Lazar Jovanovic: preference

Transcript context

…the most efficient way that I found was, I would do the four parallel builds. Let's continue off of that example. Very quickly, after you've built hundreds of projects like I did, you see the winner. The winner is so obvious, it's not even a competition. You maybe do one or more two prompts to calibrate it. And when you're like, "Okay, the winner is here," at that point I either ask the tool that I'm using, or I'll maybe let's say go to ChatGPT or whatever and ask the LLM to produce a series of PRDs. What PRDs are for, again, people that are not familiar with the terms, they are project requirements documents, or for me, I call them sources of truth. What needs to be true for this project to be successful from a couple of perspectives? I usually build something that I call a master plan. It's basically a compass saying, "Here's what we're building." It's like talking to a human. I really treat Lovable like a human being. So it's like, "This is what we're building." Then I build an implementation plan, which is, "This is how we are going to build it and this is the sequence." It's very important to me, again, going back to quality, taste, human nature. I need to define ... Because I'm still working with a system that is not emotionally intelligent yet, I need to define how I want the app to look and feel. So, another PRD that I build is design guidelines. And then finally, something that just circles it all around, which is, "Okay, when we know how things look and when we know how we're building it, how does the user journey look like? I use the registers and then what? And then when they register and do that first step, what's the second step and what's the third step and whatnot?" So I build at least four PRDs. Right? And then when these are built, I read them. That's the planning, chatting part. That's where I'll spend a lot of time now on. When I nail down that first design, I'll spend an entire day if I need to just planning this part out, like documentation and breaking things down because that's how I'm setting the course. Everything's going to be dependent on this particular part of the process. When I'm done doing that, I build one final document, which I call either plan.md or tasks.md, and .md part is Markdown. Basically, I'm just using Markdown format because I've learned that AI likes to read Markdown. And what that serves as a source of truth on actual tasks and subtasks that it will need to execute to get to the finish line. And then there's the final, final layer, which is depending on what tool you use, Cloud Code or Cursor have what's known as rules.md or agents.md. What you're basically doing with rules or agent files is you're letting the agent know how you want it to behave and what it should focus on in the long run so that you don't have to repeat yourself with every prompt. Right? So in Lovable, there's a separate menu for that in your project settings where you can define project knowledge. And usually what I'll say, "Hey, read all the files before you do anything. Don't do anything before you read all the PRDs. Read tasks.md to see which task is next, then execute on that next set of tasks. usually what I'll say, "Hey, read all the files before you do anything. Don't do anything before you read all the PRDs. Read tasks.md to see which task is next, then execute on that next set of tasks. And when you're done, tell me what you did and how I should test it." And that's where that conversation about, "I religiously read the agent output," comes into play. I gave the agent everything, all the tools and resources that it needs to succeed. I gave it the rules, I gave it the docs, I told it what to do with them. And at that point I'm just sitting and reading. I don't prompt anymore. From that point on, I can switch as many windows as I like. My prompts have become, "Proceed with the next task." I don't need the context. I outsource that and delegate that to the agent. The agent needs context and I need to make sure that it's dynamic. I need to make sure that I'm regularly updating the documents from time to time so that we shift that token window it uses and how it uses it over time, but I'm not prompting, I'm not interrupting the flow. Yes, I'll go in, test, maybe put a prompt in here or there, but that's how I can build five projects simultaneously and never lose the productivity part, which is again, as I said, I do this today, manually. Call me to talk three months from now, an agent will do this for me. I'll be out of job, pretty much. That's why I don't optimize for this skill at all. I'm using it today, to bypass the shortcomings of human nature and LLMs, but I'm optimizing 100% of my time today on good judgment, clarity, quality, taste, good copy, good fonts. People don't talk about fonts at all that work with AI. They're 60% in my mind, maybe even more in how your output's going to look like. That's my obsession. I don't obsess over these things that I'm talking today because I know what's coming. The agents are going to get better, the models are going to get better. They're not going to need me to extend the context. They're going to do it themselves. So for me, the skill that I optimize for is the one that requires better decision-making rather than better output or better alignment. Oh, my God. There's so much here. This is so awesome. Okay. So essentially, what's happening here is you start a project, try a bunch of stuff, pick a direction that feels most correct. And once you have a set direction, you spend essentially a day, not building, but working with this AI agent to plan. And then, and well, I want to talk about that. And once you have the plan, then it's ... And it's amazing that you could do stuff like this with what some people may feel are not sophisticated tools that can build incredibly powerful things. You can do a lot of this with tools like Lovable, like have plans and rules and MD files. A lot of people may not know that. And so the idea is, okay, spend all this time planning because again, that'll save you a lot of time down the road. And then only once you have a plan, you get it going. And a key part of this, this three-wishes rule is really important. The reason you're doing this in large part beyond just being really clear about the plan is this idea of one task at a time keeps the agent's context window small so that it doesn't lose track of where it's at. That part seems important. It's like, "Do this thing." And then, "Okay, cool. Now do the next thing." Right?…

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