---
type: raw-transcript
title: "The ONLY AI Developer Roadmap You Need in 2024"
youtube_id: 2NjuPJjx3zI
url: https://www.youtube.com/watch?v=2NjuPJjx3zI
slug: the-only-ai-developer-roadmap-you-need-in-2024
published: 2024-06-26
duration: "0:15:53"
fetched_at: 2026-07-21
immutable: true
---

## Transcript

**[0:00:00]** I have spent years mastering artificial intelligence and I have taken everything I have learned and condensed it down into this AI developer Roat for you so you can go from zero to hero with artificial intelligence in weeks or months instead of years like it took me I can guarantee that if you follow these

**[0:00:18]** 10 steps and learn AI in this way you're going to be comfortable creating any AI application you could possibly dream of so I look forward to diving into this road map with you I think you'll see that as I clearly outline every everything it takes learn AI it's really

**[0:00:32]** not that bad and it is a super exciting Journey so without further Ado let's dive in all right so before we dive into the AI developer road map that I have laid out neatly for you here I just want to mention a couple of things the first thing is that for every resource that I

**[0:00:46]** have in this road map whether it be a YouTube video a book or some sort of course I will have the links for those in the description of this video so check that out if you're curious to look at any of these things the second thing is that this developer map is meant to

**[0:01:01]** take you from Zero to Hero with artificial intelligence you might be somewhere in the middle maybe you are already really good at Python and have a good understanding of large language models but it is really important to constantly brush up on the basics especially as you dive into the more

**[0:01:16]** complex topics like AI agents so really every single step of this road map will apply to you so with that let's go ahead and get started the first step is to master python this is the programming language for artificial intelligence there are the most people using it for AI which

**[0:01:34]** provides the most resources for you the most libraries out there it is also the most simple and Powerful language out there for developing AI applications and so if you want to get on the ground running the fastest use Python there are a lot of resources available for you

**[0:01:48]** things like YouTube tutorials you can find so many on YouTube uh code Academy's python course I would highly highly recommend and then if you want to dive a bit deeper and want some books to check out I'd first check out python crash course very very good book don't spend too long

**[0:02:03]** on python don't try to spend six months to master it but get good at the basics with functions and object oriented programming and while loops and best practices for con with python get good at that make a few projects make sure you're really comfortable with the basics and then move on to step number

**[0:02:20]** two which is the basics of AI and large language models it is crucial to learn the basics how AI models are created how they're trained how large language models work what are their pitfalls you need to know these things so if you don't there are going to be holes in

**[0:02:35]** your code things that just don't work because you are missing that fundamental understanding of how AI works again a lot of really good YouTube tutorials for these topics here one in particular that I really want to call up because this one is so so good is intro to llms by

**[0:02:51]** Matthew Burman Matthew Burman is a phenomenal YouTuber but this video in particular 25 minutes it'll save you hours of having to research how llms work it this gives you such a good understanding in a short period of time there's also a six-hour course corsera AI for Everyone by angre NG that I've

**[0:03:09]** done I'd highly recommend this one and then again if you want to dive even deeper into these topics and really understand a lot of the theory behind artificial intelligence I would definitely recommend checking out artificial intelligence a modor approach I read this book in college it was definitely worth my time for someone

**[0:03:24]** like me that wants to understand at a deep level the theory behind all this so maybe isn't necessarily for you but if you really want to understand these things and have a strong Foundation it's worth taking some time with this step that leads us to step number three which

**[0:03:37]** is prompt engineering at a basic level you need to understand how to talk to the AI whether it's going to be GPT CLA it doesn't matter all these large language models The Prompt engineering Concepts that you want to learn will apply to all of those now here's the

**[0:03:53]** thing with prompt engineering I want to be very careful with this and be clear it is not worth spending a ton of time on promp engineering there are a lot of YouTube gurus out there that tell you that prompt engineering is the most important thing with AI that you can get

**[0:04:07]** a job for $300,000 a year as just a prompt engineer and like this is where you should be focusing all your effort that is really not the case keep it simple don't spend a lot of time on this just a few hours is enough in fact there's just one resource that I highly

**[0:04:21]** recommend checking out which is prompting guide. it's a website that brings you through all the prompt engineering techniques in a very concise way uh things like like Chain of Thought uh self-reflection one shot few shot all the different prompt engineering techniques because you want to have that in your back pocket as you start to

**[0:04:38]** actually create prompts within your AI application so don't spend a lot of time on it but make sure you have the groundwork done for your understanding of prompt engineering and that takes us to step number four which is deep learning machine learning and natural language processing or NLP now I

**[0:04:55]** actually marked this as an optional step and this might be a little controversial because these are huge topics in AI but the reason I marked it as optional is because large language models like GPT and Claud are so powerful that a lot of times the use cases that you might want

**[0:05:12]** to develop with AI those applications that you've been dreaming of making you can do a GPT and cloud and there's not a huge reason for you to train and create your own models leveraging everything you need to know with deep learning machine learning all those more advanced AI topics and so so really this is your

**[0:05:31]** own discretion think about the things that you want to develop and how custom of models you might need or if you can just use out of the box generative AI Solutions like GPT and spend some time on this if you really think that you're going to need something more custom I'll

**[0:05:44]** give one example here because I just want to be really clear and then I'll move on to some recommendations for learning uh there's a YouTuber that I know his name is Steve and he's a creator of a really awesome tool called builder. basically you give figma website designs and it turns it into

**[0:05:58]** code for you and at first the actually tried to just use GPT to do this so he'd give an image of the figma website and ask for code and it did not do well with that because GPT is a very general trained model that can't do super specific things like that really really

**[0:06:13]** well so he actually leveraged his understanding of deep learning and machine learning to create a custom model that would take the figma website turn it into code and then he'd actually use GPT in the back end to just refine the code that his custom model created so he actually brought in a ton of

**[0:06:28]** trading data of different websites and stuff and built up his own model there are definitely use cases for that so this could be really really crucial for you depending on what you want to make uh as far as resources to learn these Advanced AI topics I've done all of

**[0:06:42]** these the corsera machine learning deep learning and NLP specializations uh also by Andrew NG like the previous corsera course these are so so good it'll take you a good amount of time to get through them uh but if you think that you might need to develop some sort of use case

**[0:06:58]** around a custom model you need learn this all right and that takes us to step number five which is to create projects with artificial intelligence and so right now my call to action is just to do something quite basic make basic applications using the open AI or anthropic SDK in Python so open AI is

**[0:07:17]** GPT and then anthropic is CLA those are the most powerful models right now so I'd recommend to keep it simple use what is best for now and then later you could always explore other models as well like mistol llama open source on ones like that explore however you want but that's

**[0:07:32]** just my recommendation to get started the most important thing here as you're starting to just use AI for some basic things is to get comfortable integrating AI in your applications you can build really really simple things like chat Bots text summarizers idea generators things where you're just talking to the

**[0:07:50]** AI with a very simple prompt getting back a response and doing something with it another really important thing here is to learn how to learn from SDK documentation so instead of relying on YouTube videos to teach you how to do any basic thing like using a new model

**[0:08:05]** or function calling you can actually leverage the documentation that open AI or anthropic whatever it might be provides to you because that will speed up your development a lot when you can just read through a couple quick things instead of watching a whole 10-minute YouTube video on a topic u and sometimes

**[0:08:21]** you need those videos that's what I'm here for but being able to leverage the documentation for quick things is crucial all right and that takes us to step number six and this is where things start to get juicy because we're talking about Lang chain Lang chain is my

**[0:08:36]** favorite tool out of any tool I ever use for anything AI related seriously Lang chain is amazing all it is is a suite of tools basically python libraries that help you build AI applications by taking care of a lot of the groundwork for you so instead of having to build chat

**[0:08:52]** memory from scratch or create chains from scratch or handle different outputs from AI from scratch you can use these tools that they have that you can Implement right in your code with very little effort to take care of a lot of stuff for you so you code faster your AI

**[0:09:06]** applications are more error free it is just a win-win all around Lang chain has amazing documentation uh which is just icing on the cake and then also official YouTube tutorials as well so it's very easy to learn Lane chain and it's important to get a good sense of what it

**[0:09:21]** can help you with because I just mentioned a couple of things listed here but there's so many other things that it can help you with it can help you uh with some other topics we'll talk about later like rag it can help with prompt templating there are so many things it

**[0:09:33]** is so incredibly powerful so spend time learning Lang chain uh next up one of the topics that I just mentioned is rag learning rag is crucial all rag stands for is retrieval augmented generation and what that means is using external knowledge with a large language model so basically like I said

**[0:09:54]** it is augmented generation you are augmenting an AI by giving it external knowledge and that is so so crucial because if the AI is isolated from the outside world and it doesn't have context it needs around your application it's not going to perform very well and so it's so important to learn how Vector

**[0:10:09]** databases and embeddings work because that is the backbone of rag Lang chain helps a lot with rag like I mentioned so they got great documentation on that and there's also pine cone which is my favorite Vector database they have great documentation on rag as well so spend time learning this because this is so

**[0:10:26]** important for AI agents which we'll get into and the rest of this road map because being able to have external knowledge within your AI applications is crucial for almost any use case all right so now step number eight we get to the AI agents which is of course my favorite topic and where I

**[0:10:43]** spend most of my time teaching on this YouTube channel here uh and as you can see here we got a big Lang Suite of products here so Lang chain is the core library that helps you build things with AI but you also have Lang graph which helps you create infrastructure around

**[0:10:59]** AI agent that actually talk to each other and do very complex and amazing things then you have Lang serve which helps you deploy these applications and Langs Smith which helps you get visibility into what's going on because once you get past just a single prompt to an AI and you have many agents

**[0:11:13]** working together you got to have a good idea what's going on where all the prompts are happening all the state that's being managed Lang Smith helps with that so learning all these tools is crucial and it takes some time this starts to get a bit complicated here but

**[0:11:27]** it is worth mastering it along with uh llm function calling um which is basically the functionality that gives AI the ability to interact with the outside world being able to interact with external databases apis to work with your services whatever it might be and that is super important that

**[0:11:44]** actually goes with AI agents a lot so you'll use function calling within AI agents that you create with the Lang Suite of tools um and so this is where you want to spend a lot of time once you have the basics down it is something that I'm constantly spending every day

**[0:11:56]** of my life on and you're going to want to as well and on top of this when you actually build these AI applications it's important to learn how to deploy them as well so being able to take this backend infrastructure that you've created which is your AI application and

**[0:12:11]** deploying it out to the cloud so that you don't just have it running on your laptop forever because you're never going to get other users to use it if that is the case so that's is Step number eight and honestly this is probably the step that takes the most

**[0:12:22]** time because this is where you really get to unlock the full power of AI with AI agents all right so moving on to step number nine now this one is a little bit different than the rest of them because we're actually talking about the front end it's crucial to be able to build

**[0:12:37]** front ends around your AI applications because otherwise how are users really going to be able to use them in a good way so it's important to learn HTML CSS and JavaScript web development stuff so you got Frameworks like react which is my favorite because of nextjs uh but you

**[0:12:52]** can also learn view spelt whatever it might be it's just important to now that you know how to from Step eight deploy AI applications which are the back end that runs your models and your agents and all that you need a front end to expose this functionality in a way that

**[0:13:04]** looks nice for your users it's also important to learn front-end technologies that help with this integration on the front end like the versell AIS SDK is actually my favorite I'm going to be making videos on this in the future it is such a great piece of technology to help make front

**[0:13:18]** ends that are so robust working with things in the background like um you know an agent application you have hosted with Lang serve and so just kind of tying those two things together there U and then of course course learning how to deploy your front endend applications just like you need to learn how to

**[0:13:32]** deploy your back end as well to make your full application Live for users to use because that's the end result is to build something amazing with AI that a ton of people can use and enjoy the benefits of so that is really the last main step here uh because going on to

**[0:13:47]** step number 10 here I just wanted to mention that this road map this will get you from Zero to Hero but there are definitely some other things you might need to learn depending on your use case and so I just want to call out a couple here and then just kind of give a call

**[0:14:00]** to action to like think about what you want to make and what other things you might need to learn in order to really make that the best application it can be so a couple of examples here is like fine-tuning large language models which is basically using a little bit of your

**[0:14:12]** own data to take a general AI like GPT and make it work even better on your use case you also have things like multimodal agents uh or models sorry so being able to actually work with sound work with text and images and not just text that's really crucial too then you

**[0:14:30]** also have explainable ai which is creating models that are actually able to help you understand the output that it is producing uh mixture of Agents using different AI models to work together better than one model could work by itself that's a really cool concept that I'll be talking a lot about

**[0:14:45]** more as well uh and then you also have learning app development so if you have a need for example to actually not just have your AI application be exposed in a website you might want to have it be on mobile as well so making an app learning app development is going to be crucial

**[0:14:59]** for that as well so these are just a few examples but really what it is is like take all the steps leading up to this master those things because they're going to be crucial no matter what and then just continue to build your skill set for the things that you need to

**[0:15:11]** actually make the application of your dreams so that is everything that I have for the AI developer road map I hope that this is really helpful for you my goal is just to make it so clear-cut what to learn for AI because it is overwhelming how much is out there how

**[0:15:26]** many different Technologies there are and different patterns of development and all the different topics around AI so I hope that this makes it easy for you to decide what path forward you want to move with learning AI so let me know if there's anything else that you'd want

**[0:15:38]** me to include any other videos on these topics you'd want me to make in the future I hope that you found this clear and helpful for you if you did I'd really appreciate a like and a subscribe anyway I will see you in the next video
