AI Tools EXPLAINED: How to Use Them? (2026 Guide for Beginners).

AI Tools EXPLAINED

Most people using ChatGPT and similar tools don’t even understand what it really is and how it works. Understanding the basics of AI can make you much better at using tools like GPT image generators and even more advanced systems. In this video, I’m breaking it all down: what AI is, the types you can actually use right now, and how they work. Stick with me, and by the end, you will feel like an AI master yourself. Problem. AI is a total buzzword right now. Anything that seems remotely smart gets labeled as AI, whether it’s a chatbot, autocorrect, or even your fancy fridge. It knows when you’re out of milk. People think AI is some all-knowing super genius, but the truth is that most AI systems today are just really good.

good at doing one specific thing. That’s it. They are toes, not brains, but toes. Think of AI as a big umbrella. Underneath it, you’ve got things like large… language models, image generators, robots, you name it. At its core, AI is just a system designed to mimic human-like intelligence. It solves problems, recognizes patterns, makes predictions, stuff that looks like thinking. But it’s not thinking as we do. It has no feelings, no conscience. No aha moments. It’s following a plan, step by step, predicting what should come next. It’s impressive, but it’s not magic. After this video, you will be asking about links to all the tools mentioned. I got you. We have a website where we post reviews of all the AI tools we test ourselves. So check that out by hitting the link in the description.

We, as consumers, now have access to a few types of AI tools we can already use. Large language models, image generators, audio generators, video generators, voice assistants, and productivity AIs. These are the core ones that most of us can use right now. These tools might seem wildly different, but they all work on the same basic principles. And to set the record straight, what we call AI isn’t some thinking machine. It’s actually neural networks. Let me explain. Explain. At their core, neural networks are systems that learn patterns from data and use them to make predictions and generate results. Imagine them as a bunch of layers, filters. Each layer processes the data, passes it to the next layer, which refines it even more, and so on.

By the end, you get the final output. Networks don’t start smart. They have to be trained. That’s where developers come in. They feed the network massive amounts of data, like text, photos, or videos. and the network starts guessing outputs. Every time it gets something wrong, which at first is a lot, it adjusts its internal settings to get a little closer to the right answer. This process happens millions, sometimes billions, of times until the network becomes really good at recognizing patterns and generating results. At that point, it’s ready to take your prompts and turn them into something useful. In my YouTube agency, we use AI for almost everything, but it’s not like it does everything for us. It enhances our workflow, making us more productive.

And by the way, a video about it is already on the channel. Now, let’s go over each type of neural network you can use right now. Large language models. ChatGPT, Gemini, Claude, Mistral, Grog. A new one pops up every week. Here’s the thing. They all work basically the same. In the same way, just at different scales. How do they work? Transformers. Transformers take your input, like a question, and figure out the best output, the answer, using probabilities. Let’s say you ask, ‘What shape is the wheel?’ The model breaks it down into keywords like ‘shape’ and ‘wheel,’ then calculates how those words relate. It looks at the data and thinks, ‘All right, the next word with the highest probability here is circle.’ Boom, it gives you the answer.

Why does it get that right? main reasons. First, massive training data. These models have read so much text, and somewhere in there, they have seen plenty of mentions about wheels being circular. Second, attention. This attention helps the model focus on the important parts of the input, such as shape and wheel, rather than random filler. That same process works for any ‘anything,’ these models do. Writing essays, coding, analyzing data, you name it. But they don’t understand as we do. For them, there are no actual words. It’s all just numbers, probabilities, and math. When it comes to prompting LLMs, you might think it’s all about creating the perfect structure and nailing the right attributes. And yeah, that’s true, but… is a twist. Every model interprets prompts a little differently.

Bigger models are way more forgiving. ChatGPT is the best out there, and you can talk to it in natural language. Same with Gemini. But smaller models like Mistral or Claude might need you to step up your prom game and be more structured. That said, the core roles of prompting are universal. First, be descriptive. Models love big, detailed prompts that lay out all the context and requirements. Don’t skimp on explaining what you need. Tell the model what the output should look like, how long it should be, who will read it, and the style or tone to use. Everything. The more you explain, the better the results. You don’t want the model guessing what you want. Spell it out. Be clear about the audience.

The format, the language, and the main ideas. Second, use role play. It sounds simple, but it’s crazy effective. Telling the model to act like an expert. In a certain field, it can dramatically improve response time by narrowing the data the model pulls from, making the output more accurate, relevant, and polished. Set limits and make sure the model knows what not to include. This is another small tweak that can have a big impact. And yeah, you can combine all these instructions into a single prompt if you’re using the free version of the model. But if youare on a subscription, you’ve got room to take it step by step. If you want a deep dive into prompting and want to become an expert at it, hit that subscribe button.

A full guide is on the way, and trust me, you won’t want to miss it. The second big player in the AI world is image generators. These models operate totally differently from LLMs. Sure, they’re also trained on massive datasets, but instead of focusing on words, they work with visual elements. The model gets fed millions of images, each paired with a description. Over time, it starts to understand patterns, such as how certain groups of pixels represent a cat or a tree. Of course, that’s a simplified version, but you get the idea. Once trained, the model knows what each word in a prompt translates to in terms of pixel relationships. So, when you type something like ‘generate an image of a fluffy black cat with glowing green eyes it doesn’t just pull an image from a database; instead, it uses the relationships it’s learned to create an entirely new image.

But image generators don’t start from scratch. Every time they generate something, they begin with a blank canvas, basically static noise. Then, through a process called diffusion, they refine the noise into a detailed image. That’s why these systems are often called diffusion models. The base image is a chaotic mix of black and white pixels, and if you sum up their values, you get zero. This technical quirk is why AI-generated images often feel a little off. They lack natural contrast or highlights that stand out. If an image was AI-generated, check the ‘generated by AI’ and lighting. It’s a dead giveaway. Now, picking the right image generator depends on what you’re looking for. Dolly is super easy to use and great for beginners, but unlocking its full potential usually requires.

Gemini can do images, but it’s not the most creative or customizable. Adobe Express is user-friendly with lots of tweakable controls, but it can sometimes… churn out odd results. MetJourney is the gold standard. The web version is solid, but using Discord unlocks more features, though it requires a specific prompt format. Runway is decent for images but shines in video generation. We mostly use Smith Journey in my YouTube agency. If you’re thinking about integrating AI into your workflow, here’s my advice. Pick one tool for each task and stick with it. Consistency isn’t just key for AI, it’s key for YouTube in general. Too many creators start strong, don’t see the results they hoped for, and end up abandoning the channel with real potential. I almost fell into that trap when I was starting.

So what changed? I built a team trying to do everything on my own. Became exhausting, and it started killing the fun. Bringing people on board completely turns things around. That initial team is now the backbone of my YouTube agency, and I’ve never been happier. And here’s the thing, you don’t have to do it alone either. That’s where we come in. My team can help you with every step of the process. We’ll handle all the research and come up with fresh trending ideas your audience will love. We’ll create personalized, optimized content plans tailored to your goals. Our designers will craft thumbnails that demand attention, and our writers will whip up titles that get clicks. Need help with scripts? We’ve got experienced script writers who can nail it for you.

How do you set up your studio? Our director can hop on a call to guide you in creating a professional-level setup on a budget. We can even edit your videos and handle publishing; all SEO, titles, descriptions, and tags are covered. All you’ll need to do is sit in front of the camera and talk. Sounds good? If you’re ready to take your channel to the next level, check out the link in the description, fill out a quick questionnaire about your channel, and we’ll get in touch. Let’s conquer YouTube together. Prompting for image generators might feel similar to prompting LMS at first glance, but the focus shifts a bit. Instead of describing things like audience or tone, you’re focusing on visuals, colors, elements, composition, textures, and more.

Think of your prompt as a never-ending description of every detail you want in the image. Here’s a great way to practice. Take any image you like and start describing it. Write down everything you see, what colors are dominant, how objects are arranged, the lighting, the mood— even tiny details like shadows or textures. When you squeeze every detail out of the image, boom! You’ve got yourself a target. Use that as a template to write your own. Why does this matter? Because image generators can easily get wild with guesses. If you’re vague, the results might miss the mark entirely. To avoid this, add negative examples to your prompt as well. For example, if you don’t want blurry edges, muted colors, or unnecessary objects, say so.

Some generators, like Dolly, let you include this directly in the chat, and some models have a separate input field for negative prompts. There are two types of audio generators: text-to-speech and music. While they serve different purposes, they work in the same way. Both are trained… Massive data sets, either music tracks or voice recordings, paired with transcriptions, are all about probabilities. The models calculate sound waves for each fraction of a second based on patterns they’ve learned. Music generators like Suno, MooBear, and Refusion focus on understanding elements such as melody, rhythm, harmony, and instrumentation when given a prompt. They mix and match these components based on the relationships they’ve learned during training. Whether you want a calm piano piece or an energetic electronic track, the model builds the composition step by step.

Text-to-speech models, on the other hand, take your input text and generate speech. Tools like Levin Labs or SpeechEasy analyze each letter, syllable, and word to calculate how they should sound together. They then use this data to synthesize natural-sounding voiceovers, complete with tone, pace, and emphasis. While they’re working on different types of audio, the core idea is the same. Learn the patterns, then use probabilities to create something new and unique. Prompting for audio generators is a simple process, mainly because there is often little prompting required for music generators; many tools don’t even have a typical prompt-in-a-box. Instead, you’re usually adjusting parameters like BPM, style, and mood. However, some tools, like Suno, do let you write a text description of the song you want.

You can even have Suno generate lyrics combining its music generator with LLMs and text-to-speech tech. If you’re using Suno, keep it simple and to the point. Describe the music style, the mood, how it should feel, and the BPM—no need for specific phrasing; focus on clarity. As for text-to-speech tools like 11 Labs, there’s really no prompting involved. You paste in the text you want turned into speech, pick a voice, and tweak the properties to suit your needs. Once it’s faster, slower, or more energetic, adjust accordingly. Some tools even let you clone your voice, which is a cool extra, but again, no actual prompts are required. Video generators work a lot like image generators, with one key difference. Instead of creating a single image, they generate a series of frames that flow together to form a video.

These models are trained on massive datasets of videos paired with descriptions. From this, they learn patterns and how frames change, the spatial relationship within each frame, and the temporal dynamics, basically how objects move or transform over time. When you give them a prompt, they interpret it mathematically and generate frames one by one, starting with a base image for each, similar to how image generators work. There are two types of video generation tools. Those that create entirely new videos and those that edit existing footage. For creating new content, tools like Sora, Hyper, Runway, and Pickup fall into this category. These tools generate frames from scratch following the patterns they’ve learned during training. For editing tools like InVideo, Vizsla, and Flicky, take a different approach. They first process your prompt using LM to create a storyline.

That storyline is then broken down into scenes, with keywords generated for each one. The tool uses these keywords to search its built-in footage library, selects relevant clips and music, generates a voiceover using text-to-speech, and stitches everything together into a final video. Prompting for video generators is very similar to prompting for image generators, but with an edit layer and motion. You still need to be super descriptive, but now you have to include details about how things move. Does the camera pan, zoom, or stay still? Are the objects in the scene moving? If so, how? Are they interacting with each other? Give as much detail as you can, but keep it simple and vivid. And don’t overcomplicate your prompts— video generators can sometimes forget parts of the description or mix things up in unexpected ways.

Focus on describing the essentials: what you want to see and how you want it to move. From my experience, sticking to the basics while being clear and vivid works best. For video editors, there is usually no real ‘prompt’ thing involved. Most of the time, you’ll provide a general description of the video or its plot. The AI takes it from there, either handling everything automatically or offering a few options for you to choose from. These tools are super intuitive. If you want to get the hang of them quickly, we’ve got some great videos on the topic—so definitely check them out. Voice assistants are probably the easiest type of AI to explain. Google Assistant, Siri, Alexa, these are the names everyone knows. Unlike other AI systems, they’re not…

So much of creating content is about understanding and acting on data. They’re not that smart on their own. Most of the heavy lifting comes from transcribing voice requests and determining the best course of action. They all work in three stages. Speech-to-text, intent recognition and processing, text-to-text. Speech, these steps use the same tech principles as the audio generators we talked about, but things are starting to shift. Companies are now adding proper neural networks to voice assistants. For example, the new Siri, not out yet, is expected to offer real context understanding, including personal information, and the ability to take actions directly in apps. Luckily, natural language is the main focus of these systems, so prompting is practically non-existent. Basically, you just…

Verbalize your request however you want, and the assistant will figure out the rest: no prompting structures, no secret tips. You talk normally and hope for the best. One of the uses of AI you can use right now is productivity-based. These smart tools are popping up in all sorts of apps, helping you write, organize, and get stuff done. done more efficiently. Take email clients like Superhuman, for example. They use AI to help you zip through your inbox faster, organizing emails so you can focus on what matters. Plus, they’ve got built-in write-in tools that can rewrite, paraphrase, or adjust the length of your messages. Then there are platforms like Testcade, which is free. Line management workflows and processes simplify collaboration and overall keep you on top of your schedule.

These tools can generate project outlines assigned to tasks and track progress, which is useful for remote teams. And let’s not forget AI-powered CRM tools like HubSpot or Pipedrive, which turn boring CRM systems on their head. Use an AI to optimize your workflow. On top of this, tools like Zapier or Integromat help connect to different apps and automate tasks, making your work life smoother. So, whether you are drowning in emails, juggling tasks, or managing customer relationships, there is probably an AI tool out there ready to give you a hand. It’s all about working smarter, not harder. And by the way, we’ve already reviewed some of these tools and put the reviews on the website master. me. So check them out.

Here’s the downside, though: there’s almost no prompting involved with these tools, unlike AI systems, where you can put in a detailed request and get a tailored response. These productivity tools are more well-locked in. They’re mostly standalone setups, and you’re stuck with the options. They give you. You press a few buttons, choose what you want, and boom, that’s it—no room for much creativity or flexibility. There are tools for almost everything. Presentation generators, legal document analyzers, recruitment screening tools, code and assistance, financial planners, supply chain optimizers, and scientific research aid. Name it. But no matter the AI tool you’re working with, the golden rule stays the same. Be detailed, be descriptive, and straight to the point. Clear inputs lead to better outcomes. 

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