How to Use AI Coding Tools Better than 99% of Developers

73% of developers, well, they're still copying and pasting code from ChatGPT into their IDE, but only 8% have figured out how to build complete [music] applications 10 times faster. And the gap isn't about skill, it's how the tools are being used. Now, most developers treat AI coding tools like an advanced Google search, something that you ask questions to and then pull answers from instead of seeing them as programmable development partners that can actually help you build full system. [music] So, what ends up happening is that you get small pieces that work on their own, but nothing [music] really connects. Every new feature feels like starting over, and then everything just gets harder to manage [music] as the project grows. And that's exactly what we're going to fix today in this video, so that by the end of this video, you'll have five copy-paste [music] workflows that transform you from code copying to system building so that you can actually build complete [music] working applications without constantly restarting from scratch. So, you got [music] to stick around until the end to see which AI coding tool takes the crown [music] today. And here's the exclusive part. I've created a free course showing you how to build apps and [music] websites and even SaaS products with the winning tool today completely without any code. Now, this course normally costs $299 to join, but for the people watching this video thank you very much, it is completely free. Now, this isn't just theory, you're going to learn how to create real profitable applications using AI, and you can only access this course by watching until the very end. So, please don't skip ahead. Now, you can only access this course by watching until the very end, so please don't skip ahead. But if you have to, go ahead and check out the link in the description below if you can't wait. I still got you. Now, most developers approach AI the same way that they approach search engines. They just treat read it like a smarter autocomplete. [music] So, we'll type in a quick request and get a quick answer, and then we'll move on. And at first, it feels productive. We ask for a feature, it gives us some code maybe, and it looks like things are moving fast. But that way of using AI only works for small isolated [music] tasks. The moment that you try to build something more, say, complete, then everything starts to just fall apart. And the problem with that is that most people just never shift from asking one-off questions >> [music] >> to building actual workflows. Now, instead of thinking about how different parts of an application connect, they treat every feature like a separate request. [music] So, you end up with pieces that technically work on their own isolated, but they don't really fit together. [music] There's no shared structure, there's no consistency, and there's no real system behind it all. And what makes this worse is the lack of chaining. Now, AI is capable of handling multi-step processes, but most developers don't even use it that way. They generate something, they copy it, they paste it somewhere else, and then come back and repeat the whole process all over again for the next [music] piece. And there's no continuity between steps, no memory of what came before, and no coordination across the whole build. And that is how people get stuck in this kind of loop: prompt, [music] copy, paste, repeat. And every time that they do want to add something new, well, then they're just kind of basically starting from scratch again. It's [snorts] a waste of time, it burns through precious credits, and it leads to implementations that [music] break the moment that we try to combine them. So, if you've ever built something with AI and then you felt like it started strong, but then slowly became harder to manage, that is usually why. And it's not that the AI isn't capable, it's more that the way that it's being used isn't structured for building complete [music] systems. The top 1% of developers use AI very differently. They don't treat it like a search engine or a quick answer tool. Instead, they treat it like a programmable team member. So, instead of asking, [music] say, random questions and hoping for good results, they build systems around it. They think in terms of workflows, not just prompts. And that shift alone completely changes what AI is capable of doing. So, at this level, AI isn't just helping you write code, it's participating in the entire development process. But for that to work, there are a few core mechanics that need to just come together. And the first is context management. AI can only be as good as the information that we give it. So, instead of starting from zero every single time, we just feed it the right context, your code base, your structure, your patterns at each stage. So, that way it's not guessing anymore, it's building based on something consistent. And [snorts] then there's workflow orchestration. [music] Instead of handling tasks one by one by one, we start chaining them all together. And one step leads into the next, planning, building, and refining so that the entire feature gets developed as a connected [music] process rather than just separate pieces. And then from there, you move into code generation pipelines. [music] And this is where things start to feel more structured. So, instead of generating, say, random implementations [music] each time you create a system where code is produced in a consistent [music] way across your entire app. So, same patterns, same structure, same logic, everything just aligns. [music] And on top of that, you integrate quality assurance directly into the workflow. So, instead of just waiting until the end to test things, AI can review the code, it can check for issues, it can optimize performance [music] as part of that process. It becomes an ongoing layer, not a final step. And finally, there is deployment automation. Because once everything is built and it's all validated, the process doesn't [music] stop there. No, you carry it all the way through to deployment. And the same system that helped you build the app can also help you ship it without needing to manually piece other stuff together all at the very end. And then when all these mechanics do work together, AI stops just being a code generator. It's more, it becomes a full development system, one that can build and test and deploy applications in a more structured, repeatable way with very little friction. All right, so, like I mentioned earlier, most developers, they treat AI like a quick tool for one-off tasks, while [music] the top 1% use it as a complete system. And this is how that shift actually looks in practice. [music] These five workflows are what make the difference here. And once you start using them, then everything just becomes more structured, more consistent, and basically just a lot easier to build as your project grows. Okay, so, for the first one here, this is all about context, okay? A lot of the time when people use AI, they just jump in and then they ask it to build something right away. And it works, but only to a certain point. The code looks fine at first, but when you actually try to use it, well, then it doesn't really match your project. The structure feels different, naming isn't consistent, and [music] then we just end up spending time more time fixing things just to make it fit. And that usually happens because the AI is working with zero context. It's just kind of guessing based on your prompt. So, the shift here is simple. Before asking it to build anything, you give it a clear picture of your project. [music] And that can be, say, documentation, existing code patterns, or even just how you usually structure things. And once it has that context, it stops guessing and it starts [music] following what's already there. So, here on your screen, you can see that I'm using a sample documentation for a calorie tracker app, and I'm going to attach this directly to Base 44. Then, instead of writing a long prompt, I'm just going to ask it to build [music] based on that documentation. And then after running it, everything just comes out a lot more aligned. The structure matches what was defined, the naming stays consistent, and the components, they all fit where they're supposed to fit. And it doesn't feel like something separate, you know? You can actually use it right away without having to rework everything. And that's really the difference here. You have to guide it, so what you get actually fits your project from the very start. All right, so, moving on to the second workflow here, this one is about how features actually get built from start to finish. [music] Because when you build features the usual way, it's not just one step, it's not. >> [music] >> You start with the idea, that's the first step, then you have to design the database, set up the API, build the front end, and then test everything to make sure it actually works. [music] Now, each of those steps takes time, and more importantly here, they depend on each other. So, you're constantly switching between different parts of the app just to get one feature fully working. [music] And that's why even something simple can end up taking hours. It's not just the coding, it's the whole process of stitching everything together across back end and front end. And what changes here though is that you're no longer handling those steps one by one. Rather, instead, everything is treated as one connected flow. You give it a single [music] request, and then it handles the full pipeline from the user story to the database schema to the API endpoints to the front end components and even validation and testing. [music] Now, most of this happens behind the scenes. So, what you see here is still just the chat interface, but in the background, that's where all the magic happens. Base 44 is orchestrating all of these steps together instead of just leaving you to manage them all manually by yourself. >> [music] >> And that is where the time difference comes in. You're not jumping anymore between layers or building things in isolation. Rather, you're getting a more complete feature that already works as a whole. Now, what normally takes hours of going back and forth across different parts of your stack, that just gets compressed into [music] just a few minutes, and it comes out properly connected from the start. Now, one of the biggest things that slows [music] development down is review. Even after a feature is built out, you still have to go back through it and check for bugs and look for weak spots and test [music] edge cases and make sure that nothing is going to break once it goes live. [music] And the problem is that process, well, it takes time. Even with good developers, things still get missed, especially smaller issues, security [music] gaps, or performance problems that don't show up right away. [music] And that's really where the bottleneck happens here. Code might look finished, but getting it to the point where it actually feels safe and polished enough [music] for production is a completely different step. And the larger the project gets, then the harder it does become to catch everything manually. [music] So, the advantage here is that Base 44, yes, Base 44 can handle all of that review process [music] in a much more thorough way. It's not just looking at whether the code runs. It can go through security checks, uh performance issues, and of course, bug detection in a much more systematic way. And that matters here because those are usually the exact areas where humans, ourselves, review starts just becoming inconsistent, especially when we're moving quickly. So, here on your screen, you can see that starting with the built-in security check from the dashboard of the app that we created earlier. Now, it runs through the whole application here, and it checks for issues automatically, which already saves us a lot of time compared to say trying to inspect everything ourselves. And after that, I'm going to go ahead and just take it a little bit further, and I'm going to prompt Base 44 directly with something like this. Please do a full code review, optimize the application's performance, check for bugs, and ensure enterprise-level code. [music] So, with that prompt, and at this point, it's not just scanning for one single thing. It's actually reviewing the whole application more broadly, looking at performance and stability and overall code quality. And that's the real value of this workflow here. You're not waiting for problems to show up later on, and you're also not relying only on manual review to catch everything. Rather, you're using this system to [music] audit the code, to surface issues, to suggest even improvements, and tighten things up before deployment. So, that by the end of it, what you have is code that is much closer to production-ready with a stronger level of quality assurance built into the process. [music] Now, again, what would normally take days of manual review and testing and back and forth can actually be compressed into a much faster cycle here, while still covering the areas that matter most. For our fourth [music] workflow, let's go ahead and talk about documentation. Now, one thing that slows teams down [music] fast is just missing documentation or worse, documentation that looks complete but is already outdated. And at that point, people just stop trusting it, and then we just go straight into the code. [music] And then, instead of moving quickly, now we're trying to kind of reverse engineer how everything works just to understand [music] the whole system. And then that just gets worse as the project grows. [music] Even if documentation starts out useful, it then usually falls behind once the code changes. Now, keeping it updated manually sounds simple, but in actual practice, it is one of the first things that people stop maintaining if we're being honest here. So, the advantage is that we can [music] generate it directly from the project itself. So, rather than writing everything out by hand, Base 44 can just do that for us. It can look through the code base. It can create documentation based on what is actually there. And that includes things like API documentation, code comments, [music] and even user guides that reflect the current implementation instead of an older version of it. So, here on your screen, you can see that with the calorie tracker app project, I'm going to go ahead and open up the source code, then I'm going to prompt Base 44 [music] with something like this. Please generate a thorough documentation of the project. Save it as a markdown file in the project's directory. [music] And from here, it then creates the documentation and places it directly into the project files as a.md [music] file. And that makes a huge difference here because now the documentation is being generated from the code and not from memory or from something written weeks ago. [music] It stays much closer to what the project actually does today, right now. So, instead of documentation becoming another thing that we have to keep chasing, [music] it rather just becomes part of the whole workflow. So, we get thorough project docs without having to write them all manually. And then the result is a code base that's just easier to understand. [music] It's easier to hand off to someone else, and it is much easier for a team to just stay aligned on as the project evolves. So, now the last workflow focuses on deployment, and this is one of those parts that people usually don't really think [music] much about until something goes wrong, of course. Now, building the app is one thing, but getting it live properly is a different process. Once deployments involve multiple environments, manual steps, and of course, those last-minute checks, it becomes very easy for small mistakes to cause even bigger problems later on, broken releases, [music] downtime, or changes that just don't behave the way that we expected in production. And that's why this part matters just so much. The more manual the deployment process [music] is, then the more room there is for human error. And just that's just the way things go. So, what makes this workflow different is that the deployment side is now being managed for you in a much more structured way. >> [music] >> Because instead of say handling everything manually before, the pipeline now takes care of the flow through testing, staging [music] validation, and production rollout, while also checking for issues along the way. And a lot of that happens behind the scenes. [music] So, there isn't really much to show beyond the publishing process itself. [music] But that is what the publishing flow is actually representing. So, when I publish this calorie tracker app, that's [music] the part highlighting how Base 44 handles that transition from development into a live deployed app. And that's really the value here. You're not manually coordinating every deployment step [music] yourself anymore, and you're not relying on guesswork once the app goes live. The system [music] itself is handling environment setup and rollout and error detection in the background, so that releases are a lot more reliable. So, in the end, you get a deployment process that just [music] feels much safer and obviously here a lot smoother. So, instead of pushing changes live and then just hoping that everything holds and works out, you now have a workflow built to reduce downtime, to catch issues early, and roll things back when needed. And that gives us a much more reliable path from finished project to live product without needing deep dev ops knowledge just to ship [music] properly. Now, this is where Base 44 really does separate itself from everybody else. A big reason traditional AI coding tools fall short is that they were never built to handle production development in a really smooth way. They can help with quick snippets or one-off tasks, but once you do try to build something real, the >> [music] >> gaps become obvious. Now, the first issue here is context because most tools have limited context windows. So, after a certain point, for example, they just kind of lose track of your code base, and then you just have to keep explaining yourself over and everything all over again. And then there's memory. If the tool does not remember previous decisions across sessions, then we lose continuity, which then means every new feature just starts with re-explaining the same structure, the logic, and direction all over again. And another problem is also that these tools, they usually sit outside your actual development environment. So, even if the output is useful, you still kind of have to move back and forth between the AI and your project, [music] copying, pasting, adjusting, and reconnecting everything manually. So, that disconnect slows our whole process down. [music] And when the tool cannot properly maintain state, it then just becomes almost impossible, really, to build more complex features cleanly because [music] every step feels disconnected from the last previous step. And that's where Base 44 starts to fix those [music] gaps because with Base 44, your project has persistent context. Your code base becomes part of the system's long-term memory, so it keeps track of the structure, the patterns, and the decisions already made previously. So, we're not wasting time repeating ourselves the same explanations every single time that we come back [music] to the project, and that alone really just makes a huge difference once the app starts getting even larger. And then you also have workflow automation, and this is a big one because it means that you can now chain multiple operations together into repeatable development processes. And what that means is that you're no longer just asking for one isolated output at a time. You're now creating a flow that can handle a full feature build in a more systematic way. And that makes the process much more reliable, especially when you're building the same kinds of things across [music] different parts of the same app. There's also the integration side here because Base 44 connects with a bunch of tools that actually matter during development, like say GitHub, deployment platforms, and testing frameworks as well. So, the work doesn't just live in some disconnected AI tab that you have to manually transfer out of. Rather, it [music] stays tied to the real project and the real workflow. And on top of that, there's also team collaboration. Because once you have a process that works, it doesn't have to stay with one person. Teams can share those workflows and use the same approach across projects, which then makes development just a lot more consistent and actually [music] helps everyone work from the same playbook. So, the real advantage here is that Base 44 isn't just helping you generate code. It gives you an environment where context stays in place and workflows can be repeated. Your tools are already connected, and then the whole process just carries forward properly as the project grows. So, now to tie everything together, here's what that actually looks like in a full build. So, as you can see, I'm going to start simple here and I'm going to prompt Base 44 with this. [music] Hey Base 44, please create an exercise tracking application, and that's it. Nothing complicated, just a very clear request to kick things off. [music] And from there, I'll switch into plan mode. And at this stage, as you can see, it doesn't immediately start building. Rather, it first asks me a few clarifying questions about my requirements. [music] And after answering those, it generates a full plan. And this plan already shows how it's [music] thinking about the structure here, things like the database setup, the API architecture, [music] and how the front-end components are also going to be organized. [music] And even though there isn't an existing codebase here yet, >> [music] >> you can see how it's establishing the patterns that it's going to follow. And once the plan does look good, I'm just going to go ahead and approve it and let it run. And after that, it begins building the application from scratch. And this [music] includes generating the database migrations, API endpoints, and front-end components all together. And you won't see each individual step happening because again, most of this just runs [music] behind the scenes. But that's the key point here, it's handling the full build process as one connected system [music] without any manual intervention. So, as the build completes, it also takes care of the supporting parts, like tests and documentation. So, it's not just creating the core feature here, it's producing a more complete setup around it. Then for deployment, I'm just going to go ahead and publish the app. And as you can see, that step handles the transition into a live environment, showing how deployment is then managed without needing to configure everything all manually ourselves. And when we look at the full flow here, that's where the whole difference really shows because what would normally take two to three days setting up the database, building back-end logic, creating the front-end, testing everything all over again, and then deploying, that gets reduced to around just 30 minutes. >> [music] >> So, from a single prompt to a fully built and deployed application, everything just moves through one continuous process without breaking it into disconnected steps. At some point, you're going to feel it. Things are working, but you're constantly going back to fix something, rethink a decision, or just clean things up after the fact. And you're making progress, but it's not as smooth as it could be, and small issues just start stacking up over time. [music] And that is usually when you realize it's not just about building features anymore. It's really also about how you're building them. So, the first strategy is pair programming with AI. And the main difference here is that you're not only using it to generate code after you've already decided everything yourself. You're also using it earlier in the process to [music] kind of talk through architecture decisions and trade-offs and implementation direction before anything even gets built. [music] So, that kind of back and forth usually leads to better decisions because now you're thinking through the whole structure first, not just reacting after the code is already there. You can also use those sessions to keep the build more consistent. [music] And within a session, you can establish the coding patterns, the structure, and also the naming style that you want it to follow, and that helps everything [music] just come out more aligned as you keep on building. So, now in Base 44 specifically, [music] this part has limits though. You can guide it within the session, but you can't really permanently teach it your coding preferences >> [music] >> across all future work. And you also can't create custom AI personas for different types of development inside the platform. So, that part applies more as a general strategy for AI development overall, even if not every tool supports it in the same way. So, the second strategy here is intelligent [music] error resolution. And this is where AI becomes a lot more useful than just [music] fixing whatever error message is in front of you. A stronger approach is using it to understand how [music] the problem moves through the full stack, front-end, back-end, database, and everything in between. So, you're fixing the actual cause, not just the [music] symptom. And that's what makes debugging just the whole lot faster and also more reliable, especially when the issue isn't just isolated to one layer. So, over time, this also becomes useful for pattern recognition. [music] When similar problems, for example, just keep showing up, AI can help identify those recurring patterns and then generate solutions based on them. And once you do start seeing those patterns clearly, you can go one step further and then just ask for preventive fixes so its future features don't just run into the same issue again. [music] And the third strategy here is performance optimization. This is another area where people usually just kind of wait too long because they only think about it once the app starts feeling slow. But AI can help much earlier in the process by analyzing [music] performance bottlenecks across the front-end, the back-end, and database without requiring you to manually profile every single part first. So, from there, it can then suggest optimizations and even give you a sense of what kind of impact [music] those changes might have before you apply them. And that makes it easier, just a whole bunch easier to decide what's actually worth improving. And when performance [music] issues do reach the data layer, it can then also help you with things like database query optimization and caching strategies based on how the app is [music] actually being used. So, these strategies are less about one feature and more just about how you work overall. And this is usually the point where AI stops being something you only call when you're stuck and more like becoming part of how you think through architecture and debugging and performance [music] from the very beginning. All right. So, at this point, you've seen [music] what this actually looks like when you stop using AI casually and really start using it with a system behind it. And if there's one thing to take away from this video, it's [music] this. Don't just prompt and hope. Build a flow, stick to it, and then let it compound. Try it out yourself. Pick one idea, run it through the same process, and then see how far you can take it. And that's it for this one. I want to thank you for investing your time with me today, and I'll see you at the next one.