How I AI - Successfully coding with AI in large enterprises: Centralized rules, workflows for tech debt, and training your team | Zach Davis (Director of Engineering at LaunchDarkly)

Starting point is 00:00:00 Vibe coding is not an acceptable enterprise development strategy. I love it. I can do 100 commits a week by myself on my side project, on my startup. But when you're working on a code base and a platform like Launch Starkley that powers trillions and trillions of experiences every day, you can't take the same strategies and tactics that a vibe coder could take. One of the things that I realized is what's good for humans is also good for LLMs. And so I really started with how do we make sure that The repo is well set up for humans to know how to work in it.

Starting point is 00:00:33 So we have front-end organization, we have accessibility, we have a JS style guide. So all is this very detailed documentation that we've put into the repo itself rather than have it in other places. And this way, LLMs can access it, humans can access it, et cetera. I think all the engineers out there are like crossing their fingers

Starting point is 00:00:50 and hoping that there's one rules protocol to rule them all that shows up. And I think what you've shown is you can just create that yourself. And then that makes it much more scalable. Welcome back to How IAI. I'm Claire Vaux, product leader and AI obsessive here on a mission to help you build better with these new tools. Today we have a great episode for anybody trying to deploy AI agents in a real engineering team with a real code base, not just vibe coding. We have Zach Davis, director of engineering at Launch Darkly, who's going to show us how he sets up centralized rules and docs for all his AI agents,

Starting point is 00:01:28 uses AI to burn down tech debt, and keep his hiring bar high. Let's get to it. This episode is brought to you by WorkOS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems.

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Starting point is 00:02:57 using. So before we dive in, can you just tell us a quick list, maybe not something? a quick list, of all the AI tools the technology team at LaunchDarkly are now using. Yeah, absolutely. Let's see. So on the design side, again, we're exploring a bunch of things. So there's going to be a bunch of tools. So lovable V-0, Figma Make. We're using on the product side, obviously we're using chat PRD.

Starting point is 00:03:24 And then on the engineering side for code, heavy cursor users, heavy Devon users. We're also using now like Cursorses background agent. I personally use Winsurf because I like WinSurf, but most of the rest of the work does not. We are using, we're trying to augment. We are looking into Cloud Code. We're doing all the things. We're also looking into PR review. So we use co-pilot for Code Review and we use Cursor for Code Review as well.

Starting point is 00:03:51 Okay. And I feel like 18 months ago we were using maybe a little GitHub co-pilot, but not much. Yeah, not much more. And one of the things that I really liked that you and I did together and you were a champ in coming along this journey is we really decided that in order for AI to be really effectively adopted by a team like LaunchDarkley's Engineering Organization, which is over 100 people, we really needed to put some concerted effort behind it and put a person in charge. And lucky you, you drew the either shorter long straw, however that works. you know, what do you think about teams approaching this kind of engineering wide transformation and what kind of organizational and cultural things you need to do to make it possible? I do think having a person who's kind of, I don't know if in charge is the right word,

Starting point is 00:04:42 but whose responsibility it is to drive that kind of change. And I think that having someone who's close to the code helps a lot because you don't really know what's working and what's not working unless you're in the code, least on some basis. And so that can be a manager that can be a director of someone, but it has to be someone who's actually trying these things, I think, matters a lot. And yeah, you, I think you were looking for someone to take that role. And I was, I think, skeptical, right, of how well, things were working really well for you over on your side job on chat PRD. But when I tried to do the same things in our code base, I was struggling. And so I really came at it from a standpoint of

Starting point is 00:05:25 I want to understand what works and what doesn't and and either be able to push back on you and say, hey, you know, like it's great over there, but on this, you know, larger codebase that's not working or be able to actually drive change. And now I'm at the like, I'm on board. Let's drive change. I think that matters a lot. Yeah. And one of the things that I think is really important for our listeners, especially ones that are at growth stage or larger companies, is vibe coding is not an

Starting point is 00:05:55 acceptable enterprise development strategy. Like, I love it, right? I can do 100 commits a week by myself, on my side project, on my startup. And, you know, I can recover from quality issues, you know, the maintainability of the code, the issue right now. That's not my big business issue. But when you're, you know, working on a code base and a platform like Lans Starkley's that powers trillions and trillions of experiences every day, you can't take the same.

Starting point is 00:06:25 strategies and tactics that a vibe coder could take and bring those into not just an individual developer's workflow, but an entire team's workflow. And so what have you kind of discovered as you try to figure out how to make these tools work for a larger team? With smaller teams, you have more flexibility in terms of how you approach these things. With larger teams, you have more enablement and stuff like that. We're kind of in the messy middle. And it's, I found it more difficult to sort of like operationalize that, make everyone successful. And what I found was everyone was on their own journey to try to be successful with AI. And that just doesn't scale very well. Right. So yeah, you really need to come up with the system in order to to make everyone

Starting point is 00:07:13 more successful. What I want is when those skeptical engineers jump in and they try cursor, they try whatever for the first time or they try it for the first time in a while, I want them to be successful so that they get that aha moment. And if you just leave them on their own, then you're not going to get there. Totally. And we had, I think you and I had mutually had the experience of developers having their first experience to actually be negative, whether it was with cursor or with Devin. It was like, see, I knew this was never going to work. And here's my first pass proof that it didn't work. And so you really did a lot of what I appreciate about you is you did a lot of, you did a lot of technical work to make sure people were successful. We'll definitely talk a little bit more about

Starting point is 00:07:55 some of the kind of culture and operations piece, but I actually want to get, dive into what you did in the code base to make it easier to work with Cursor and Devin. So can you walk us through some of those things that you did? Yeah, absolutely. So in my IDE here, you can see one of the things that I realized is what's good for humans is also good for LLMs. And so I really started with how do we make sure that the repo is well set up for humans to know how to work in it. And so we have this docs directory. And I pulled a bunch of stuff from Confluence, from Google Docs, from other places in the repo. And I put it all in here, right?

Starting point is 00:08:33 So we have front-end organization. We have accessibility. We have a JS-style guide. So it's this very detailed documentation that we've put into the repo itself rather than have it in other places. And this way, LOMs can access it, humans can access it, et cetera. In addition, we had these, we had cursor rules before, we had a CloudMD file, and I wanted to consolidate that. And so instead of a dot cursor rules, I have this dot agents rules, and the idea is to kind of centralize all of this knowledge in one place.

Starting point is 00:09:04 And so you can see here, I have something like TypeScript Essentials, which has a really kind of like quick, like the quick hits of what's really important. and then it also links off to like the comprehensive docs and says, hey, go if you want to find out more, you know, go look at the JS style guide. And so then our cursor rules actually just point to that, right? So our cursor rules say, hey, if you want type script guidelines, go find this file in dot agents. And then I talked about augment earlier. We were traveling augment.

Starting point is 00:09:40 I set this up yesterday. And I had, I asked the augment agent. to just create this. I pointed at the cursor rules and I pointed it at our agent's rules and I said, can you just create this file? And so it did the same thing. And this way, we don't have to duplicate everything

Starting point is 00:09:58 across multiple tools or tool files and it's much easier to get stuff working well by default. And the whole idea with this is, again, I want people to be successful out of the gate and having this kind of centralized place, having all this documentation in the repo, just makes it way easier for tools to be successful by default. Yeah, one thing that I hear a lot is people are really frustrated with the tool-specific rules.

Starting point is 00:10:25 They're like, why do I have a Claude.mD? Why do I have a cursor rules? Why do I have these GitHub rules, especially if you're experimenting with the number of tools that you're trying? Each tool has isolated their rule set in an individual file structure. And I think all the engineers out there are like crossing their fingers and hoping that there's like one rule. protocol to rule them all that shows up. And I think what you've shown is you can just create that yourself, create a directory in your repo, put a consolidated set of rules, make your sub-rules for each of those tools point to those rules. So say when you're looking, you know, when you're working

Starting point is 00:11:00 on front end reference these rules. And then that makes it much more scalable. The other thing that I want to call out for you is, I think what you said at the beginning, you're using, I don't know, a dozen tools, probably like three to five IDs across the engineering organization, probably within any individual engineer, you're testing like, I want cursor today and Devon tomorrow. And if you don't have your rule set up for all those tools, then you're starting from scratch every time. And so I really like this idea of a rule set up and then consolidation.

Starting point is 00:11:35 I'm curious, do you feel like the rules have improved the quality of the outputs significantly? Yeah, yeah, absolutely. So here I'm looking at our feature flagging rules. And it's interesting because we are a feature flagging. We have a lot of feature flagging code in the code base. And one thing we noticed was that some of the models, some of the tools would get confused about whether we were asking it to create feature flags on the like launch darkly product or whether we're actually trying to get it to do stuff in the code. And so there's a bunch of stuff that I did to just be really specific about how to make, how to be successful when creating feature flags. I want you to return a link, that kind of stuff. And it really has made a difference yesterday. Literally, one of our product managers was doing a task with Devin and was able to tell it to put the feature behind a flag. And Devin went and used the MCP and hit the flag and everything worked. So I have another question about rules because Launched Darkly's giant mono repo, 10,

Starting point is 00:12:40 10 years old or something like that. It's got a lot of code in it, front end, back end tests, all this stuff. What do you think if you had to get some advice to peer engineering leaders who are approaching the same problem? What are the must have rules from your point of view in the code? You know, I saw like a lot of front end stuff. But what, you know, what are the quick hits of what you think should belong in a kind of cursor rules or a rule set? I would say the best tip is ask the agents, right, to get you started. So Devin actually has a great wiki. for each repo that Devin works on, it creates a wiki, and it has a ton of really good information in it. And so I actually started with Devin and I said, hey, can you, like, this is what I'm trying to do. Can you create basically the human readable docs for this?

Starting point is 00:13:24 And so Devin did a pass and created a bunch of docs, suggested some structure. We went back and forth and I kind of tweak things. And then I took that output and I went through it with kind of like a fine-tooth comb because I think it matters, right? it matters to get those details right. And then once I had the human readable docs, I went to cursor and I said, hey, can you, can you take these docs and can you take your existing cursor rules and can you turn those into a dot agents file? And so it was a combination where you can kind of lean on the agents a little bit to help

Starting point is 00:13:58 you get unstuck and get started and then also use your knowledge of what's important in the repo. And the other thing is that I was looking at where I, are people getting stuck. I knew that people on the front end would struggle with getting like testing, basically, writing unit tests. It would write just tests and we use V test and stuff like that. And so putting in specific rules to make sure where people get stuck,

Starting point is 00:14:25 we have rules to help the agents be more successful. Would you mind pulling up Devin and actually giving an example of generating rule? And I have an idea for you, which is like rules around. generating data visualizations since we've done so much and just, you know, see what it comes up with. Yeah. Here's an example of both asking Devin Wiki a question and then also using Devin to create a rule. So we can say to the Devin Wiki, we can say, what are the libraries used for charting on the front end? I'm curious while this is loading. How long did it take you to set up Devin's environment? It's something that, you know, everything's easier with a little vibe-coded Greenfield app,

Starting point is 00:15:08 except for setting up Devon's environment. It's just as hard. I'm curious what your experience has been configuring Devon to work in a large repo. Yeah, I would say to get up and running with Devon, I got started pretty quickly. And we have kind of, we have a separate flow for front-end and back-end. We have a concept of sort of like front-end-only mode, which proxies against another back-end. And so I was able to get Devon's machine up and running pretty quickly in just front end only mode. And then I was able to take on front end tasks using Devon.

Starting point is 00:15:42 One of our other engineering managers actually came in and saved the day on the back end to get the full running our whole end to end up and running with Devon. And that took him, I think, a little bit more time than it took me. But the nice thing is you can do that sort of incremental, you know, you do what works. You don't have to have Devon running your full app locally in order to get value out of it. And so it's just about kind of like doing it piece by piece. And again, if it's hard to get Devin up and running, it's probably hard for your human developers to get up and running. So there's always incentive to make those things better. Yeah, I will give just because you said it, you said, you know, Devon's environment doesn't have to be running for you to get valued.

Starting point is 00:16:25 My number one Devon prompting trick is don't run this locally. Just give me the code and I'll test it for you. So sometimes I bypass that process entirely. Okay. So you asked this question of Devon Wiki. What are libraries used for charting on the front end? And it gave answer recharts, some other things. And so what would you, one, is this information pretty accurate?

Starting point is 00:16:46 And two, what would you do with it? Yes, it is accurate. We are using multiple libraries. And so that was one of the things I was curious about is we've brought in several libraries and we're kind of trying to figure out how to consolidate. And so it picked out that we're using recharts or using VizX. It lists e-charts as a secondary library. I don't know if that's strictly true, but generally this seems very correct.

Starting point is 00:17:08 And so I like to use Devon Wiki to just sort of ask basic questions about the repo, make sure I understand what we're doing. But if I actually wanted to create a rule, so you can't take action from Devin Wiki. So what I want to do is I want to create a new document, a human-readable, you know, human-centered document in, our doc slash front end about how do you charting libraries. And then I want to also add a rule to dot agent slash rules. So I'm just going to give this to Devin and I am going to see how it goes.

Starting point is 00:17:42 So Devin's going to spin up a new session here. And one thing I want to call out for folks listening on the prompt is you specifically said you want it to create a markdown document. Markdown is every engineering agent's favorite file type. So that's a good way just to give a little bit of stuff. structure to your code. It also tends to pretty print and be human readable and easy, easy to view in GitHub and all that stuff. And so what you're doing here is just asking Devin to make those docs for you. And this is one of my favorite use cases of Devin. I think

Starting point is 00:18:20 you know this. It's my favorite Devin hack, which is I have a GitHub action on every PR that writes docs for the PR and adds to a change log, program. with Devin, I found that it's a very good technical writer. You know, sometimes the code is okay, but the technical writing is very clear and very good. Yeah, I think that's exactly right. The Devin Wiki is very good. It knows a lot about your code base. It has this very explicit way of learning and understanding your code base. And so it is very good about kind of describing that back and, as you said, doing it in a solid technical writing way. Yeah. And then one of the other things that I want to call out for people that are maybe listening and not watching is,

Starting point is 00:19:02 we are chit-chatting because we're waiting for Devin to spin up a virtual machine. So for those that don't understand kind of how Devin works, it actually spins up a virtual environment that reflects a development environment. It's going to open it up. It's going to read your codebase. It's going to do all this stuff. And so, you know, it takes a minute to actually boot into an environment a little different than running something like cursor locally. Yeah, that's exactly right, where these other tools are just using whatever you have locally. Devon is running its own machine, which has a lot of upside. It can run a browser and see a browser.

Starting point is 00:19:39 It can do a lot of things that don't come out of the box with these other tools, with the downside that it takes a little time. Depending on your repo, it takes a little bit of time to actually set that machine up and get it running. But like you said before, if your machine is slow to cold boot for Devon, it's probably slow to set up locally for an engineer. So again, aligned incentives on getting your repo to work well for both your agent co-workers as well as your human, your human colleagues. That is my favorite thing is all the things that have been hard for humans forever.

Starting point is 00:20:13 And we have just kind of swallowed it and said, well, that's the way this works, become even more important today with these LLM tools to solve and improve. Well, I think it becomes more important to solve and prove. And then I also think it becomes easier to solve and improve them. If I said, you know, two or three or four years ago, Zach, go document everything in the repo. High quality human readable docs, you just go do it by yourself. It would take forever to generate high quality docs that, you know, really reference our code and understand the nits and details. And I think the fact that even you can spin up docks so quickly is so transformational to how you can. And I know we'll see this in a little bit

Starting point is 00:20:58 like burn down tech debt, make your engineers happier. And so I do think, you know, a lot of engineers have the skepticism that adoption of AI tools is really about moving faster, shoving more junk in the code, like just getting feature bloat. And I actually do think for mature engineering organizations, it is also an opportunity if you approach it correctly to take care of some of the things that you have just hated forever in either how you run your software, how your team operates, or the code itself. And so that's one of the advantages I think people underestimate in larger organizations because they blur the line in their mind of AI-assisted engineering with vibe coding, which is not what we're talking about right now. No, not at all. And technical debt is

Starting point is 00:21:44 my favorite use case for AI to supercharge a medium-sized organization. Okay. So what we're seeing here is Devin's looking through your repository, accessing its knowledge. Actually, I'll take a pause here. Have you set up the knowledge in Devin explicitly, which is for folks that don't know, little snippets of kind of rule, it's almost like Devin's rules in some ways, little snippets of knowledge and rules. Have you set those explicitly? Or have you simply accepted and approved the ones that Devin suggests? Yeah. So as you mentioned, one of the things that I really liked about Devin, especially as we were first getting started, is it builds up this knowledge on its own in some ways. So as you're interacting with Devin, it will make suggestions for additions to its knowledge so that it gets sort of like, quote, unquote, smarter every time.

Starting point is 00:22:36 Where some of these other tools have memories, because Devin's starting a fresh session every time across different users, it has a centralized knowledge, kind of like repository. And so it's been a mix. We've sort of let it build up over time and various people have accepted knowledge. I added some knowledge very early on. I will intentionally add stuff when I run into problems. But then again, when I moved all this documentation to the repo and I was trying to centralize everything, Devon's knowledge now primarily points to that same dot agents directory because I don't want to have the duplication. I want it to work for all the tools.

Starting point is 00:23:16 I don't want just Devon to be effective. I want all tools to be effective. Got it. So you've really taken all your tools, whether locally hosted in the repo or cloud hosted like Devon, and just made this agent's folder. Yes. That is it then is exactly right.

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Starting point is 00:24:16 Visit maven.com slash Lenny to enroll now. Use code Lenny's list for $100 off. That's M-A-V-E-N dot com slash Lenny to get ahead in the AI era and start building. So Devon has a plan now. One of the things I like about Devin is it gives this confidence now, like how confident is it in the task at hand, which is nice because sometimes it's not confident. and it's better not to proceed.

Starting point is 00:24:49 This is something that, as we mentioned, Devin should be really good at. And so I feel good about the ability to execute this, but it will give you sort of an overview. If I thought, if I read through this and I didn't like what it was doing, it's going to run prettier on the markdown files, which actually I think is a good idea. But if I didn't think that was a good idea, I could update its plan while it's deciding what to do next. Yeah, the other thing that I enjoy about Devin is nine times out of ten, it's confidence. confidence gets higher as it goes. So it always starts like medium confidence, but I have to investigate and then it's like high confidence.

Starting point is 00:25:24 I know what to do. But occasionally it fails me deeply and I have bullied it so much that like starts to progressively lose confidence and that it's like low confidence. I haven't been successful so far. So I find the confidence assessment pretty accurate. Yeah. Okay. So now it is creating it created multiple.

Starting point is 00:25:46 Markdown files. So it's created a charting libraries.md file. And we can actually, if we want, we can jump over. So there's a shell. There's also the code. So I can actually go look at what it's creating while it's creating it. So charting libraries guideline. It's creating that in our dot agent slash rules slash front end.

Starting point is 00:26:06 It looks like I think it also created one in docs. So this is the human readable version, which I'm not going to go through in detail, but looks. It has examples. It has a different libraries. I like all of that. And then in the agent's rules, it's sort of a consolidated, you know, must use this when. I like seeing that. I would go through here and really make sure it's accurate and what we want.

Starting point is 00:26:30 And then it's a little long, I think, you know. For me, I want to keep the agents rules pretty concise. So you're not including the context and just so it's not too much. And I would also want to make sure that it links out to the full. documentation is another trick that I like to do so that a tool can decide to pull in that additional context if it wants. Well, one little trick that I learned from another how I AI guessed is that if you notice cursor reads long files in chunks of 200 lines. And so his goal was to keep these files under 200 lines so that it's not chunking the content. And so I saw yours is just like a little bit

Starting point is 00:27:12 over 200. So one of the things you might add to your rules for rules is try to keep your rules files under 200 lines, for example. Now, again, I don't, I don't know if that's actually full or true, but it is a tip somebody gave me. So I'm passing it along with no personal context. No, I mean, that that's actually, again, that's a good tip for humans, just like it's a good tip for LOMs. And you said something that I think is really interesting, which is I actually, I have a read me, I have a human read me about the rules so that people understand how to create new rules, but I should actually probably have something geared towards LLMs so that when LMs are adding new rules, they're doing a better job of it.

Starting point is 00:27:54 Yeah. Okay. So I see this. It looks great. So it's created a human readable docs in your docs folder, a rules for your LLMs. You're going to review this. You're going to do a PR and merge those docs in to the repo. maybe take a look at them, edit them. And you've used, you know, Devon Wiki, Devon agent,

Starting point is 00:28:19 and then it's spun up this code base to write those docs. And so I think this is a really great flow. I think people are going to learn a lot from this. You know, one of the things you said earlier was that tech debt is your favorite use case for these AI tools. I love to hear it because this is how I try to pitch senior engineers and senior engineering leaders like you to really adopt these tools when they're really skeptical. Can you walk us through how you actually approach burning down tech debt using these tools where it's made it easier maybe? Yeah, absolutely. So here we are in cursor and I'm going to show you that same agents directory. So I showed you agents rules before. We also have agents slash migrations. And so this has a couple files in it. It has a CSS module conversion

Starting point is 00:29:07 file, which I created to help us convert CSS files to modules, CSS modules. And then it also has, I just added this one the other day, which is the one that I would like to show. And so what it is is basically, it's a combination of instructions for agents and a checklist, basically like a task list of what to burn down. And so the problem that I was running into is that our front end, unit tests, when you run yarn test, it just, there's so much noise in the console that it's really distracting. There's some actual legitimate problems in there that are just kind of being warned about

Starting point is 00:29:50 and ignored. And I wanted to pay that down, but it's, it's one of those things that is annoying, but it's not quite annoying enough for someone to own it. And also it's such a big problem. It's really hard for one person to just kind of like take that and pay that down. And so. Well, and I'll say, imagine as an engineer, you go to your product counterpart and you're like, hey, I just want to spend like a week or two just making our test logs just a little less noisy. So my life's just a tiny bit. Like it's such a hard pitch to make for work like this. It's super important.

Starting point is 00:30:27 And like the pitch can work on the right leader. But again, like this is the kind of thing that's hard to justify in a fast moving org. Yeah. So I'm actually, I think what I'm going to do is I'm going to. is I'm going to ask I will talk through kind of how this works and in the background

Starting point is 00:30:42 I will have cursor take the next stab so I'm actually going to say so it has this context it knows I'm in this file and I'm just going to say can you take the next tier of tasks

Starting point is 00:30:52 I can see here there's a tier one tier two there's three files I think that's reasonable and fix them and I'm just going to click go and we're going to see what happens

Starting point is 00:31:03 okay so in in the meantime what I did to actually produce this is I ran yarn test and I piped the output to a log file, which I'm not like a super techy tech person. And so I actually asked Cursor how to do that effectively. And then I had a log file and I gave that to Cod to Cod code. And I asked Cod to basically create this file. And so what it did is it went through all, it actually had trouble with how big that file was, but it was smart about working around that. And so it found out that we have something like 1,200 extra lines in a in a test run that we don't need to be there, that we don't really want there. And

Starting point is 00:31:44 then it quantified this, or it sort of grouped this into different types of warnings. And then which files are the worst offenders? And so then once we have this file, I said, great, like, can you go fix the tier one worst offenders? And so it actually went and has done that successfully. That's been merged in, reviewed and merged in. And then I can do stuff like this where I just say, like, for any, the thing that I like about this is you can just give this to any agent now. I can slack Devin and say, at Devin, can you pick up the next task in the front end test noise cleanup? I can do it here in cursor and watch it go. I could give it to cursor background agent. It sort of like makes it easy to pick these things up as

Starting point is 00:32:34 individual tasks and make progress on them. What I like about this approach as well is it's very, there's a lot of parallels to how you would approach something like this with an engineering team of human partners, right? You're going to take a problem. Somebody's going to go investigate it and identify priority tasks to do. You're going to put those tasks in some sort of task tracking system. And you and I both know all of our beloved tasks. tracking and project management systems and I am starting to see cursor markdown files become the new

Starting point is 00:33:11 task tracking system. So I'm seeing this trend of these checkmarked files in cursor just being the source of truth for progress on initiatives. So you created basically a list of epics and tasks here, if that's what we call it. And they're prioritized by how severe they are. And then what I like about how you're approaching this, instead of saying like rip through all 1,300 noisy, lines. You're saying prioritize them. Do them one by one. And then what I'm presuming you're doing is the work happens, whatever agent you decide to do the next task, close it out, you review the PR, you make sure any changes work, you merge it, it gets marked off. The other thing I want to call out is while you are probably running this yourself, you could probably also get more people on the

Starting point is 00:33:59 team to be aware that this test exists and just say, hey, if you have a few minutes and you're able to review a next set of noisy tests, like tell Devin to pluck off one and do the code review for me. And it's all set up and it's ready to go. So, you know, I think this multiplayer aspect is very important when how you approach some of these tools when you're working in a larger, larger team. Yeah. I just today, I had a PR app to fix a few stray errors on this, on this one file. And one of the one of the people from the team that works primarily on that, I included them in the review and he said hey if there's any more stuff like this feel free to kind of like throw it over the wall to us you don't have to be the one that does all of this he didn't know that i was just

Starting point is 00:34:44 using uh you know clod or whoever and so now i can actually just point him at this file i said hey you know take a look at this file and if there's any ones you want to pick off in your ownership area then then just go ahead and and you're exactly right doing that democratizing that this is great for, again, I'm saying the same things, but it's great for bots. It's also great for humans. Humans can come in here and understand this and work against it. Yeah. And if you're feeling like, you know, crafting your farm to table code and you want to pluck one of these off yourself and you want to fix it, you can approach it the same way, right? Just open the file, mark the thing is done, do the PR. And so I really do think it's important that folks think of kind of these tools as an extension of, you know, of the team and the more the tools can operate the way the team would operate and the more the team can operate in the same way the tools can operate, then we can kind of all collaborate together and be much more efficient. So I think this is a great, super great example. I'm not going to make all of us watch cursor go through tests and lent errors because I have lost enough of my life

Starting point is 00:35:57 to doing that. But I think it's a really, really great example of tech debt. And then just to ask the question, you know, what's the end payoff for front-end developers? Like the actual issues bubble up in your tests and these tests get less noisy? Yeah, I think one is it's easier to find stuff when stuff is going wrong. Two is I think it said that the biggest problem was actually accessibility warnings. So that's like a real problem that exists. But when there's 1,200 lines of that. And a lot of that's coming from like the same component. If it's tested a bunch of times, will spam the logs. But being able to sort of surface the actual signal through the noise,

Starting point is 00:36:37 I think is one of the key benefits. Okay. And then for our last workflow, I know that, Zach, you're going to impress everybody, and everybody's going to think you are just an AI-enabled, you know, cutting-edge engineering leader who only works with his army of bot friends.

Starting point is 00:36:56 But you're actually hiring and launch darkly. We expand the team. you're always bringing in great talent. And you've actually used AI to solve another problem, which is making sure that you're doing a great job hiring. So do you mind spending a couple minutes on what that little workflow looks like? Yeah, absolutely. So I am, you know me.

Starting point is 00:37:15 I'm a little bit of a conflict avoidant person. I don't love giving people tough feedback. You know, it's something I've grown to do over my career, but especially when it's someone I don't have a strong relationship with. It's not a direct report. I don't love just dropping in being like, hey, this isn't great. But we were trying to improve, make our hiring more consistent.

Starting point is 00:37:36 And I created a rubric for all of the panels that we have. So there was really clear guidelines about how to score a candidate. But the other piece of it was we needed people to follow those guidelines. And I wanted to be able to give people feedback about whether, basically, I wanted to raise the bar of the actual scorecards that we were creating. So I created this custom GPT. I gave it the rubrics and I gave it examples of good, good scorecards, bad scorecards, and gave it as much kind of like you helped me write the prompt.

Starting point is 00:38:10 So thank you very much for that. And so what I'm going to do is I'm actually just going to paste in a scorecard. And so this is, you know, a scorecard that we got. And I'm going to click, click go. And it's going to do a few things. One, the rating that it's giving me is the rating. of the scorecard itself. So it's a little meta.

Starting point is 00:38:34 But I want to know, basically, one of the things I did in the prompt is I said, give it a rating, is it excellent, good, fair, or poor in terms of scorecard? And then I want you to list out strengths and potential improvements. And then the last thing that I had to do,

Starting point is 00:38:50 which I also think you help me with, so thank you very much, is what I, the format I wanted to give this to people in was to send it to them over Slack, right? like, hey, like, thanks for doing this. But also I had a little bit of feedback. And so it actually gives me the detailed feedback. But then it also crafts a short Slack message that I can, if I want, just copy and paste and send to the person who created the scorecard. I love this because so many managers and hiring managers can empathize with this.

Starting point is 00:39:22 Because if you're running an interview panel, you're having everyone from your boss to your direct reports to people you've never really worked with directly interview candidates, right? Because you have these cross-functional interviews. And while you can have all the rubrics in the world, interviewers sometimes write terrible notes and, you know, assess the wrong things or don't give you the right details or really using the rubric incorrectly. And you're not sitting in every single one of those interviews to give live coaching. And so this is a really nice way to make sure that you're holding the kind of standards very high. And then giving you some leverage as a manager to give your team coaching and then as they get this coaching, they get better at doing

Starting point is 00:40:03 the interview feedback and then you can be more confident in your hiring decisions. Yeah. And honestly, this helped me. In order to test this out, I was doing a bunch of interviewing and I was writing scorecards and I would pace it in and see what kind of feedback it gave me. And it was giving me very good feedback. And I learned very quickly the kinds of things, be, you know, be more specific, you know, avoid certain kinds of things. And so it actually made me write better scorecards just through trying to create this tool for other people. Okay, Zach, you have given a masterclass in how engineering leaders at larger companies can really approach integrating AI into not just their individual workflows, but their team

Starting point is 00:40:46 workflows. So just to cover what we talked about, one, let the team experiment. Like every tool, just let's see what works. So you seem pretty kind of generous with your experimentation mindset around what tools can use, can bring value to the team. I think two, context is king. And so you are loading up your actual repository with docs and rules. Three, those rules are centralized so you don't use agent or tool-specific rules. You create a central agent repo and then point all your specific tools toward that. You use your AI tools to actually create those rules.

Starting point is 00:41:20 You use cursor and other tools to create plans to burn down tech debt and then have those AI tools, burn down that tech debt. And then since you have all this free time now, you're coaching yourself and your team to be better interviewers and better hireers. So just that. No big deal. That's all you have to do. You things, yeah. And this is all, I mean, truly from a personal professional development perspective, these skills were developed what in the last 12 months, right? Oh, I think January is when I really started, like, took on the mantle and playing with Devin and really going down this past cohort. So six months and we didn't give you, I mean, I think we offered, but like

Starting point is 00:42:03 formal L&D, none of that. We just pushed you into it and said go. Yeah. Okay, I'm going to wrap with two quick lightning round questions and I'll get you back to all your AI-assisted code. Question number one, you listed so many AI tools. Which one is your favorite? Or which one has been most transformational? That's really hard. I would say windsurf, actually. Everyone was hot on cursor and it just wasn't the ux at the time was not clicking for me and i saw a video for windsurf and i was just like whatever i'll give it a try and i had the free trial and within an hour i think i was paying for it because i just it really clicked for me and the agent workflow just really quick clicked and i was hooked amazing and then when AI is not listening to you you're such

Starting point is 00:42:53 your conflict avoidance so i'm actually very interested in your answer here AI is not listening to you. You need to give it harsh feedback. What are your tactics? I know you don't yell. I know you're very polite. But what do you do? I mean, sometimes I lose it. But I have to, I, the thing that I actually do is sometimes I just feel like it's not the right task. Right. So it depends. If I, if I think it's something that AI should be good at, then I get a little snippy with it. Maybe I don't yell, but I'm definitely, you know, getting a little annoyed. But I also think that sometimes it's okay, right? Like sometimes it's not going to work and you don't have to keep banging your head against it. And I think developing that sense of where it works and where it doesn't has been really powerful for me. And also sometimes I just like getting in there and getting dirty, getting my hands in the code.

Starting point is 00:43:47 And so, yeah, I think my technique is actually either I do it myself or I go back and try and fix it. you know, am I providing the right context? You know, what, what is missing that it can't accomplish this effectively? Yeah. You're a very good manager. So I think it's from those skills. All right, Zach, this has been super informative. Where can we find you? And is there anything we can do to be helpful? I'm on LinkedIn. We are hiring at LaunchDarkly. And also, if you are a launch, Kirkley user user, and you have any feedback, I love user feedback. So please send it, send it my way. Amazing. Well, thank you so much, Zach. Thank you. Thanks so much for watching.

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How I AI - Successfully coding with AI in large enterprises: Centralized rules, workflows for tech debt, and training your team | Zach Davis (Director of Engineering at LaunchDarkly)

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