AI Learning Roadmap 2025

Ever feel like you've been chasing the wrong AI path? Look, I get it. I wasted months jumping from one trend to the next, following outdated advice that promised quick results, but only left me stuck. In 2025, AI isn't about just learning the basics. It's about mastering the skills companies are crying out for right now. If I had to start my AI journey over, I'd do it differently. I'd skip the fluff and dive straight into what works in today's world. And in this video, I'm sharing exactly how I'd learn AI from scratch in 2025. No gimmicks, just actionable strategies that will fast track your success. Let's dive in. So first of all, thank you for coming back and a big welcome to all of our new subscribers joining us today. My name is Jake Dawson and around here we keep AI sales automation simple, practical and actually useful. No fluff, no theory, just stuff you can use right now to grow your business. Today, I'm walking you through the exact roadmap. I wish I had when I started learning AI. Not one of those, just take this course and you're good kind of thing. No, this is a step-by-step, no BS version with the tools, the mindset, and the exact moves I'd make if I had to start from zero in 2025. All right, so now that you know why this matters and how we're cutting through the noise, let's just jump into the first step of the actual roadmap. So step number one, understanding the basics of AI and how it fits into automation. You don't need to be a developer. But you do need to understand a few core things so you don't get stuck later on. Think of this like learning to drive. You don't need to build the car, but you should know what the pedals do. Let's start with the essential technical stuff. And I promise I won't make your eyes glaze over. First, you'll run into something called JSON. Just think of this as a way that apps talk to each other using data. It's like a shared language made up of key value pairs. You'll see things like curly brackets and little labels like name or email. and And you don't need to write JSON, but it's good to recognize what you're looking at when it pops up in tools like Make.com or when ChatGPT gives you a structured output. Now another buzzword that gets thrown around a lot is API. Don't panic. API is just a fancy way of saying, hey app, can you go fetch this thing for me or send this info somewhere else? Most tools you'll use will already be hooked up with APIs, so you won't need to touch them directly. knowing what they do helps you connect the dots when things aren't working out. Now, let's talk AI-specific stuff. You'll want to understand how to write prompts that actually get results. That means learning what a system message is versus a user message. Basically, system messages tell the AI what kind of personality or behavior it should have. Like saying, you're a helpful marketing assistant. And then the user message is where you give it instructions. Knowing the difference helps you get a way better output, especially in tools like ChatGPT or Claude. And if you've ever seen settings like temperature, here's the deal. It's just a slider for how creative the AI gets. Low temperature is super safe and predictable. High temperature is a little wild, sometimes genius, sometimes complete nonsense. When you're doing automation stuff, you usually want to keep it low, like 0.2 or 0.3, so it sticks to the script and doesn't go rogue. Also, get used to giving formatting instructions. Literally say, give me the output in bullet points. or format this as a table. And you'll be surprised how many people forget that AI can follow specific formatting rules. It just needs to be told. All right, once you've got those basics, now you move into figuring out what to automate. This is where people go off track. They try to automate the hardest stuff first. Don't do that. Instead, look at your day to day, ask yourself, is there anything that I do in a repetitive way that's kind of boring and happening over and over again? Let me give you some no-brainer examples. Emailing processing is a goldmine. You can have the AI categorize incoming emails by topic or urgency. You can even generate quick reply templates based on what the sender's asking. If you're constantly answering the same kind of questions, this alone will save you a couple of hours a week. And then there's content work. Maybe you're writing blog posts, product descriptions, social captions. It's all copy. And AI is scary good at this now. You could feed it a product spec sheet and get back a polished description or take a long blog and have it. turned into five social media posts. It's fast and with the right prompts, it's clean. Now, how do you know if something's worth automating? I use a quick four question test. Can I clearly explain what the input looks like and what I want out of it? Does it happen in the same way every time? Could I explain it to someone in steps under a minute? And will this save me at least 30 minutes a week if I automate it? If all the answers are yes, that's a perfect task for me to automate. Cool. So now that you've got the what, let's set up the where, your AI workspace. You'll want to create accounts on three main platforms right now. ChatGPT, DeepSeq and Genspark. Start with ChatGPT. Inside, create separate chats for different use cases. Don't dump everything into one thread. If it's research, use one thread. If it's email summaries, use another. You'll thank me later when you're trying to find past prompts. If you've got GPT-4 or 4.0, turn on search so it can pull in real-time info. Or switch to GPT-04 Mini High if you want to to do anything like chart generation, CSV analysis. or simple number crunching. You don't have to use it right now, but just having it up and on opens up some cool options. Then jump onto DeepSeek. This one's a beast when it comes to handling complex stuff. It's built for deeper reasoning, long document analysis, and organizing messy info into clean formats like tables or JSON without needing you to touch any code. What sets DeepSeek apart is the expanded context window. That means you can feed it entire reports, multiple articles or long conversations, and it won't get confused halfway through like some other AIs do. It actually holds the thread and gives you a clear step-by-step breakdown. It's also super handy for content work. You can generate multilingual posts, rewrite messages for different platforms, or turn scattered notes into structured drafts. And if you're doing research, DeepSeek can pull the key insights, compare sources, and spit out a clean summary that actually makes sense. And the best part? It's more cost efficient than a lot of the other big name models out there, especially for math heavy tasks or detailed analysis. So if you're looking to automate smarter, not harder, this is one of the tools I definitely have in your stack. Now let's talk about GenSpark. This one's wild in a good way. GenSpark isn't just another AI that spits out code or answers your questions. It's a full on no code super agent that can actually build things for you. I'm talking real websites, interactive games. custom tools done in minutes from a simple text prompt. You tell it what you want and it doesn't just give you a snippet or a mock-up, it gives you the whole thing ready to use. What makes it special is this setup called mixture of agents. It basically means GenSpark isn't just one AI, it's a team of specialized bots working together. One's handling content, one's doing research, another maybe is even making a phone call to a business if needed. I'm not joking. It can literally place calls on your behalf. It also creates videos, presentations, social media content, you name it. And it can handle multi-step workflows too. So it's not just about building stuff. It can automate how those things actually run. If you're new to all this, no worries. It's got a generous free plan, 200 credits a day. That's more than enough to get real stuff done. And this isn't like other assistants that just give you ideas and leave you hanging. GenSpark actually does the work. It thinks, plans. and executes tasks across different domains without you again needing to write a single line of code amazing all right now that your tools are set up and you've identified a few solid automation tasks let's move on to the part that actually makes ai work for you how you talk to it this is the part that most people skip and then they wonder why the ai gives them garbage responses so step two is all about mastering basic prompting and if you've ever typed something into chat GPT and have gotten back total. useless information, this is the fix. Think of prompt engineering like learning a new language. You wouldn't explain something to your five-year-old niece the same way you'd explain it to your accountant, right? AI is the same way. It responds best when you give it clear structure, simple context, and a little bit of direction. It's smart, but it's not a mind reader yet. So first, you need to understand the idea of the context window. This is basically how much memory the AI has to keep track of your conversation. Each tool has a limit. GPT-4-0 has a pretty solid memory. DeepSeq gives you an even bigger space to work with. But others like Claude might max out sooner depending on what plan you're on. The point is, don't ramble. If your request has 12 moving parts, keep it tight. Split long tasks into smaller chunks and summarize what you've already covered when you hit the limit. Next up, output formatting. This is one of those things where just saying it out loud makes it better. Literally tell the AI how you want the answer. Give it to me as a checklist or summarize in three bullet points or put this into a table. You'll be shocked how much better the output gets when you're just specific about what it should look like. And if you're feeding that output into another tool like Google Sheets or Make.com, formatting is everything. A clean table saves you from a huge headache later. Now. Here's where it gets fun. Zero shot versus few shot. Zero shot is when you ask an AI to do something with no example. Like write me a tweet about this blog post. That works sometimes, but if the AI starts missing out, the tone or the structure is weird, you switched a few shot. And that just means you showed a few examples of what you do like and then ask it to make something similar. So you'd paste in two great tweets you wrote and then say, now make five more like these. The AI goes, oh, got it. And usually nails it. You're basically training it in real time. And here's the cool part. You can save these prompts as templates. So if you always write social posts from blog articles, Just build a prompt like, here's a blog post. Turn it into a three-line tweet and a one-paragraph LinkedIn post. Keep it casual and direct. Done. Now you've got a plug-and-play prompt for content every week. Same thing for email replies, summaries, lead outreach. Look, once you build a good prompt, save it. At this stage, I recommend starting your own little prompt library. Just a basic Google Sheet will do. Column one, what's the task? Column two, the actual prompt. Column three, what tool will work best with it? You'll eventually build up a go-to toolkit that saves you hours. And while we're on tools, here's how to pick the right one for prompting. If you're doing everyday stuff like summarizing content, writing posts, or replying to emails, ChatGPT is your main player. Super fast, super solid. For more complex or structured outputs, like when you're building prompts that need to follow exact rules or layouts, DeepSeek shines here. It's more stable with long inputs and can hold complicated structures better. And if you're building something interactive or multi-step, like a content pipeline that turns into an idea, into a post, into an image, caption, hashtags, this is where GenSpark really becomes the player. You can build out the whole flow using a mix of their tools with almost no setup. So let me give you a real world example. Say you run a coaching business and you want to automate your weekly content. You feed your blog post into ChatGPT and say, summarize this into two punchy bullet points and write a motivational quote inspired by the main idea. Then paste that into your prompt template and you're done in 60 seconds. Next week, just drop in the new blog, same prompt, same format, new content. And this is how you start thinking like a prompt engineer, even if you've never written a line of code. The success sign here is when you ask for something and the AI gives you exact. what you want it for the first time. No rewrites, no actually can you try it again, just done. And you'll notice that once your prompts get stronger, your entire workflow smoothens out because now you're not wasting time rephrasing things or hoping the AI gets it, you're in control. And when you hit that point where you've got a growing list of reliable prompts, the right tools dialed in, and you're getting clean outputs every time, you're ready to move on because now the AI is not just helping you, It's working with you. All right, now that you've got your prompt skills dialed in and the tools are starting to feel like second nature, it's time for the fun part, actually building your first simple automations. This is where things start clicking. You're not just testing ideas anymore. You're saving time, creating tools, and getting real results, again, without writing any code. Now, don't worry. We're not jumping into some massive 10-step AI machine just yet. We're gonna start small, but smart. Think of your first automation as a little digital assistant that handles one annoying task for you perfectly every time. So here's how to think about it. Every automation starts with a workflow. That's just a fancy word for a step-by-step process that turns input into output. Like someone fills out a form on your site and boom, AI writes a response, formats it, and sends it to your inbox, one click, zero typing. You'll also start using templates. If you've got something you do often, like writing intro emails, summarizing blog posts, or creating weekly social content, you don't need to start from scratch each time. Build a basic version once and then just change the parts that matter. The name, the topic, the offer. Those are your input variables. You just plug them in and the AI handles the rest. Let's look at a real example. Let's say you're a consultant and you get the same five questions over and over again in your DMs. Instead of replying every single time, you set up a system. Someone fills out a form with their question, make.com pulls the input, sends it to ChatGPT with your response template, and emails them back a clean, professional answer. That right there is a reusable response system. And once you've set it up, it just runs. To build something like that, you need to understand how your tools talk to each other. That's what we call integration points. So if your workflow starts in Google Forms, goes through make.com, hits ChatGPT, and then drops into Gmail or Slack, you need to be clear on what output from the chat you're going to be getting. One tool becomes the input for the next. Otherwise, it's like trying to plug a toaster into a garden hose. It doesn't work, people. This is also where documentation starts to matter. I know it's tempting to just wing it and hope it all works out. But trust me, write down your steps. Literally open up a Google Doc and write it out. What's happening? Step one. When someone fills out the form, step two, make .com triggers, chat GPT, step. Step three, format the response. Step four, send the email. That way, if something breaks or you want to improve it later, you're not just staring at a spaghetti mess, wondering what you built last Tuesday. Now, tool selection is key. This is where understanding what each platform does best makes everything easier. If you want to generate text, ChatGPT is still your go-to. If your task involves organizing messy info or building a structured report, use DeepSeek. And if you want to build something that actually does stuff, like make a little app, turn a prompt into a working landing page, or run a multi-step workflow across content and cause, GenSpark. This is your best bet. It's built for non-coders who just want real tools, not just fancy replies. Now, let's say you're working with a mixed type of content. Text, images, maybe even a spreadsheet. This is called multimodal processing. Tools like GPT-4-0 and GenSpark are available. are good at this. So if you want to take a screenshot of a sales dashboard and drop it into the chat and say, summarize my top performing product, it can actually do that. It's wild. So here's your first project idea to start with. Create a simple automation that takes a customer inquiry and turns it into a polished reply. You can use Google Sheets to collect the questions, Make.com to connect the sheet to ChatGPT, and Gmail to send the final response. Keep the prompt simple. Write a friendly email answering this question in under 150 words. Keep the tone helpful and professional. Then test it with a few dummy entries and see how it feels. You'll get the hang of it fast. And once you've got one automation running, something clicks. You'll start noticing all the other tasks that you can automate. Content creation, lead scoring, product descriptions, research summaries, and you will build a small collection of custom tools that feel like your personal AI army. And here's how you'll know it's working. You'll start saving actual time every week. You'll spend less time formatting or rewriting, and you'll see clearer, more consistent outputs from the AI. And you'll stop second-guessing your tools because you'll actually understand what's going on behind the scenes. This is the phase where your skills go from theory to real-world application. You're not just playing with AI anymore. You're building your own systems. That's the difference maker. And once these simple automations are locked in, we'll move on to optimizing them so they can scale, get smarter, and handle even more of your work behind the scenes. All right, so now that you've built a few solid automations and you're feeling more confident with tools like ChatGPT, DeepSeq, and GenSpark, it's time to really level things up. This is where we move from building simple one-off tasks to connecting everything into a full-on system that actually runs your workflow while you sleep or scroll TikTok or pretending like you're busy. This step is all about connecting tools together to create more powerful multi-step automations. We're going to use platforms that act like the glue between your apps. They move data from one tool to another, trigger things based on conditions, and keep everything humming behind the scene. First, you want to pick your automation platform. I always recommend starting with Make.com. It's the most powerful. And yeah, it looks a little intense at first, but the visual builder makes it easy to follow. If that feels like too much, Zapier is more beginner friendly, though it has some limits. And if you just want to automate, like when I post on Instagram, save it to Google Drive. If that, then that is the easiest, but it won't go far. Whichever one you choose, go through their getting started tutorial. Trust me, it's not a waste of time. It walks you through setting up your first scenario, picking trigger events, and connecting to your accounts. It's like learning to cook one meal before trying to open a restaurant. So once you're set up, start with a few basic integrations. For example, have your email connect to ChatGPT. So when a specific type of email comes in, it gets automatically processed and summarized. You could also create a data collector, like having new form entries or messages go straight into Google Sheet. or build a notification. notification system like when someone fills out your contact form you get pinged in slack with the summary and the follow-up reminder now Test each one and I mean really test it don't wait for it to break when you're not looking, try different inputs, make sure the data flows correctly, and watch what happens when the input isn't perfect. Because spoiler alert, real world inputs are almost never perfect. Once you're comfortable, it's time to stack things together into real workflows. Let's say you want to automate your content production pipeline. You could start with deep seek, pulling insights from articles or forums. Then ChatGPT takes that info and drafts the post. After that, use another AI to clean up the language, maybe add a quote or a stat, and then send it to a scheduling tool like Buffer or Notion to post it later. That's a full pipeline, start to finish, and you didn't touch any single Google Doc or social media app yourself. Or picture this, you run a small agency and want to handle leads automatically. So you build a workflow like this. A lead comes in through a type form. Info gets added to a Google Sheet. ChatGPT writes a personalized follow-up, Gmail sends it, your CRM logs it, and you just built a customer interaction system that works while you're at the gym, or let's be honest, while you're pretending to be at the gym. And here's where it gets really cool. You can start combining multiple AI tools in one float to play to their strengths. So maybe DeepSeq handles the heavy research part like comparing five competitors or analyzing survey responses. Then ChatGPT takes that output and writes a summary or short report. And if you want to share it with a client or team, Genspar can turn that into a web page or a clean PDF, maybe even a slide deck if you're feeling fancy. If something doesn't work, like maybe one tool gives you a weird output or misses the point entirely, this is where you add fallback steps. You could build in logic that says, if this fails, try again with a simplified version. Or if it returns nothing, send me an alert to review. That's called error handling. And it keeps things from falling apart when AI does AI things. You'll know when you're hitting the milestone when you've got at least one full system running that involves three or more tools. Not just AI writes an email, but AI reads something, transforms it, passes it to another tool, and something useful happens. You're saving real time there. Your output's more consistent and things just work. So at this point, you've got real systems running. You've built a few automations, connected your tools, and they're doing actual work for you without babysitting them every five minutes. That's already a huge win. But here's the thing. This isn't the finish line. It's just the launch pad. Because the real magic happens when you start optimizing and expanding what you've built. This phase is ongoing. It's where you stop thinking of AI. as a one-off helper and start treating it like a partner that keeps getting better over time. You're not just reacting anymore, you're iterating, improv-ing, and thinking a few steps ahead. Let's start with refining how you talk to your AI tools. You already know the basics of prompting, but now we're stepping into advanced prompt engineering. This is about taking control of how the AI thinks, getting super specific with your instructions, setting behaviors up front, even stacking instructions in a way that guides the AI through a thought process. You're not just saying, write a paragraph. You're saying, analyze this, reason it out step by step, then summarize it with clear bullet points. And yep, that's actually a thing called chain of thought prompting. It's like telling the AI, show your work. And it usually delivers better, more accurate responses, especially for anything analytical or multi-layered. Next up, quality control. You want to make sure the stuff your AI is spitting out is actually good. That means setting up checkpoints in your workflow. Before a response goes to a client, maybe it gets reviewed. Or you tell the AI to self-check its own output. Review this tone, grammar, and formatting before finalizing. You can also build workflows that compare two outputs and pick the better one. Nerdy? Sure. But super useful when consistency matters. And now we talk integration. This is where you get strategic. You're not just using tools side by side anymore. You're combining them to create systems. DeepSeek pulls data, ChatGPT writes the summary, GenSpark builds the output, and your automation platform pushes it wherever it needs to go. You start thinking like a builder, not just a user. But here's the key. You can't grow if you're not learning. So set up a routine that works for you. Subscribe to a couple good AI newsletters. I personally recommend the ones that don't waste your time with a bunch of fluff. Just give you the real tools, updates, and use cases. Block off a couple hours a week to try something new. That could be testing out a new feature in make.com or comparing chat GPT outputs between... Seek and GPT build a learning backlog. It's like a to-do list, but for ideas that you want to try when you've got time. And now that you're actually building useful stuff, start documenting your wins. Make before after snapshots of how much time and effort you're saving. Record a little screen shares of you walking through what you built. Even if you're just talking through a workflow in Loom, it shows that you're making progress. Write down what the problem was. what the AI now handles and what changed. That's your portfolio. And if you want to speed things up even more, hey, join my community. Seriously, it's not just a shameless plug. This is where people are sharing their best automations, giving feedback and figuring out the smart stuff together. It's where I drop tons of templates, breakdowns and updates before they even hit YouTube. You can post your projects there, ask for feedback or just lurk and learn. you'll pick up more in a month from this group than six months trying to do this on your own. And the final move here is staying sharp. The AI world is changing fast. The tools you're using now are probably going to look different in three months. So schedule a little time every quarter to update your automations, tweak your prompts, and check out what's new. Keep growing, keep improving, and keep building things that actually work for you. Hey, if you made it this far, congrats. You now have the skills, the tools, and the mindset to not just use AI, but to build with it. And that's the difference between being stuck in tutorial mode and actually running a smarter, faster business. Let's keep pushing. Oh, and before you click away, here's another video you'll probably want to watch next. It's packed with even more tips to help you crush it with automation. I'll see you there.

It's about mastering the skills companies are crying out for right now. If I had to start my AI journey over, I'd do it differently. I'd skip the fluff and dive straight into what works in today's world. And in this video, I'm sharing exactly how I'd learn AI from scratch in 2025. No gimmicks, just actionable strategies that will fast track your success.

Let's dive in. So first of all, thank you for coming back and a big welcome to all of our new subscribers joining us today. My name is Jake Dawson and around here we keep AI sales automation simple, practical and actually useful. No fluff, no theory, just stuff you can use right now to grow your business. Today, I'm walking you through the exact roadmap.

I wish I had when I started learning AI. Not one of those, just take this course and you're good kind of thing. No, this is a step-by-step, no BS version with the tools, the mindset, and the exact moves I'd make if I had to start from zero in 2025. All right, so now that you know why this matters and how we're cutting through the noise, let's just jump into the first step of the actual roadmap. So step number one, understanding the basics of AI and how it fits into automation.

You don't need to be a developer. But you do need to understand a few core things so you don't get stuck later on. Think of this like learning to drive. You don't need to build the car, but you should know what the pedals do.

Let's start with the essential technical stuff. And I promise I won't make your eyes glaze over. First, you'll run into something called JSON.

Just think of this as a way that apps talk to each other using data. It's like a shared language made up of key value pairs. You'll see things like curly brackets and little labels like name or email.

and And you don't need to write JSON, but it's good to recognize what you're looking at when it pops up in tools like Make.com or when ChatGPT gives you a structured output. Now another buzzword that gets thrown around a lot is API. Don't panic. API is just a fancy way of saying, hey app, can you go fetch this thing for me or send this info somewhere else?

Most tools you'll use will already be hooked up with APIs, so you won't need to touch them directly. knowing what they do helps you connect the dots when things aren't working out. Now, let's talk AI-specific stuff.

You'll want to understand how to write prompts that actually get results. That means learning what a system message is versus a user message. Basically, system messages tell the AI what kind of personality or behavior it should have.

Like saying, you're a helpful marketing assistant. And then the user message is where you give it instructions. Knowing the difference helps you get a way better output, especially in tools like ChatGPT or Claude. And if you've ever seen settings like temperature, here's the deal.

It's just a slider for how creative the AI gets. Low temperature is super safe and predictable. High temperature is a little wild, sometimes genius, sometimes complete nonsense.

When you're doing automation stuff, you usually want to keep it low, like 0.2 or 0.3, so it sticks to the script and doesn't go rogue. Also, get used to giving formatting instructions. Literally say, give me the output in bullet points.

or format this as a table. And you'll be surprised how many people forget that AI can follow specific formatting rules. It just needs to be told. All right, once you've got those basics, now you move into figuring out what to automate. This is where people go off track.

They try to automate the hardest stuff first. Don't do that. Instead, look at your day to day, ask yourself, is there anything that I do in a repetitive way that's kind of boring and happening over and over again?

Let me give you some no-brainer examples. Emailing processing is a goldmine. You can have the AI categorize incoming emails by topic or urgency.

You can even generate quick reply templates based on what the sender's asking. If you're constantly answering the same kind of questions, this alone will save you a couple of hours a week. And then there's content work.

Maybe you're writing blog posts, product descriptions, social captions. It's all copy. And AI is scary good at this now.

You could feed it a product spec sheet and get back a polished description or take a long blog and have it. turned into five social media posts. It's fast and with the right prompts, it's clean. Now, how do you know if something's worth automating?

I use a quick four question test. Can I clearly explain what the input looks like and what I want out of it? Does it happen in the same way every time?

Could I explain it to someone in steps under a minute? And will this save me at least 30 minutes a week if I automate it? If all the answers are yes, that's a perfect task for me to automate.

Cool. So now that you've got the what, let's set up the where, your AI workspace. You'll want to create accounts on three main platforms right now.

ChatGPT, DeepSeq and Genspark. Start with ChatGPT. Inside, create separate chats for different use cases. Don't dump everything into one thread.

If it's research, use one thread. If it's email summaries, use another. You'll thank me later when you're trying to find past prompts. If you've got GPT-4 or 4.0, turn on search so it can pull in real-time info. Or switch to GPT-04 Mini High if you want to to do anything like chart generation, CSV analysis.

or simple number crunching. You don't have to use it right now, but just having it up and on opens up some cool options. Then jump onto DeepSeek. This one's a beast when it comes to handling complex stuff. It's built for deeper reasoning, long document analysis, and organizing messy info into clean formats like tables or JSON without needing you to touch any code.

What sets DeepSeek apart is the expanded context window. That means you can feed it entire reports, multiple articles or long conversations, and it won't get confused halfway through like some other AIs do. It actually holds the thread and gives you a clear step-by-step breakdown. It's also super handy for content work. You can generate multilingual posts, rewrite messages for different platforms, or turn scattered notes into structured drafts.

And if you're doing research, DeepSeek can pull the key insights, compare sources, and spit out a clean summary that actually makes sense. And the best part? It's more cost efficient than a lot of the other big name models out there, especially for math heavy tasks or detailed analysis.

So if you're looking to automate smarter, not harder, this is one of the tools I definitely have in your stack. Now let's talk about GenSpark. This one's wild in a good way.

GenSpark isn't just another AI that spits out code or answers your questions. It's a full on no code super agent that can actually build things for you. I'm talking real websites, interactive games.

custom tools done in minutes from a simple text prompt. You tell it what you want and it doesn't just give you a snippet or a mock-up, it gives you the whole thing ready to use. What makes it special is this setup called mixture of agents.

It basically means GenSpark isn't just one AI, it's a team of specialized bots working together. One's handling content, one's doing research, another maybe is even making a phone call to a business if needed. I'm not joking.

It can literally place calls on your behalf. It also creates videos, presentations, social media content, you name it. And it can handle multi-step workflows too.

So it's not just about building stuff. It can automate how those things actually run. If you're new to all this, no worries. It's got a generous free plan, 200 credits a day.

That's more than enough to get real stuff done. And this isn't like other assistants that just give you ideas and leave you hanging. GenSpark actually does the work. It thinks, plans. and executes tasks across different domains without you again needing to write a single line of code amazing all right now that your tools are set up and you've identified a few solid automation tasks let's move on to the part that actually makes ai work for you how you talk to it this is the part that most people skip and then they wonder why the ai gives them garbage responses so step two is all about mastering basic prompting and if you've ever typed something into chat GPT and have gotten back total.

useless information, this is the fix. Think of prompt engineering like learning a new language. You wouldn't explain something to your five-year-old niece the same way you'd explain it to your accountant, right? AI is the same way.

It responds best when you give it clear structure, simple context, and a little bit of direction. It's smart, but it's not a mind reader yet. So first, you need to understand the idea of the context window. This is basically how much memory the AI has to keep track of your conversation. Each tool has a limit.

GPT-4-0 has a pretty solid memory. DeepSeq gives you an even bigger space to work with. But others like Claude might max out sooner depending on what plan you're on.

The point is, don't ramble. If your request has 12 moving parts, keep it tight. Split long tasks into smaller chunks and summarize what you've already covered when you hit the limit. Next up, output formatting. This is one of those things where just saying it out loud makes it better.

Literally tell the AI how you want the answer. Give it to me as a checklist or summarize in three bullet points or put this into a table. You'll be shocked how much better the output gets when you're just specific about what it should look like.

And if you're feeding that output into another tool like Google Sheets or Make.com, formatting is everything. A clean table saves you from a huge headache later. Now. Here's where it gets fun.

Zero shot versus few shot. Zero shot is when you ask an AI to do something with no example. Like write me a tweet about this blog post.

That works sometimes, but if the AI starts missing out, the tone or the structure is weird, you switched a few shot. And that just means you showed a few examples of what you do like and then ask it to make something similar. So you'd paste in two great tweets you wrote and then say, now make five more like these.

The AI goes, oh, got it. And usually nails it. You're basically training it in real time. And here's the cool part.

You can save these prompts as templates. So if you always write social posts from blog articles, Just build a prompt like, here's a blog post. Turn it into a three-line tweet and a one-paragraph LinkedIn post.

Keep it casual and direct. Done. Now you've got a plug-and-play prompt for content every week. Same thing for email replies, summaries, lead outreach. Look, once you build a good prompt, save it.

At this stage, I recommend starting your own little prompt library. Just a basic Google Sheet will do. Column one, what's the task?

Column two, the actual prompt. Column three, what tool will work best with it? You'll eventually build up a go-to toolkit that saves you hours. And while we're on tools, here's how to pick the right one for prompting. If you're doing everyday stuff like summarizing content, writing posts, or replying to emails, ChatGPT is your main player.

Super fast, super solid. For more complex or structured outputs, like when you're building prompts that need to follow exact rules or layouts, DeepSeek shines here. It's more stable with long inputs and can hold complicated structures better.

And if you're building something interactive or multi-step, like a content pipeline that turns into an idea, into a post, into an image, caption, hashtags, this is where GenSpark really becomes the player. You can build out the whole flow using a mix of their tools with almost no setup. So let me give you a real world example. Say you run a coaching business and you want to automate your weekly content. You feed your blog post into ChatGPT and say, summarize this into two punchy bullet points and write a motivational quote inspired by the main idea.

Then paste that into your prompt template and you're done in 60 seconds. Next week, just drop in the new blog, same prompt, same format, new content. And this is how you start thinking like a prompt engineer, even if you've never written a line of code. The success sign here is when you ask for something and the AI gives you exact. what you want it for the first time.

No rewrites, no actually can you try it again, just done. And you'll notice that once your prompts get stronger, your entire workflow smoothens out because now you're not wasting time rephrasing things or hoping the AI gets it, you're in control. And when you hit that point where you've got a growing list of reliable prompts, the right tools dialed in, and you're getting clean outputs every time, you're ready to move on because now the AI is not just helping you, It's working with you. All right, now that you've got your prompt skills dialed in and the tools are starting to feel like second nature, it's time for the fun part, actually building your first simple automations. This is where things start clicking.

You're not just testing ideas anymore. You're saving time, creating tools, and getting real results, again, without writing any code. Now, don't worry. We're not jumping into some massive 10-step AI machine just yet. We're gonna start small, but smart.

Think of your first automation as a little digital assistant that handles one annoying task for you perfectly every time. So here's how to think about it. Every automation starts with a workflow.

That's just a fancy word for a step-by-step process that turns input into output. Like someone fills out a form on your site and boom, AI writes a response, formats it, and sends it to your inbox, one click, zero typing. You'll also start using templates. If you've got something you do often, like writing intro emails, summarizing blog posts, or creating weekly social content, you don't need to start from scratch each time.

Build a basic version once and then just change the parts that matter. The name, the topic, the offer. Those are your input variables.

You just plug them in and the AI handles the rest. Let's look at a real example. Let's say you're a consultant and you get the same five questions over and over again in your DMs. Instead of replying every single time, you set up a system. Someone fills out a form with their question, make.com pulls the input, sends it to ChatGPT with your response template, and emails them back a clean, professional answer.

That right there is a reusable response system. And once you've set it up, it just runs. To build something like that, you need to understand how your tools talk to each other. That's what we call integration points.

So if your workflow starts in Google Forms, goes through make.com, hits ChatGPT, and then drops into Gmail or Slack, you need to be clear on what output from the chat you're going to be getting. One tool becomes the input for the next. Otherwise, it's like trying to plug a toaster into a garden hose. It doesn't work, people. This is also where documentation starts to matter.

I know it's tempting to just wing it and hope it all works out. But trust me, write down your steps. Literally open up a Google Doc and write it out. What's happening? Step one.

When someone fills out the form, step two, make .com triggers, chat GPT, step. Step three, format the response. Step four, send the email.

That way, if something breaks or you want to improve it later, you're not just staring at a spaghetti mess, wondering what you built last Tuesday. Now, tool selection is key. This is where understanding what each platform does best makes everything easier. If you want to generate text, ChatGPT is still your go-to. If your task involves organizing messy info or building a structured report, use DeepSeek.

And if you want to build something that actually does stuff, like make a little app, turn a prompt into a working landing page, or run a multi-step workflow across content and cause, GenSpark. This is your best bet. It's built for non-coders who just want real tools, not just fancy replies.

Now, let's say you're working with a mixed type of content. Text, images, maybe even a spreadsheet. This is called multimodal processing. Tools like GPT-4-0 and GenSpark are available.

are good at this. So if you want to take a screenshot of a sales dashboard and drop it into the chat and say, summarize my top performing product, it can actually do that. It's wild. So here's your first project idea to start with. Create a simple automation that takes a customer inquiry and turns it into a polished reply.

You can use Google Sheets to collect the questions, Make.com to connect the sheet to ChatGPT, and Gmail to send the final response. Keep the prompt simple. Write a friendly email answering this question in under 150 words. Keep the tone helpful and professional. Then test it with a few dummy entries and see how it feels.

You'll get the hang of it fast. And once you've got one automation running, something clicks. You'll start noticing all the other tasks that you can automate. Content creation, lead scoring, product descriptions, research summaries, and you will build a small collection of custom tools that feel like your personal AI army.

And here's how you'll know it's working. You'll start saving actual time every week. You'll spend less time formatting or rewriting, and you'll see clearer, more consistent outputs from the AI.

And you'll stop second-guessing your tools because you'll actually understand what's going on behind the scenes. This is the phase where your skills go from theory to real-world application. You're not just playing with AI anymore.

You're building your own systems. That's the difference maker. And once these simple automations are locked in, we'll move on to optimizing them so they can scale, get smarter, and handle even more of your work behind the scenes. All right, so now that you've built a few solid automations and you're feeling more confident with tools like ChatGPT, DeepSeq, and GenSpark, it's time to really level things up.

This is where we move from building simple one-off tasks to connecting everything into a full-on system that actually runs your workflow while you sleep or scroll TikTok or pretending like you're busy. This step is all about connecting tools together to create more powerful multi-step automations. We're going to use platforms that act like the glue between your apps. They move data from one tool to another, trigger things based on conditions, and keep everything humming behind the scene.

First, you want to pick your automation platform. I always recommend starting with Make.com. It's the most powerful. And yeah, it looks a little intense at first, but the visual builder makes it easy to follow. If that feels like too much, Zapier is more beginner friendly, though it has some limits.

And if you just want to automate, like when I post on Instagram, save it to Google Drive. If that, then that is the easiest, but it won't go far. Whichever one you choose, go through their getting started tutorial. Trust me, it's not a waste of time.

It walks you through setting up your first scenario, picking trigger events, and connecting to your accounts. It's like learning to cook one meal before trying to open a restaurant. So once you're set up, start with a few basic integrations.

For example, have your email connect to ChatGPT. So when a specific type of email comes in, it gets automatically processed and summarized. You could also create a data collector, like having new form entries or messages go straight into Google Sheet. or build a notification.

notification system like when someone fills out your contact form you get pinged in slack with the summary and the follow-up reminder now Test each one and I mean really test it don't wait for it to break when you're not looking, try different inputs, make sure the data flows correctly, and watch what happens when the input isn't perfect. Because spoiler alert, real world inputs are almost never perfect. Once you're comfortable, it's time to stack things together into real workflows. Let's say you want to automate your content production pipeline.

You could start with deep seek, pulling insights from articles or forums. Then ChatGPT takes that info and drafts the post. After that, use another AI to clean up the language, maybe add a quote or a stat, and then send it to a scheduling tool like Buffer or Notion to post it later. That's a full pipeline, start to finish, and you didn't touch any single Google Doc or social media app yourself.

Or picture this, you run a small agency and want to handle leads automatically. So you build a workflow like this. A lead comes in through a type form.

Info gets added to a Google Sheet. ChatGPT writes a personalized follow-up, Gmail sends it, your CRM logs it, and you just built a customer interaction system that works while you're at the gym, or let's be honest, while you're pretending to be at the gym. And here's where it gets really cool. You can start combining multiple AI tools in one float to play to their strengths.

So maybe DeepSeq handles the heavy research part like comparing five competitors or analyzing survey responses. Then ChatGPT takes that output and writes a summary or short report. And if you want to share it with a client or team, Genspar can turn that into a web page or a clean PDF, maybe even a slide deck if you're feeling fancy. If something doesn't work, like maybe one tool gives you a weird output or misses the point entirely, this is where you add fallback steps.

You could build in logic that says, if this fails, try again with a simplified version. Or if it returns nothing, send me an alert to review. That's called error handling. And it keeps things from falling apart when AI does AI things. You'll know when you're hitting the milestone when you've got at least one full system running that involves three or more tools.

Not just AI writes an email, but AI reads something, transforms it, passes it to another tool, and something useful happens. You're saving real time there. Your output's more consistent and things just work.

So at this point, you've got real systems running. You've built a few automations, connected your tools, and they're doing actual work for you without babysitting them every five minutes. That's already a huge win. But here's the thing. This isn't the finish line.

It's just the launch pad. Because the real magic happens when you start optimizing and expanding what you've built. This phase is ongoing.

It's where you stop thinking of AI. as a one-off helper and start treating it like a partner that keeps getting better over time. You're not just reacting anymore, you're iterating, improv-ing, and thinking a few steps ahead.

Let's start with refining how you talk to your AI tools. You already know the basics of prompting, but now we're stepping into advanced prompt engineering. This is about taking control of how the AI thinks, getting super specific with your instructions, setting behaviors up front, even stacking instructions in a way that guides the AI through a thought process. You're not just saying, write a paragraph. You're saying, analyze this, reason it out step by step, then summarize it with clear bullet points.

And yep, that's actually a thing called chain of thought prompting. It's like telling the AI, show your work. And it usually delivers better, more accurate responses, especially for anything analytical or multi-layered. Next up, quality control. You want to make sure the stuff your AI is spitting out is actually good.

That means setting up checkpoints in your workflow. Before a response goes to a client, maybe it gets reviewed. Or you tell the AI to self-check its own output.

Review this tone, grammar, and formatting before finalizing. You can also build workflows that compare two outputs and pick the better one. Nerdy? Sure. But super useful when consistency matters.

And now we talk integration. This is where you get strategic. You're not just using tools side by side anymore.

You're combining them to create systems. DeepSeek pulls data, ChatGPT writes the summary, GenSpark builds the output, and your automation platform pushes it wherever it needs to go. You start thinking like a builder, not just a user.

But here's the key. You can't grow if you're not learning. So set up a routine that works for you. Subscribe to a couple good AI newsletters. I personally recommend the ones that don't waste your time with a bunch of fluff.

Just give you the real tools, updates, and use cases. Block off a couple hours a week to try something new. That could be testing out a new feature in make.com or comparing chat GPT outputs between...

Seek and GPT build a learning backlog. It's like a to-do list, but for ideas that you want to try when you've got time. And now that you're actually building useful stuff, start documenting your wins. Make before after snapshots of how much time and effort you're saving.

Record a little screen shares of you walking through what you built. Even if you're just talking through a workflow in Loom, it shows that you're making progress. Write down what the problem was.

what the AI now handles and what changed. That's your portfolio. And if you want to speed things up even more, hey, join my community. Seriously, it's not just a shameless plug.

This is where people are sharing their best automations, giving feedback and figuring out the smart stuff together. It's where I drop tons of templates, breakdowns and updates before they even hit YouTube. You can post your projects there, ask for feedback or just lurk and learn. you'll pick up more in a month from this group than six months trying to do this on your own. And the final move here is staying sharp.

The AI world is changing fast. The tools you're using now are probably going to look different in three months. So schedule a little time every quarter to update your automations, tweak your prompts, and check out what's new.

Keep growing, keep improving, and keep building things that actually work for you. Hey, if you made it this far, congrats. You now have the skills, the tools, and the mindset to not just use AI, but to build with it. And that's the difference between being stuck in tutorial mode and actually running a smarter, faster business.

Let's keep pushing. Oh, and before you click away, here's another video you'll probably want to watch next. It's packed with even more tips to help you crush it with automation.

I'll see you there.

Transcript for:AI Learning Roadmap 2025

Transcript for:
AI Learning Roadmap 2025