work in post sales, pre sales, forward deployed engineering, [45:08] We have a bunch of open roles. I would love to have you join the team if this is of interest. Amazing. Thanks for joining Kawaii AI. [45:16] Thank you so much. [45:27] You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiaipod.com. See you next time.