AI is becoming more powerful and more deeply woven into everything we do. Some people try to ignore it, but it's not going away. If you're watching this, you already knew that. You're not asking if you should learn AI. You're asking how. Whether you want to work smarter, spark new ideas, automate parts of your business, or just buy back your time, this video will give you the full road map. You'll learn the key concepts, the right tools, and a clear step-by-step action plan. It's simpler than you think, and by the end, you'll be ahead of 99% of people trying to figure this out. But I also know the AI landscape can feel overwhelming. So, before we dive in, let's break down the biggest barriers that keep most people stuck. I'm not technical. That's totally fine. Most modern AI tools are built for non-technical users. If you're even a little tech curious and willing to learn and experiment, which you probably are if you clicked this video, that's all you need. And just to be clear, there will be zero coding involved here. It's changing too fast. Every week there's a new model, a new update, a shiny new benchmark. One day it's ChatGpt in the lead, then it's Cloud, then Gemini. But the truth is, most of that is just noise. If you stuck with one solid model instead of chasing every new release, you'd be way better off. They all catch up to each other within a month anyway. What actually matters is the fundamentals, the core skills, and those don't change. I'll walk through all of them soon. There are too many tools. Yep, there are thousands, but you don't need most of them. In fact, you can do 90% of what you need with just three to five solid tools. The rest are either repetitive or super niche. I'll help you narrow down that list later in this video, too. I can't keep up with all the AI news. Honestly, don't. Unless you're creating AI content like I do, there's no reason to follow every headline or test every new tool. You're better off focusing on the bigger picture, the underlying trends, and stay aware of the updates that actually matter. The easiest way to do that is by subscribing to a couple good newsletters, people whose job it is to sift through everything, test what's worth testing, and summarize the highlights. There are plenty out there, including ones tailored to your industry. We run one at Futureedia. I'm obviously biased, but I think it's the best. That's not the point of this video, though. There's no one-sizefits-all here, but most people fall into one of three paths. Path one is the everyday explorer. You're not trying to build anything complex. You just want to make life easier. Summarize documents, write clearer emails, prep presentations, organize your learning. You're here for more time, less stress. like maybe a teacher using Chat GPT to draft lesson plans and tailor them to different grade levels or a student using Notebook LM to organize notes and prep for exams. Path two is the power user. You want to do more faster. Whether that's content creation, brainstorming, or solving problems. Maybe you're a creator using Perplexity for research, chatbt to write scripts, MidJourney for thumbnails, runway for B-roll, Sunno for music, Dcript for editing, and n to automate your posting workflow. Stacking tools can become extremely powerful. Path three is the builder. You want to go deeper. Automate tasks, build custom tools, or scale parts of your business. Tools like NADN, Manis, and Cursor. They let you connect apps, automate complex tasks, and build powerful systems all without writing code. Maybe you create an agent to handle support tickets or automate your lead genen or build an internal tool that saves your team hours every week. And just to be clear, in this video, I'm focusing on no code builders. Everything I'm talking about here is totally accessible. And the cool part is moving from one path to the next is easier than you think. You might start as an explorer and end up building real tools a few weeks later, hopefully with the help of this video. Let's break down a few core concepts before we jump into the tools. Artificial intelligence is the broad umbrella software designed to simulate human intelligence like learning, reasoning, or problem solving. Within that you have machine learning which is how AI systems actually learn by finding patterns in data and improving over time without being explicitly programmed. Then there's deep learning, a sub field of machine learning that uses neural networks. And these days when most people talk about AI they're usually referring to generative AI tools that can create new content, text, images, videos, music, and more. That's what we'll be focusing on in this video. I just mentioned the others to give a bit of context. And there will be some new terms that show up and I'll explain them in context. Now, let's talk about tools. One of the most important parts of this video, but also the one that can feel the most overwhelming. There are literally thousands of AI tools out there, but I'll break this down into five main categories. LLMs, research, image, video, and audio. Then there's one more category I'll cover that probably 80% of the AI tools you'll come across will fall into. These are specialized wrappers that use a foundation model and build a nice UI and additional features on top. There's more to it that I'll cover in that section, but understanding this makes the entire AI tool landscape feel less overwhelming. You don't need to spend hours researching every tool. Instead, start by identifying the problem you want to solve, the task that's eating up your time or energy, and look for the best tool to help with that. In a huge number of cases, the solution will be a large language model or LLM. The LLM is the most important tool in most people's AI toolkit. There are a ton of options and honestly, it doesn't matter that much which one you use. Maybe you go with Chat GBT because you're used to it, Gemini because you use Google products, or Claude because you like their philosophy, or Grock because you're an Elon fan, or Meta because you're into open source. They all have slightly different strengths and vibes, but the core functionality is very similar, and the underlying concepts, especially prompt engineering, are the same across the board. For this video, I'll be using Chat GPT in most of the examples since it's the most widely used, but everything I show here applies no matter which model you choose. These tools are all powered by what's called a large language model or LLM, a type of neural network trained on massive amounts of text data to understand, generate, and manipulate human language. They're incredibly versatile and powerful. People use them for everything from content creation and research to coding, translation, customer support, and more. This is where most people start and for good reason. Almost everyone can find high impact use cases for an LLM in their work or day-to-day life. Many of these models including Chat GPT, Claude, Gemini, and Grock are also multimodal, meaning they can work with more than just text. They can analyze images, describe visuals, and in some cases process video or audio. Gemini, for example, is currently one of the best at understanding video input. But here are a few terms you'll see around LLMs that are helpful to understand. So, a prompt is the instruction or input you give the model. A token is a small chunk of text, usually just a few characters or part of a word. LLMs process input and output in tokens, not words. Understanding tokens is useful when you're dealing with length limits or pricing, since most models charge by the number of tokens used. Hallucination is when the model makes something up, usually with confidence. This happens frequently, so never assume the answer is 100% accurate. Always double check important outputs. Rag or retrieval augmented generation. This is a setup where the model retrieves real data or documents to ground its answer instead of relying only on its training like searching the internet and using that information. Neural networks are the underlying architecture powering LLMs. They're inspired by how the human brain processes information and are designed to recognize patterns and relationships in data. You don't need to memorize these. They'll make more sense as we keep going and you see them in context. Here are a few simple use cases using ChachiBT. Paste in a URL and get a summary of an article. Upload a rough script and ask it to tighten the writing while keeping your voice. Drop in a massive PDF and get a digestible breakdown. Solve complex math problems. Brainstorm ideas, automate writing, simplify tasks, the list goes on and on. If you have a problem you want to solve, start here. If you want a full deep dive into everything ChatGBT can do, I've made a separate video on that. Another fast way to level up with chatbt is with this free chatbt resource bundle provided by HubSpot. There's a total of five PDFs that go in-depth on how you can utilize chatbt in your career to get ahead, solve problems or save time. My favorite is called supercharge your workday with chatbt. It covers specific examples of how chatt can be used in various industries sales and marketing, project management, enhanced decision-m and problem solving, time management and organization. It walks through step by step with different tips and even has a section titled 100 ways to try chatbt today with 100 sample prompts you can use and modify no matter what career you have. There's sure to be a bunch in there that apply. And that's just one of the resources in the bundle. Use the link in the description to go download that. Thank you to HubSpot for sponsoring this video and providing free resources to the people that watch this channel. This next category is technically built on top of LLMs, but it's so useful and distinct in how it helps you think that it deserves its own category. At the core, these tools combine language models with real-time information and or your personal data sources to help you search, summarize, and synthesize fast. Perplexity is one of the biggest players here. It's an AI powered search engine that uses rag, retrieval augmented generation, to give you answers grounded in real sources. Tools like ChateBT and Claude can search the internet, but Perplexity is built from the ground up to specialize in research and is so good at it, it's worth checking out. Another standout tool is Notebook LM. This might be the most powerful second brain I've used so far. You upload your own materials, notes, PDFs, articles, YouTube videos, and it helps you query, summarize, and connect them in genuinely useful ways. It's like having an AI research assistant that knows your personal knowledge base inside and out. It can find and locate sources directly within any of your documents and show you where it got it from. But whether you're a student, strategist, researcher, or just trying to think more clearly, these types of tools can seriously upgrade how you process and apply information. The image category has exploded, and the quality of what these tools can create is honestly incredible. Now, we're talking hyperrealistic scenes, branded graphics, stylized illustrations, and even clean editable text, all from a single prompt. Most image models today are based on something called diffusion. They start with a field of random noise and gradually remove that noise to reveal a final image that matches your prompt. Different tools have different strengths. The midjourney is still my favorite for realism and aesthetic quality. Chat GPT's image generator is amazing for interactive creation. You can generate an image, ask it to change small details, remove the background, or add new elements, all using natural language. Ideogram is especially strong when it comes to graphic design and text within images like posters, logos, or UI mock-ups. And to be clear, all of these tools can do a bit of everything pretty good. But depending on your goal, one may serve you better than the others, and there are far more than what I listed. Video is one of the fastest moving areas in AI, and new updates are constantly reshaping what's possible. Just recently, V3 from Google dropped a huge update that's gone super viral that you've probably seen. It can generate full scenes with synchronized video, dialogue, sound effects, and like emotions all from a single prompt. We can talk. No more silence. Yes, we can talk. [Music] That used to take a whole production pipeline. Now it happens in minutes, and Hyo 2 has pushed things even further with insane physics. You can create scenes with complex motions that felt impossible just months ago. The list of other amazing video tools is continually growing. There are two main ways to generate AI video. There's text to video. You just write a prompt and it generates the full scene. Then there's image to video. You provide a start frame, an end frame, or both, and the model animates from that. This gives you more control and lets you control the aesthetic while guiding the action through prompting. There are additional tools that let you animate characters using real motion. Runways Act 2 lets you upload a video of yourself or someone else and drive a character or scene with that motion. Mo is really good with restyling footage into any style you can imagine. Topaz can creatively upscale videos, enhancing the quality while reimagining the details. There's a ton of fun stuff to play with here, and it's evolving fast. Many people are using it to go viral on social media, but also to create full music videos or even advertisements for major companies. There are a few main areas in AI audio. Text to speech has come a long way, and 11 Labs is still the leader here. You can generate hyperrealistic voiceovers, clone your own voice, or create custom voices with different accents and tones. Write a script, pick a voice, and generate a polished narration in seconds. These voices can sound very natural and conversational. It's amazing. Music generation is a category that's kind of mind-blowing. There's a few key players here, mostly suno and yo, that let you create fulllength multi-instrument songs with singing just from a text prompt. [Music] champagne and cyanide. Or you can also guide the generations by uploading a reference track. [Music] Then there's voice input like what you can do in chat GPT. You can talk to it in real time and it responds with a natural conversational voice. It's surprisingly fluid, like having a back and forth conversation with a super helpful assistant. Isn't that right? Exactly. It's pretty cool how natural it can feel, right? It's almost like chatting with a friend who just happens to know a ton of stuff. It definitely makes things super convenient, especially when you're on the go or multitasking. And then pushing things even further, tools like Google AI Studio can listen to your voice and watch your screen at the same time, giving you real-time guidance or instructions as you work. I've used this before as an assistant to help me learn new softwares. Yeah. What's next? The background is still there. Okay. Now, go to the effect controls panel at the top left of the screen. There you should see the options for the ultra key effect. Click the eyropper icon next to the key color option and then click on the blue background in the program monitor. There's one additional category I want to cover. Let's call it specialized rappers for now. You'll see thousands of tools online that look brand new, but under the hood, most of them are just custom interfaces built on top of foundational models like chatbt, Claude, or Gemini. They're designed for very specific use cases, things like writing emails, fixing resumes, reviewing PDFs, or generating marketing copy. and they usually add a clean UI, some guard rails, and pre-loaded prompt engineering to make those models easier to use for that one task. And that's not a bad thing. These tools can be genuinely useful. But it's important to understand what you're actually looking at. Just ask yourself, is this a new capability or just a polished wrapper? If it's the latter, you might be able to recreate it yourself inside Chacht with a well-crafted prompt and a few examples. From there, it's a choice. Do you want to pay for the convenience and user experience, or would you rather build it yourself? that might take more time but could be more cost-effective and customizable. That said, some platforms go far beyond basic rappers. They combine multiple tools into full endto-end workflows. For example, a marketing platform that writes ad copy, generates branded visuals and videos, runs Facebook ad campaigns, and then AB tests the results all automatically. And those can be game changers for the right use case. And could you recreate something like that with LLMs, automations, and custom agents? Absolutely. And I'll show you how later when we get to those sections. That's where we're changing paths from the power user to the builder. It involves a lot more setup, testing, and trial and error. For many people, paying an extra $20 or $50 a month is worth avoiding that hassle. My goal here isn't to tell you which path to take, just to help you clearly see what these tools are, why they exist, and how to decide what's worth your time and money. Those are the main categories. And to cut the learning curve on some of these tools, we do have an entire learning platform on Futuredia. There's over 20 full deep dive courses into all aspects of AI, including courses on most of the leading tools like chatbt, notebook, LM, midjourney, and others. Then many of the skills and other aspects I'll cover like prompt engineering or building a chatbot for your site. There is a whole library if you want to take the next step there. Of course, there's other resources across the internet, but we have tried to make this the most userfriendly and comprehensive platform for learning AI. But moving on, let's zoom out for a second. The tools will change. The features will evolve, but these four core skills will stay useful no matter what. Prompting is the most essential skill. Learning how to clearly communicate with AI will get you better, more useful responses. You don't need advanced prompt engineering for most tasks, but a few simple best practices can dramatically improve your results. Just start by being specific. If you use vague prompt, catch PT has to guess what you really want and fill in all the gaps. One of the easiest ways to improve those prompts is to follow a simple structure. Aim, context, rules. Aim is what do you want the AI to do? Write a product description. Explain this concept. Brainstorm five ideas. Number two is context. This is critical. Give the model relevant background and information. Who is this for? What's it about? Like for a Gen Z audience based on this resume from these bullet points. Or a powerful form of context is examples, especially in writing. If you want a specific tone or format, include a sample. Then number three is rules. Add any limits, formatting, or style preferences. Use bullet points. Keep it under a 100 words. Use simple language. Respond with JSON. Make it sound like a friendly expert. Include a table or flowchart. Let's do a quick example. So, here's a vague prompt. Write a blog post about productivity. After I send that, you can already see what it had to guess. Who was the audience? What kind of tone do you want? How long should it be? What kind of productivity are we talking about? That's a vague term. Now, compare it to this. I'm a business productivity coach. Write a 500word blog post for busy entrepreneurs about how to plan a productive Monday. Make it casual and include three actionable tips. End with a motivational quote. This is much more useful. It doesn't matter if you follow the aim context rule structure in the exact order. What matters is that you cover those elements. Like in this example, aim is write a 500word blog post. Context is I am a business productivity coach for busy entrepreneurs about planning Mondays. Rules was 500 words, casual tone, three tips, end with a quote. And in the case of a blog post, you'll typically have previous blog posts that you can upload to ask for it to write in your style. You can just add to the end, here's an example blog post. Write in this style. Easy. Roll prompting is another powerful technique. It's like a shortcut that instantly shifts the tone, perspective, and depth of the response just by telling the model who it is. Here's a quick example. You are a travel vlogger. Describe the experience of visiting Tokyo for the first time versus you are a business travel consultant. Describe the experience of visiting Tokyo for the first time. This is a simplified example, but notice how much of the context and tone is shaped just by assigning a role. even before adding the additional details you normally would. The first response will tend to focus on food, culture, street scenes, and sensory details. The second will highlight airport efficiency, transportation, meeting spaces, and business etiquette. It's the same city, same question, completely different output. Now, over time, you'll start thinking this way naturally. You won't always follow a strict order like aim, context, rules. It will all be included, but mixed in together naturally. The key is just to think clearly about what you want, who it's for, and how it should sound. That mindset will help no matter what you're trying to create. The more you practice, the more powerful it becomes. For a deeper dive, I'd recommend this resource that has a bunch of additional tips and techniques you can use. You don't need to know every AI tool, just the landscape. Understand the main categories and what's possible. That way, when you run into a problem, you'll recognize that it's solvable, and you'll know where to start looking. Workflow thinking is the ability to break big tasks into smaller steps that AI can help with. If you try to throw a huge multi-step request at an LLM all at once, it usually falls apart. But if you break it up into clear steps and use the right tools for each one, you'll get way better results. Sometimes it might seem like a task can't be done with AI, but maybe 80% of it can. That's still a massive timesaver. Creative remixing is the skill of combining tools in unexpected ways. Not always to follow a plan, but to explore what's possible. Sometimes you start with a clear goal. Other times you try something, get an interesting result, and decide to follow that direction instead. This happens a lot with AI, especially the creative tools. The results aren't always predictable, but sometimes leaning into what the AI is good at produces better results than sticking rigidly to your original plan. Now, it's time to level up. Once you understand how individual tools work and start linking them together, you can begin automating tasks. That means building workflows that complete steps for you without manual input. Platforms like Zapier and Make have been around for years to do this, but Naden has become especially popular lately. Part of that verality is because it lets users sell workflow templates, and that has led to some grifting. You know, make $1,000 a day on autopilot if you buy my $50 template, that kind of thing. So, if you're watching YouTube videos about it, just know what to look out for. That said, the platform itself is incredibly powerful. And one big reason for its rise is the introduction of the AI agent node. That's one of the most intuitive ways to build agents. So, it's a great entry point into one of the most hyped and genuinely useful concepts in AI. And there's an important distinction here between automations and agents. Automations are fixed. They follow a step-by-step sequence A to B to C. Even if they get complex with branching logic, they still follow a predetermined path. Agents are dynamic. They can reason, make decisions, and choose which actions to take based on context. To function, an agent needs three things. A brain, usually a large language model, memory to retain context or past interactions, and tools, actions it can take, like sending messages, updating documents, triggering workflows, or calling APIs. A great way to practice is by slowly building an AI personal assistant. You start simple and add tools and functionality as you go. So, maybe you start just with an agent that reads your calendar and gives you a quick summary of your day, prioritizing what matters most. Then you add the ability to reschedule events or time block. And after all that, maybe it starts reading and summarizing your emails and eventually even sending replies on your behalf. Then you could give it access to your SOPs or notion docs for added context and connect everything through a simple chat interface. And that could just be in Telegram or WhatsApp. Over time, you'll be able to just send a quick message like something came up, rearrange my schedule for tomorrow, and it will be able to execute that. Or it could be summarize anything urgent for me today or write me hooks for a video on AI agents inspired by my hook database in notion or summarize the comments on my latest YouTube video. You can build in all sorts of things that apply to you. And I recommend starting with something like this because you'll catch every error and it's a safe way to experiment, debug, and iterate before building agents that run inside your business. I do have a full video on how to build this kind of workflow if you want to go deeper. It is probably the most straightforward agent guide out there. And I'll mention you may already be using agents. Chat GPT's deep research mode or the similar feature in perplexity in Gemini. It's a simple but powerful agent. So you give it a research task and then it plans the best way to approach it. It searches multiple sources all over the internet, identifies gaps, pivots its strategy, and then compiles everything into a clean report. It is incredibly useful. But learning how to build your own agents that give you that same kind of reasoning and execution power tailored to whatever task you choose is the next level. Vibe coding is a new approach to building software and tools that's emerged from some of the later AI updates. But here's the basic idea of how vibe coding usually works. You describe what you want in plain language using voice or text. The AI generates the code or a basic app structure. You test it, see what works and what doesn't. You describe your changes. Then the AI updates the app. And you just repeat that until it's working the way you want. You're just going with the flow of what the AI gives you, vibing, until you get something functional with no coding required. Now, this isn't at the point where you'll get full-scale productionready software through vibe coding, unless you're Jack Dorsey, I guess, but you can get a proof of concept prototype or an MVP you can test. I mean, there are cases of people fully vibe coding apps and publishing them to the app store. But an amazing way a lot of people are using this right now is building personal use tools or internal apps that streamline their own productivity. Like for example, you might build a lightweight CRM just for your sales workflow or a content creation app with your voice and hook templates and storytelling formats built in. A few tools that support this kind of workflow. Windsurf lets you build simple, usable apps with a polished interface. No code required. It's best for MVPs or internal tools. Lovable is designed for solo creators and small teams. It helps you design and build AI powered products quickly with a focus on user experience. Replet lets you build and test full apps with a clean UI all in your browser. It's good for rapid prototyping, especially with some light technical knowledge. Cursor is the most powerful. It's a desktop coding environment powered by AI. This is ideal if you already know a bit of code and want hands-on control. You can use it if you don't know how to code, but it will look more intimidating when you first start. But why this all matters is it makes software creation more accessible than it's ever been. If you're building for yourself or just testing an idea, it's often faster and more enjoyable than traditional coding. And as the tools get better, more people will be able to replace subscription-based SAS tools with personalized versions just by prompting for them. Now, I don't have a deep dive video on this yet. I haven't gotten to the level of expertise I'd want before making one, but if you want to go further, there are already a lot of good resources out there to explore. To make this actionable, I've broken it down into a simple plan. First, identify the biggest pain points in your life, work, or business, like what causes the most stress or procrastination, and what takes the most time. Next, write out what a potential solution could look like, even if it feels rough or incomplete. Then, research tools could help solve it, and ask CHACHT to help. In many cases, it will be a large language model like ChachPT, but based on the categories I covered earlier, you should have a pretty good idea where to look if it's not. From there, iterate. You may need to break it up into subtasks or use a bit of the prompt engineering we covered. You don't need to get it perfect right away, but just make adjustments, iterate until you can solve that task. Just dedicate whatever time you can to this. You don't have to go all in. Even 15 minutes a couple times a week can lead to serious time savings later on. Now, in parallel with this, just try exploring new tools. If you're already using chatbt, try doing something new inside of it, like creating a project, generating an image, making a mind map, or analyzing a document or data set. It has way more built-in capabilities than most people realize. I've got videos that cover all of them. I'd also recommend experimenting with tools like Perplexity and Notebook LM. They're both incredibly useful and their free versions give you a lot to work with. And once you've explored individual tools, start combining them. Just build a simple workflow that connects two or more. Then take the next step and automate something. Pick a basic repetitive task and set up a simple workflow that does it automatically. Once you get over the hurdle of building your first automation, you'll start seeing opportunities everywhere. So to sum all that up, start with a painoint, find the right tool, iterate, combine, then automate. That's the full road map. Don't just use AI because it's cool. Use it to actually solve problems. Start with one friction point in your life or work and see how far you can get with the tools and concepts I covered today. Most of this will come a lot easier than you first expect, and you don't need to keep up with every new release. The tools will keep changing. The core skills and principles won't. Even if you only apply a small part of what we covered here, you're already ahead of 99% of people. And if you do want to go deeper, we've built a full course platform at Futuredia. It has over 500 lessons across over 20 AI courses. You'll find full learning paths on chat GPT, prompt engineering, automation, custom GPTs, video generation, coding with AI, and more. All included in one subscription. So whether you're just getting started, you're building internal systems, or applying AI in your business, there's probably a course that fits exactly where you're at. You can get a 7-day free trial using the link in the description. Or if courses aren't your thing, the newsletter will keep you in the loop with the most important updates. But bottom line, you don't need to master everything today, but the next step is to just keep going. If you're ready for that, this video is the one I'd recommend watching next.