A 5-step framework

0:00 Starting off with database/data management type apps, hardwarebased apps, dashboards, chatbot/ass assistant agent type apps, coaches/arning agent type apps, multimodality, and finally automation/mros. Now, before we dive into all the different app ideas, I do want to do a quick crash course on my five-step framework for how to approach building AI applications all the way from ideiation to deployment. This will give you a practical starting point for how to build these projects and the tools to use for this when you actually get really excited to build these applications.

0:46 A portion of this video is sponsored by Bault. The five-step framework is metaprompt, product requirements prompt, the PRP, implementation, debugging, and deployment. The first part of the framework is the meta prompt. And this is what I like to use in the very beginning in the ideation phase because it goes through and asks you questions like what's the purpose of the app? Who's the target audience? Who are you marketing? What are the core features? And it goes through all of these and forces you to think about who it is that your application is actually designed for.

1:08 Because after all, if you don't even know exactly what it is that you're building, it's kind of unfair to expect your AI to figure out how to build it, right? So, I'm actually going to provide you the meta prompt, which I'll link in the description that I like to personally use. Okay. So, after you think very deeply and answer all of these questions, the output of this meta prompt is going to be called a product requirements prompt, which is the second part of this framework, a PRP.

1:30 If you guys have heard about a product requirements document before, it's generally what a lot of product managers will use to have a very clear description of what their product is and all the core features surrounding it. So that meta prompt will help you generate this PRP. And what you do is that you take this PRP and you input it into your AI assisted coding tool. Just with a single prompt, you'll probably be able to get like 80 to 90% of your core set of features, which is honestly really really impressive.

1:53 But to get the rest of the 10 20% to actually make it into a full application, we do need to go on to the next third step which is the step of incremental implementation. You see, for the things that it doesn't get quite right, you do need to incrementally start asking it to change certain things like add a little bit more of this or change a little bit more of this, change the UI a little bit more.

2:15 Um, you know, add something else like maybe add like an audio functionality. And actually step three and step four go hand in hand and that is debugging. as you're working with the AI and getting it to implement more things. You're also going to be coming across errors or things that the AI is just not doing correctly. This is really really normal and part of the process. So when you do encounter these errors, the first thing that you want to do is just try to ask AI to fix it itself.

2:38 Literally just be like there's usually a button that you can just click and be like, you know, try to fix the error itself. Um or you can do things like take a screenshot of the error or just point out that something is not working like this button is not working. Then the AI is usually pretty good at going back and then fixing these errors. This is also going to be incremental. So you're going to be repeating this process of incrementally implementing a feature and then debugging it, implementing, debugging, implementing, debugging until towards the end, you get to a point in which you're happy with the features that have been implemented and it's functioning properly.

2:59 Then at that point, you're ready for step five, which is deployment. Honestly, there are a lot of things that I can talk about in this deployment section, including things like security, risk, version control, or to be hosting things. But the good news is that for most of these VIP coding AI assisted coding tools, they do have their own deployment options. Okay, so that is a really really quick crash course on building an AI application all the way from ideation to the deployment stage.

3:21 There is so much more that I can talk about and I actually made like full videos going through this process which I will link above over here and also in the description if you do want to go into more detail about these step-by-step processes. But for the rest of this video, I do want to focus on the AI apps that we are very excited to start building. Starting off with database/ data management type apps. This category of AI applications is all about leveraging AI's incredible ability of turning raw or unstructured data into something that you can search, query,