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 the answer that my customers were looking for.

Clarvo: They don't want the doc's answer. They want the step-by-step answer of how all these services cascade together.

Al Chen: 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 codebase questions.

Clarvo: Did you just say Claude Code write me a script that pulls all these?

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.

Clarvo: 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 will find the information. You have to be less precious about where and how you store the information.

Al Chen: Throw into Confluence. Throw it into Notion, throw it into Slack, wherever. 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 codebase.

Clarvo: Let's give Claude Code a little spiff every time it answers a question correctly. You got to split your quota with Claude Code.

Al Chen: Yeah, it gives you better answers the more bucks you give it or something.

Clarvo: Coin operated Claude. That's going to be my new skill.

Clarvo: Welcome back to How I AI. I'm Clarvo, product leader and AI obsessive here on a mission to help you build better with these new tools. Today we have an episode all about 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 nuance 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 Orcus, 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 Siloed low code platforms, outdated process management systems, and disconnected API management tools weren't built for today's AI powered world.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 enterprisegrade security, compliance, and observability. Orcus, orchestrate the future of work. Learn more and start building at orcus.io. Al, thanks for joining how I AI. I am really excited about this episode because we've seen 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 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 customerf facing things and solve customerf facing problems. So tell me what 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?

Al Chen: Sure. Uh 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 indepth 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 my customers even by you even using Claude Code or ChatGPT 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 the 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 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 like backend images that you have to that customers have to deploy onto their Kubernetes cluster. And I realized that all these different um services like UI API, autho repo. We're not a mono repo. they have like multiple different repos. And so what I realized is that I can actually pull all of these repos into my VS Code. Initially was just more for me to like I wanted to understand like 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 my our entire codebase questions that are not answerable by our public documentation. And so sometimes I'll get really inep questions about, well, how does this feature actually work? And so I'll ask Claude Code look into the API repo, look into the auth repo and help me come up with an answer. If you can't find the answer, reference other repos within my directory, my 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 qu learn how our codebase works. And how this has really helped me is I don't have to constantly ping our, you know, team engineering channel with, hey, what's the answer to this question? The customer just ping me about this. And you can imagine engineers being really frustrated when I'm trying to, you know, post these questions and then the customer asks me a follow-up and then I'm 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 that almost down to zero by pulling all these repos into my local VS Code.

Clarvo: I'm really empathetic to this this problem because I you know 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 you know people think that CPOs chief product officers or CTOs are internal facing. No no no we're sales people. We're always sales people. 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 highlevel 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 codebase 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 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. So I do think because the codebase is you know at least your main branch is always the source of truth it becomes a really reliable you know uh context set for you to answer questions about how the product operates.

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 Claude Code is I have this script at my root directory that says like I just do something called pull all and this 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 like all these directories on the lefth hand side of my VS Code. So it the alternative which I was doing before for like a few weeks and I realized this is just as I'm doing this is doing git pull origin main on every single directory and it was just like not scalable because there's now like 15 different repos I have to pull the latest from. So that's kind of how I solved the codebase 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 Claude Code write me a script that get pulls all these?

Clarvo: Oh yeah, yeah, yeah. I I have no idea. This is the I'm I'm opening up the script right now in my VS Code. And it's like what 16 lines. I have 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 did it in like one shot.

Clarvo: 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. Um loading a project at the multi-reo 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 in in your IDE so that when you're querying it with Claude Code or you're clearing it you know with something like Cursor um it can have it can it can go across traverse across repos and really give you highly contextualized answers. Yeah, our code bases happens to be in multiple repos, but I just pull 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 monor repo, uh could be yeah, actually I don't know how this would work with monor repo because I've never done it with monor repo with Claude Code, but um at least for us at Galileo, this is how how it works.

Al Chen: Well, I have a many monor repos 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 I think really thinking about that and you can even do that contextualized to the problem you're trying to solve right um and and doing that I think is really helpful could you show us just using Claude Code what kind of question you could answer with this code context

Al Chen: I will um give give you an example of um I guess um I'm a big believer in using shortcuts too. So I use a bunch of custom uh Claude Code custom commands to help me do stuff. So one thing I do a lot is helping my customers deploy Galileo onto their VPC. So I have a a 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 um cannot use CRDs and they are using Google Secrets Manager and want to deploy the wizard image give me a stepbystep process on how to do it. This is actually not a super representative um query because they're way more detailed than this and I provide a lot more context but I want Claude Code to to focus on um looking at Confluence first because I know that we have a whole bunch of deployment stuff there and then from there if they can't find the answer it will go off into all the different repos along the lefth hand side of my Claude Code VS Code 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 like our codebase to answer a very kind of in-depth um deployment question.

Al Chen: The one thing um I'm not sure if we should talk about this now but I started doing um this in Confluence where we have a we call it a customer quirks page. These are all kinds of all of our enterprise customers you typically have airgapped environments. So they have all these security measures and we have to abide by them when we deploy the pro 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 name spaces. You know here's how they handle side cars and service service to service encryption. the things I have no I know nothing about but um as I'm meeting with my customers I'm putting this all into this this one Confluence page this ever growing Confluence page and then this is actually one of the core pages that goes into this uh 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 Claude 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 it's way more effective and just gives the customer more trust that we we know what we're doing. Essentially

Clarvo: 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 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 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 um 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. Uh that was never never the value ad.

Al Chen: Yeah. Yeah. I mean, I think um even if it's as simple as, hey, you you've 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 just ongoing stream of consciousness of documents you want to have Claude Code scan, I would just say throw into Confluence, throw into Notion, throw it into Slack, wherever. 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 codebase.

Clarvo: 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 Claude 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. 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 got to come across as a much more um 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.

Al Chen: Yeah, I 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 have 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 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 quoteunquote middleman and you know the issue is that like our code is proprietary and all that kind of stuff but I have seen um there's actually a case study from LangChain and since a lot of LangChain's um repos are you know it's open source like their support agent bot actually does a lot of things I do but it is able to query you know 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 Claude Code or Cursor or whatever. Um, but I've got went through that kind of thought experiment of like I'm still kind of a bottleneck in terms of answering my customers questions cuz I kind of like hold the keys to our code. But if they somehow had 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 too, but they just don't happen to have our, you know, proprietary codebase.

Clarvo: Yeah. I was I was going to ask you, are you worried that like the AIbot is coming and you're you're cut you're cut out of it? And I I'm I'm just 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

Al Chen: well to I don't just blindly copy and paste the answers I get from Claude 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 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 you know things like you know you'll see like a bullet point saying like 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 a bot just makes it seem more human and we've actually I mean this is kind of going into 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 proof read 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 I still come see myself as a human providing value and callulling 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 Claude Code is not hallucinating or not saying anything out of the out of the ordinary. In my system prompt, I always, you know, in my Claude Code, I say things like, "Don't make anything up. Always site 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 Claude Code told me. Does that jive with what you're thinking?" And there are times when I'm wrong or Claude Code is wrong because our engineers have been thinking about refactoring into this new model which is not captured in our codebase 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 in Claude.

Clarvo: 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 RZ 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 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 um 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'm going to really chitchat with with good old Claude. Um 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 uh 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. Customerf facing stuff is not is not going away. Um so for anybody that wants to survive you know the the incoming apocalypse I do think customerf facing roles and spending more time customerf facing is a really important part of everyone's job.

Al Chen: Absolutely. if you're working enterprise sales like that is all people handshakes um lunches dinners so that will never be replaced I think by AI anytime soon

Clarvo: 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 um 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 they're subverted. Um, we'll see. 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 hardto-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 Agette 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 at times.com/howiAI. All right, so we have just to recap, we've shown how you use all these repositories in your very complex codebase. Pair that with Claude Code, um, 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

Clarvo: there are also instances where you need to be doing more reactive um support in 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.

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 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 onthe-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 qu uh questions about like our Galileo call back function and how it admits 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 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 help article. The main thing with Pylon 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 indepth in the weeds uh com 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 knowledgebased articles, it's kind of like just on the fly you have a Slack thread you want to summarize, use it uh create it in Pylon and then it just automatically gets autopublished to this uh knowledgebased site. So one of the things that I love about this is this represents my then 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 be like and then we could probably do like longtail SEO off all these questions and I think you can like chain chain these like and then um 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. Um, Matt at Suzie showed kind of a 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 full team.

Clarvo: Yeah. I think if you go back to pre-AI days and I'm doing this with Intercom was you we wanted to see what are 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 determining your product road map 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 and 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 um Interpret which I've used in the past.

Al Chen: There's they've been a how I sponsor so thank you Interpret. Yeah. Yeah. So um 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 to think about too. But yeah AI definitely helps to your point make that virtual cycle for customers but also for your product team. So I have a question. Um, is this the the Al system or is this the Galileo, you know, field engineering system, which is, you know, 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?

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 way of doing things yes it's my way but I'm also very probably one of the more 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 content text. And so I've told multiple people like pull all the repos into your local machine and have Claude Code run an init command to index the whole codebase or whatever. And I'm just like constantly sharing these tips and tricks to my teammates to make sure they're also um functioning at their capacity. So, it's my way, but I'm I would say I'm also very opinionated about how we should do things because I've done things the hard way, the manual way. And this way to me is like just 10 times way more productive. So, um we don't have like a specific like, oh, because Al's doing it now, 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 Claude Code or whatever, and here's why I think you should adopt my um 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 um how AI can be infused into all those processes as well.

Clarvo: 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 um 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. Um 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 customerf facing out there on things they can do starting tomorrow to use AI um to to give their customers a better experience. Let's jump into lightning round questions. And I have one that's really top of mind,

Clarvo: 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 customerf 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?"

Al Chen: Um I think from the engineering engineers 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 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 uh your customerf facing team then you kind of just take away that toil and the constant like on callness of like 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 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 Co-Work which is a little more noodey 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. Um so I think that's kind of um from an engineer's perspective how much time can you save and then also how more effective your your customer um facing or can be and I think the correlary correlary 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 And so that could be today if it's if they're not technical is just doing like a simple tutorial or enablement session on how do you use GitHub, how do you use Git commands, um things like that. And there might be some self-learning you have to 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 is getting your environment set up. Once that thing is set up, then using Claude Code is just like using any other AI chatbot. Um, so I think there's like a few different ways I'd approach it from to democratize access to your repos.

Clarvo: 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 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 okay opening up some code you don't understand in an IDE because that's just going to be the substrate by which we communicate for the next 3 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 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 um 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 Claude 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 upleveling technical skills using some of these these tools.

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 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 and then 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 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 is 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 it's you start getting into Kubernetes and into the deeper in the 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. Um, 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's is is moving so fast.

Clarvo: Yeah. And this is just to to make everybody feel comfortable. This is not just a beginner thing. And I find myself doing this with um GPT-4o 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. 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 codebase 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. Um, 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?

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 Claude 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 cuz I used to be an analyst and u 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 turn if I don't get this right or you know like um my my quota is dependent on getting this this thing done. Um, so those are kind of ways I've approached it, but that those are like half answers. I think the the real answer, and this goes back to curiosity thing, is like, and "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 like how much reasoning it does to come to the answer. And so it's just going going one step deeper and saying like 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. Um, 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 like 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 u sometimes, you know, give you new insights about your codebase that your product that you haven't thought about before.

Clarvo: 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. Um and say look I'll give you a point on this deal if we can answer this this question.

Al Chen: Very very funny. And I think today um 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. Uh, every time.

Clarvo: Yeah. Gives you better answers the more bucks you give it or something. That's exactly coin. Claude coin 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?

Al Chen: Um, I'm on LinkedIn um Alchen on LinkedIn at Galileo and um check out Galileo if you're building a AI 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, um we have a bunch of open roles. So would love to have you join the team if um this is of interest.

Clarvo: Amazing. Thanks for joining How AI.

Al Chen: Thank you so much.

Clarvo: 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 at howiaipod.com. See you next time.