← How I AI
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- [00:00]
Al Chen
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.
- [00:11]
Claire Vo
They don't want the doc's answer. They want the step by step answer of how all these services cascade together.
- [00:17]
Al Chen
What I realized is that I can actually pull all of these repos into my VS code and I can now use Claude code to ask our entire code base questions.
- [00:27]
Claire Vo
Did you just say Claude code? Write me a script script that pulls all these.
- [00:30]
Al Chen
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.
- [00:39]
Claire Vo
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.
- [00:55]
Al Chen
Throw into Confluence, throw 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.
- [01:06]
Claire Vo
Let's give Claude code a little spiff. Every time it answers a question correctly. You got to split your quota with Claude code.
- [01:14]
Al Chen
Yeah, it gives you better answers the
- [01:15]
Claire Vo
more bucks you give it or something Coin operated Claude. That's going to be my new skill. 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 hard harnessing your code to make your customers experience way better. Al Chen, who's on the field engineering team at Galileo, shows us how he uses their 15 repositories and Claude code to answer every nuanced customer question that comes across his desk. And use that to make the entire customer base and his entire team a lot happier. Let's get to it. This episode is brought to you by Orchis, the company behind Open Source Conductor, which powers complex workflows and process orchestration for modern enterprise apps. In 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. With Orcus Conductor you get a modern orchestration layer that scales with high reliability and brings humans, AI and systems together in real time. It's not just about tasks, it's about orchestrating everything. 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. Orcus Orchestrate the future of work, learn more and start building@orcusio Al thanks for joining how iai I am really excited about this episode because we've seen a a lot about using your code as documentation. You know we've heard engineers saying you know docs and code should be in the repo Product manager 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. So tell me, what problem were you facing when you decided I'm just going to clone the repo and fire up Claude code and solve some of these problems myself?
- [03:34]
Al Chen
Sure. So working at Galileo on the field engineering team, I'm on the front lines in terms of working with our enterprise customers who are typically developers themselves and asking very 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 ANSW to my customers. Even by even using cloud code or chatgpt or whatever and trying to take all these different help docs and trying to come up with an answer. It just still wasn't coming up with the answer that my customers were looking for. And I just Background I'm not an engineer, I've never held an engineering role, but I think I know enough to just be dangerous. And I realized that our product, Galileo's product, we're an observability tool for AI applications. 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 backend images that customers have to deploy onto their Kubernetes cluster. And I realized that all these different services like ui, API, Authz, comment, they are all individual repos within our Galileo repo. We're not a mono repo. We have multiple different repos. What I realized is that I can actually pull all of these repos into my VS code. Initially it was just more for me 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 open VS code now and most of these directories correspond to one of those services within our architecture. So one repo corresponds to one service. And by having all of these repos in my VS code, I can now use Claude code to ask our entire code base questions that are not answerable by our public documentation. And so sometimes I'll get really end up questions about well, how does this feature actually work? And so I'll ask cloud code, look into the API repo, look into the AUK Z repo and help me come up with an answer. If you can't find the answer, reference other repos within my directory, my cor, my root directory and help me figure out the answer. And so that's the key unlock was when I figured out I could get way more in depth, way more technical and at the same time myself I can learn how our code base works. And how this has really helped me is I don't have to constantly ping our team engineering channel with hey, what's the answer to this question? The customer just pinged me about this and you can imagine engineers being really frustrated when I'm trying to post these questions and then the customer asks me a follow up, then I'm posting that follow up in the Slack thread. So I'm sure many of you who are working on the front lines of customers understand how that feels, but I've basically reduced all of that almost down to zero by pulling all these repos into my local VS code.
- [06:46]
Claire Vo
I'm really empathetic to this problem because I used to work at LaunchDarkly leading product and engineering very technical product. We too had an architecture diagram that looked very similar to that. And again as a more of a. 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? And you know, when you have seven layers of caching in your app, you can give the high level 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 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 you know and I know this is always evolving over time. So even if you got the answer right a month ago, maybe your team shipped an update or maybe 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 at least your main branch is always the source of truth, it becomes a really reliable context set for you to answer questions about how the product operates.
- [08:33]
Al Chen
Yeah. And to quickly address your comment about how your code is obviously always evolving. I mean we're pushing out, you know, multiple features per day, multiple releases. And one thing I've done wrote this with cloud code is I have this script at my root directory that says like I just do something called pull all and I'm not sure if this is how other people do it, but it just pulls the main branch into my repo for all the repos in my root directory. So if I do this every day I kind of get the latest code across all these directories on the left hand side of my VS code. So the alternative, which I was doing before for a few weeks and I realized this is just asinine 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 like 15 different repos I have to pull the latest from. So that's kind of how I solve the code base is always evolving problem to make sure that I'm always getting the most up to date information for my customers.
- [09:33]
Claire Vo
And I have to ask, did you just say Claude code? Write me a script that git pulls all these.
- [09:37]
Al Chen
Oh yeah, yeah, yeah. I have no idea. This is the. I'm opening up the script right now in my VS code and it's like what, 16 lines? I haven't, 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 I just did it in like one shot.
- [09:55]
Claire Vo
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 which is in VS code, in cursor and 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 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 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 idea so that when you're querying it with claude code or you're clearing it, you know, with something like cursor, it can have, it can, it can go across kid traverse across repos and really give you highly contextualized answers.
- [10:54]
Al Chen
Yeah, our code basis happens to be in multiple repos but I just pulled them all into this like giant Galileo directory here. And so everything is like at the same parent. But yeah, if you're in a Monorepo, could be. 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 at Galileo, this is how it works.
- [11:15]
Claire Vo
Well, I have many Monorepos and yeah, you just open it at the right. I would, I would say my advice to folks is open Claude code or open, you know, your IDE at the right level and sometimes it's narrow and sometimes you need to go up a directory and, and I think really thinking about that and you can even do that contextualized to the problem you're trying to solve. Right. And, and doing that I think is really helpful. Could you show us just using cloud code what kind of question you could answer with this code context?
- [11:47]
Al Chen
I will give you an example of. I guess I'm a big believer in using shortcuts too. So I use a bunch of custom Claude code custom commands to help me do stuff. So one thing I do a lot is helping my customers deploy Galileo into their vpc. So I have a custom command called DPL which is, it actually references our. The first thing it does is it looks at our confluence because we have a whole bunch of confluence pages about how to deploy into kubernetes using our different images and stuff like that. So I'll say dpl, my Customer cannot use CRDs and they are using Google Secrets Manager and want to deploy the wizard image. 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 I want cloud code to focus on looking at Confluence first because I know that we have a whole bunch of deployment stuff there and then from there if it 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 to find the answer. So right now it's just using the Lassian MCP to pull information from Confluence and then marries that with our code base to answer a very kind of in depth 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, we call it a customer cork stage. 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 their environment. And so I literally have a 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, servers, service to service encryption, things I have no, 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 actually one of the core pages 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. 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 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 and deployment requirements, it's way more effective and just gives the customer more trust that we know what we're doing.
- [15:01]
Claire Vo
Essentially what I love about what you showed here, which is kind of combining the repository with the Confluence MCP and then both team generated general documentation as well as you generated like micro documentation at the customer level is I've heard so often in my 20 years in enterprise SaaS like 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? And people have spent 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 for us across systems, right? So you can be in your code querying Confluence. 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 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 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.
- [16:26]
Al Chen
Yeah, yeah. I mean, I think even if it's as simple as, hey, you, you came come across a really great answer in Slack, like in a really engaging Slack thread. Throw that into a Confluence page or 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 consciousness of documents you want to have cloud code scan, I would just say throw into Confluence, throw into notion, throw it into Slack, whatever. And then 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.
- [17:04]
Claire Vo
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 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 Codex to build, you know, better user experiences or more resilient backends or any of that stuff. 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 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. 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 that's gotta 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 gonna service your customers well.
- [18:23]
Al Chen
Yeah, I hope so. I mean I think our customers, I think my customers are hopefully enjoying the answers I provide and the in depthness that I provide. I think I've thought about taking this to the extreme which is there we have certain, I've certain customers who are like, 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 because they can't see your code, but me al I can see the code, help me get the answer. And so if I take that to the logical conclusion, it's like why can't we just share our repos with the customer? Because then they can just start querying our repos directly to get the answers they need instead of me as kind of like the quote unquote middleman. And you know, the issue is that like our code is proprietary and all that kind of stuff. But I have seen there's actually a case study from LangChain and since a lot of Langchains repos are open source, their support agent bot actually does a lot of things I do, but it is able to query all the public open source repos and 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 cloud code or Cursor or whatever. But I've went through that kind of thought experiment of like I'm still kind of a bottleneck in terms of answering my customers questions because I kind of like hold the keys to our code but if they somehow had a sanitized version of it, then maybe they could just self answer their questions too because they're also all using VS code and cursor and Claude 2 but they just don't happen to have our proprietary code base.
- [20:06]
Claire Vo
Yeah, I was going to ask you, are you worried that the OWL bot is coming and you're cut out of it? And I'm curious how you think about then when like you, you 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, you know, add. Add the value?
- [20:31]
Al Chen
Well, to. I don't just blindly copy and paste the answers I get from cloud code to my customers in Slack or email or wherever. I still try to proofread everything and I actually do like try to make it sound more human and you can then say the argument, oh, why don't we just 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 you'll see a bullet point saying in summary, here are the things you need to do to make sure your clickhouse works within. So it's like removing things like that that just make it seem like it's from bot just makes it seem more human. And we've actually, I mean this is kind of going to, going behind the scenes of how we work, but we've been dinged sometimes where the customer will say, can you just not give me an error response and just give me like 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 like the bottom line up front, what do I need to know to like deploy this image onto my cluster. And so that's where the human. I still see myself as a human providing value and calling that down to what they actually need. And 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 like cloud code is not hallucinating or not saying anything out of the ordinary. Even in my system prompt I always, you know, in my cloud code I say things like don't make anything up, always cite your sources, point me to the line of code where you're getting this information from. But even with that, if I don't fully understand how this function works or whatever, I'm still paying like the engineering channel to say, hey, this is what cloud code told me. Does that jive with what you're thinking? And there are times when I'm wrong or cloud code is wrong because our engineers have been thinking about refactoring into this new model which is not captured in our code base anywhere. It's just captured in like a meeting note somewhere or just like, you know, hallway conversations. And so those are the things that I'll never be able to query, let's say in Claude.
- [22:45]
Claire Vo
Yeah, I would, 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, 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. You want to know that you have somebody that can gather the right folks around your team and your deployment and you know, you want to enjoy working with that person. And I will just say 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 clot code session. Like I want to really chit chat with, with good old Claude. And I do think you still have that relationship with, 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, 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 important part of everyone's job.
- [24:10]
Al Chen
Absolutely. If you're working enterprise sales like that is all people. Handshakes, lunches, dinners. So that will never be replaced, I think, by AI anytime soon.
- [24:23]
Claire Vo
Well, you know, and there might be a generational shift though here, I think as, as we sell. As we sell, we'll see. You know, I used to say my, my joke in enterprise sales. And the biggest, the, the biggest headwind to enterprise sales was I was starting to sell to millennials who like, wanted you to text when you showed up at their door. They didn't want you to knock on their door. Like there's. We'll see how enterprise sales changes generationally. This episode is brought to you by tines, the intelligent workflow platform powering the world's most important work. Business moves faster than the systems meant to support it. Teams are stuck with repetitive tasks, scattered tools and hard to reach data. AI has huge promise, but struggles when everything underneath is fragmented. Tines fixes that. It unifies your tools, data and processes in one secure, flexible platform. Blending a jet tech, 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 billion actions every week. Tines is trusted by companies like Canva, Coinbase, Databricks, GitLab, Mars and Reddit. Try tines@tines.com howiai all right, so we have just to recap, we've shown how you use all these repositories in your very complex code base pair that with Claude code, which is 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 for your customers anchored in exactly how your code works and exactly how their infrastructure works, making everybody happier and getting customers off the ground quicker. But 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 and Slack and supporting customers there?
- [26:33]
Al Chen
Yeah, so like many, I say digital AI native companies, we do a lot of our customer support through Slack. You know, we have external channels with our customers and not, I mean I come from the I used to work in a world where everything was through like a central Zendesk or intercom or whatever, but for enterprise customers it's kind of like a 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 Slack channels. And I'm going to show you what this looks like in this tab. And this is an example of a conversation I had with a customer asking in depth questions about like our Galileo callback function and how it emits different events. And as you can imagine, I was using Claude code to help answer these questions in addition to using our docs. But when you're looking at a conversation like this in Pylon 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 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, 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 any AI tool you want to generate an help article. The main thing with pylons is everything is kind of just like in one interface so you don't have to worry about like 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 and those articles are abstracted to not show any specific customer information. But then when we publish these articles they go, they go into this knowledge base which is also public knowledge base. And this is kind of like the living truth of like in depth, in the weeds 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 pulling down the docs, repo, submitting a pr, getting it approved, so on and so forth. And so it's a lot more of a polished process. Whereas with these knowledge based articles it's kind of like just on the fly, you have a slacker you want to summarize, use it, create in pylon and then it just automatically gets auto published to this knowledge base site.
- [29:18]
Claire Vo
So one of the things that I love about this is this represents my, that like what I call the 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 from a customer. 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 I would turn that into an article and it was like, okay. And you turn it into article and then what you do, it's like, and then 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 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. You can really pull the thread of these tasks that like no human team would have the capacity to really do. But if you think of it as a system, it helps your human teammates, it helps your customers, and you can get a lot of stuff done. We have a couple episodes. Matt at Suzy showed kind of a version of this where he takes a customer, a recorded customer call and like, 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, you know, atomic unit of a question in Slack and you've turned it into something that benefits. Benefits the whole team.
- [31:04]
Al Chen
Yeah, I think if you go back to pre AI days and I'm redoing this with Intercom was 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 questions and insights into different themes and categories, those can end up determining your product roadmap too. And so I think with AI just kind of automates much 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 platforms you can buy that do this for you. I think there's one called Interpret, which I've used in the past.
- [31:44]
Claire Vo
They've been a how I sponsor.
- [31:46]
Al Chen
So.
- [31:46]
Claire Vo
Thank you. Interpret.
- [31:47]
Al Chen
Yeah, yeah. So. But you know, I think again, depending on how you want to view, view this whole virtuous life cycle, maybe you don't want all of your data to be like in a silo in one place and you want to be more open. So there's. That's to think about too. But yeah, AI definitely helps to your point, make that virtual cycle for customers, but also for your product team.
- [32:09]
Claire Vo
So I have a question. Is this the OWL system or is this the Galileo field engineering system, which is 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 is benefiting from all the tips and tricks that you're figuring out yourself?
- [32:32]
Al Chen
Sure. So I. My previous background was I've worked in kind of the no code, low code space. And 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 talking to customers, answering their questions and pulling in the right context. And so I've told multiple people like 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 like constantly sharing these tips and tricks to my teammates to make sure they're also functioning at their capacity. So it's my way. But I would say I'm also very opinionated about how we should do things because I've done things the hard way in the manual way and this way to me is like just 10 times way more productive. So 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 people show. Here's the problem I had, here's the results I had with cloudcore 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.
- [34:00]
Claire Vo
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 than just on your team. All right, well, so to just recap, again, your code is your source of truth. It can help you answer customer questions, it can help you document customer solutions. You can also do that with other channels like Slack and then like create these virtuous loops of solving a single customer's problem and then a system to solve that problem more scalably across your entire customer base for yourself 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 to use AI to give their customers a better experience. 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, I, I don't really want the customer facing folks like going into our repo, querying it and then just yoloing answers over to our customer base. Especially 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 ungating access to the repo to non technical roles?
- [35:29]
Al Chen
I think from the engineering engineer's perspective 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 have you been asked a last minute question on Slack. A last minute DM ping mentioned 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 customer facing team, then you kind of just take away that toil and the constant on call ness of answering these random product and engineering questions that is already in your code base 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 how much of your time is being sucked away from your customers team because they don't have access to the code. And I mean I think there's some no code. I mean I think you can maybe pull in your code into Claude Cowork which is a little more no Cody and other kind of like more no Cody ways of doing things. But I think 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 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 maybe you increase the hiring bar for your customer success or customer engineering team to feel comfortable using GitHub and pulling repos into your local machine. And so that could be today if they're not technical, just doing like a simple tutorial or enablement session on how do you use GitHub, how to use git commands, things like that. And there might be some self learning you can do on the side too. But I think once you're, 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 then using cloud code is just like using any other AI chatbot. So I think there's like a few different ways I'd approach It from to democratize access to your repos.
- [37:52]
Claire Vo
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, Sorry babe. You gotta like learn a little bit how to code. You have to learn a little bit what git works. Like you have to be okay opening up some code you don't understand in an IDE because that's just gonna be the substrate by which we communicate for the next three years. It's gonna go like closer and closer to the code because these LLMs are extremely good at understanding code. And so I think across the board 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 there's no better time to learn how to code. Truly no better time to learn how to actually code. And I think people that are shipping with cloud code, but not using that as an excuse or a support to learn some fundamental software engineering concepts 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, 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, super patient, infinitely wise, you know, like teacher in your computer that you can use to learn to code. And I think, you know, you talked a little bit about kubernetes and how you, you scaled up on that. So I'm curious your thoughts on just up leveling technical skills using some of these, these tools.
- [39:38]
Al Chen
I, I think the meta takeaway is like you just have to be curious about like how things work. I can't really say anything else else besides that. It's kind of like I come from that same world too of like looking at a book. And then I would say the graduation above that was knowing how to write a good Google query.
- [39:56]
Claire Vo
And then Stack Overflow going to Stack
- [39:57]
Al Chen
Overflow and then how many of you resonated with this where you go to some Stack Overflow Q and A, you copy and paste the code. It doesn't work perfectly of course. So you're googling the error you get from that thing you copied and pasted. And of course everyone in Stack Overflow is super Snarky and it's like not a kind of healthy conversation. And then to your point, you have this like infinitely patient, infinitely kind assistant who never gives you the wrong code snippet from Stack Overflow. 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 go the extra step and like what you said and then what if you go the extra step and say, okay, thanks Claude, you told me this is the answer, tell me why this works. And then you start getting into 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 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, but I've never found a situation where 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.
- [41:15]
Claire Vo
Yeah. And this is just to make everybody feel comfortable. This is not just a beginner thing. And I find myself doing this with GPT5.4, which is like a powerhouse model and also talking to the most esoteric senior software engineer you've ever met where explains its plans in these very technical terms. And I'm like, dude, just like explain to me what you're doing in number one. 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 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, no matter what your seniority level is, your experience with technology, you can always learn, learn something better. Okay, my last question before we get you out of here. When AI is not giving you the right answer, it's giving you AI slop that it wants to email to your customer. What is your prompting technique?
- [42:19]
Al Chen
I, I'll, I'll say one thing first off the bat is like I'm very relentless when it comes to getting the right answer from Cloud or from AI. Like I treat it like my 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. 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 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, my quota is dependent on getting this, this thing done. So those are kind of ways I've approached it. But those are like half answers. I think the, the real answer, and this goes back to curiosity thing is like, think hard, think harder about why you're giving me this answer. Think hard about why this is right. And so in Claude code, there's actually like this think hard, think harder paradigm of how much reasoning it does to come to the answer. And so it's just going one step deeper and saying, you gave me the answer. Tell me why you think this is the 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 like, 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, you know, give you new insights about your code base that your product that you haven't thought about before.
- [44:00]
Claire Vo
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 it'll be live by the time this episode goes live. Stripe just released this, like, payments protocol so you can pay your agents, so you can, you know, toss it a couple agent bucks or whatever.
- [44:39]
Al Chen
Every time it gives you better answers, the more bucks you give it or something.
- [44:42]
Claire Vo
That's exactly Coin, Claude. Coin operated Claude. That's going to be my new, my new skill. Well, Al, this was great. Where can we find you and how can we be helpful?
- [44:52]
Al Chen
I'm on LinkedIn. Al Chen on LinkedIn at Galileo 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 for deployed engineering. We have a bunch of open roles. I would love to have you join the team if this is of interest.
- [45:14]
Claire Vo
Amazing. Thanks for joining. How I AI thank you so much. Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube or even better, leave us a comment with your thoughts. 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@howiaipod.com See you next time.