Generative AI Explained (2025): 20-Minute Guide of Every AI (from ChatGPT to Gemini)

I've been working with AI for over three years and I've done it all. I've tested every tool there is, every prompt technique, every feature. I had to learn everything myself, but I want to make it easy for you. So, in this video, I'll share the most important things I learned about AI that will help you get a solid start and great results right off the bat. I will tell you about AI we have right now, chat bots, image, and video generators, teach you how to prompt effectively, and what to avoid. This is the only video you need to start.

start. AI is everywhere, yet each tool does one narrow job. Underneath that web facade is a spotter called a neural network. It chews through millions of examples to learn the patterns and keeps guessing and tweaking itself until it learns to reliably repeat what it saw. Later, given a prompt, it runs the hall pipeline again to give you the result you're looking for. Big chat bots. Chad GBT, Gemini, Claude, Mistral, Grock are all built on transformer engines. They basically first turn words into numbers, mix them up, and then when needed, pick the order of words with the highest odds. The more parameters the model has, and the more training it does, the better the results. The recipe always stays the same. Read, spot patterns, guess. Image generators work similarly, but trade words for pixels. After studying millions of picture caption pairs, they learn what pixel combos fit golden retriever on the beach description. So they start with noise and slowly add pixels until the image looks similar to the ones it knows. Video tools stretch that trick through time. Sora runway pa hyper build clips frame by frame focusing on smooth motion. in video and visas skip synthesis. An LLM reads the text, grabs stock shots, adds an AI voice, and spits out a cut. The headache with these tools is coherence. Stock shots or newly generated ones might not look alike, making the video look bad. As for audio AIs, they basically come in two flavors. Text to speech tools like 11 Labs and Music Bots. The first type of tools slice scripts into phonemes, map them to waves, and blend them so the voice flows. and music bots like Sono and Refusion pick notes and rhythm until the track is full, copying similar tunes that fit the description. Both live on probability maps of sound. Then there's voice assistants like Siri and Alexa. These ones chain speech to text, a small intent engine, and text to speech. That middle layer is now tiny LMS that keep context and can poke buttons in your apps. Though their main trick is still to listen, fetch, and read back. All those tools is what we're already using, but even day-to-day apps get upgrades. Mail clients like Superhumans sort mail and summarize it. Task managers like Tascade drafts to-dos and keeps you on your toes about them. There's many more examples. Even your PDF viewer now has an option to chat with your PDFs. All these small apps and tools seem tame yet save hours.

So to effectively control these models, you need prompts. And the process of writing these prompts is called prompt engineering. And to prompt effectively, you must understand how AI understands your commands. Every model, whether it writes a paragraph or creates a scene, starts by swapping each word you type for a number. Then it looks at those numbers, searching for patterns that match the billions of similar words or pixels it saw back in training. You want these commands to be as clear as possible. If your prompt is padded with polite fluff, the pattern looks blurry. The model fills the gaps with its own ideas and the answer drifts off target. If your prompt is tight and crammed with context, the pattern looks sharp for AI and the answer comes out exactly the way you imagined. That's why prompt engineering saves hours of manual editing. With prompt engineering and AI, you can do anything, even create websites from scratch. But don't use chat GBT for that. It's not good at designing working websites. Instead, I've been playing around with Durable, sponsor of today's video. I used to think building a website was something that took days, maybe even weeks. Coding, design decisions, SEO, hosting, it all felt like too much. With Durable, all I need is a description, location, and a name. That's it. Within seconds, Durable had generated an entire site, a header, sections, images, even pre-written service descriptions that actually made sense for what I do. It looked professional right out of the gate. I've built sites before, but never this fast. And what really surprises me isn't just the speed, it's how flexible everything is. I can swap images, rewrite content, adjust the layout without touching a single line of code. The AI did the heavy lifting, but I still feel like the site reflects the idea perfectly. This is honestly one of the most stress-free ways I've ever launched something. Whether you're starting a side hustle, building a portfolio, or just need a digital home for your ideas, Durable makes that first step ridiculously easy. You will be shocked how quickly you can go from maybe I should make a website to having one live and ready. I will leave a link in the description. Be sure to check it out. The first thing you have to do to your prompt is to shave off filler. The model doesn't need please, maybe, or if it's not too much trouble. Each extra word eats up room and adds another place things can go wrong. A straight command like summarize this article in two paragraphs is cleaner and leaves less to chance. After you trim, pile in the detail. Tell the model the topic, the angle, the audience, the tone, the length, even the year if you care about style. Remember, the model knows statistics, not intention. The more you reveal, the less it invents. If the reply still feels off, you can ask the model to rewrite your own prompt so it's clearer. It will coach you in real time and show you new tricks. In the end, prompt engineering isn't coding. It's purposeful writing. Every word you add or drop tilts the model's internal map of probabilities. Once you learn how to control that map, AI stops feeling random and starts acting like a tool you can count on.

I keep every prompt tidy by imagining five little boxes. I fell from left to right. First boxes said the voice. Maybe I tell the AI you're a travel writer. Second box says the job like write a city guide. Third box gives the scene. The reader is off to Paris for the first time and only has two days. Fourth box frozen limits. Nothing pricier than €40. Cap the hall piece at 600 words. No slang. Fifth box nails the shape of the reply. Two paragraphs for each neighborhood. Plain text. With that simple row of boxes, the model now knows what to say, how to say it, and when to stop. That fivebox formula never changes, but every kind of AI adds on extra layers. An LM chatbot lets you tweak settings like temperature when you need either wild ideas or tight precision. An image generator wants visual clues into the scene box, subject, lighting, lens style, colors, even stuff you don't want. A video generator thrives on motion descriptions and shot length in the limits box. A music model adds details about tempo, key, and instruments. The formula stays the same. You switch stuff around and add new info to match the task.

Large language models are basically giant word machines. They treat words the way a calculator treats numbers. When you type a prompt, the model chops the sentence into tiny bits called tokens. Each token turns into a row of numbers. Those numbers flow through many attention layers that measure how strongly one bit connects to the next. The network then guesses the most likely next token, tacks it on, and keeps looping until the answer is done. Because the model was trained on mountains of novels, manuals, forum rants, and code, it has already spotted almost every word pattern you can imagine. Today, these models don't stop at text. Almost every LM now can see pictures, describe what they see, and even use these images as part of their reasoning. That's why these LMs are called multimodal. And you really need to know how to effectively work with all the file types. But all that info won't fit into a single video. So instead, we've created and launched 101 crash course into Generative AI. We'll walk you through everything from crafting your first prompt to mastering the kinds of advanced techniques that completely shift your results. And since new lessons drop every week, you can learn at your own rhythm. We've taken all the insights from our team and packed them into bite-siz lessons full of visuals and clear breakdowns. And right now, if you're watching this in AI Master, you can grab a 63% discount on your first one-year subscription. Just click the link below, join us, and start turning your AI curiosity into real practical skills. But speaking of LMS, their memory, called the context window, keeps stretching from a few thousand tokens to millions, so they can halt an entire book in mind at once. The window is still finite though, so in a very long chat, the earliest messages eventually slide out of view. Each reply is always a fresh guess, not a copy paste from storage. That's why the same prompt can come out a bit different each time. These models can feel magical, but they still make things up, site sources that don't exist, stumble on simple math, and pass along the biases hidden in their training data. Their strength is speed, not perfect truth.

Prompting a language model is a bit like drawing up a mini contract. You promise clear instructions and the model promises to stay on track. I still lean on the same five invisible boxes and suggest you do the same. Roll, task, context, constraints, format. Then sprinkle in a few extra cues for style. Label the RO first so the AI slips into the right voice. State the task with a simple action verb. Draft, compare, translate, so there's zero doubt about the job. Next comes context. Who will read the piece? Which facts matter? Even which spelling you want, etc. And remember about adding constraints. They draw hard lines. Maybe you want a reply no more than 600 words, no buzzwords, Chicago citations only, and nothing older than 2020. Finish the prompt off by describing the shape of the reply, plain paragraph, two column table, valid JSON, etc. The model needs to see that shape in words. On top of the formula, you can add examples. They act as shortcuts. show a couple of examples and the model will mimic the pattern faster than any written explanation. For long or tricky work, I ask it to think out loud first and then give the answer. That trail of reasoning makes it easy to spot any leaps before I trust the result. I never expect a perfect first try. Fire the prompt, skim the draft, point out the gaps, and push only those gaps in the next turn. That back and forth slowly snaps everything into place. And I always watch the context window. Long text inputs or huge files can get trimmed or numbered so the model can refer to them without chewing up space. So even with a huge token window Chad GBT has now overload it with details. Again, be direct, be explicit, show an example when you are unsure and let the dialogue sharpen the result.

Image generators work similarly to text generators, but instead of doing math with words, they do it with pixels. During training, they study millions of photos and build a giant cheat sheet that links scraps of text to shapes, colors, and textures. When you type a prompt, the model drops a layer of static noise onto the canvas, then uses diffusion to scrub away any pixel that doesn't fit the description until a clear image pops out. I'm oversimplifying, but still the principle is the same everywhere, but each generator feels different. Chad GBT's generator works best with human language. Simple prompts without too many parameters. Mjourney wants short punchy commands and prompts actually change depending on the version. Whether it's a web version or Discord one, each tool is unique, but the body of a prompt stays roughly the same for all of them. With some generators, you can go deeper into parameters. And here you have only two to pay attention to, a guidance weight and seed. A guidance weight, often called prompt strength, tells the model how tightly to hug the caption. Low values give loose, dreamy images. High values clamp down on every detail. Each generation also has a random seed which you can save and reuse for new images. Reusing that seed with the same prompt recreates the picture almost pixel for pixel. Handy when you want to tweak one detail without losing everything else. And remember, most models spit out 512 or 1024 pixel squares because they were trained that way. Asking for 4K or 3x4 portrait won't magically boost the raw pixels, but we'll pack more detail into the frame so you can upscale later.

Think of image prompting as painting with sentences. Here, we're still going to lean on the same formula. Raw, task, context, constraints, format, but on top of it, we'll drop a visual trio. Subject, description, style. First, say what should show up, then what it's doing and where it sits, and finally the look. Oil painting, cyberpunk photo. A full line like a sleek black cat perched in a rain slick street under neon purple lights gives the AI a much sharper target than just a cat. Cyberpunk night. When generating images, don't forget about that initial formula because a simple thing like context actually shapes the shot. If it's a YouTube thumbnail, say so and the model will push bold colors and a centered layout. If it's headed for print, add 300 dpi magazine cover so it packs an extra texture. Then lay down hard limits, the aspect ratio, how much empty space or the brand's color palette. Negative cues are your safety net. Tell it no text, no watermark, no people. The magic happens in the loop. Generate the first image. Check what feels off. Maybe the hands look weird or the sky is too flat. Ask to change those things. Each cycle takes seconds so you can iterate dozens of times. After a while, the whole generation process feels like a silent handshake. You hand over precise words. It hands back precise pixels and the space between idea and finished picture almost vanishes.

The same thing and same rules go for video generators. These models try to do for moving pictures what image models do for stills, but they have a tougher job because every frame has to flow into the next. The AI must learn not only what a red balloon looks like, but how its shape drifts as it floats across the sky. The newest models like Sora, Veo 3, and Cling can keep a character on model, hold the lighting steady, and dodge the flicker. There are many video generators out there and picking one isn't easy. And do you really want to read dozens of articles to find the one you like? Instead, try our tool finding agent. As a member, you'll get access to exclusive AI agents we've designed to make your learning easier and smarter. Need help crafting the perfect prompt? There is an agent for that. Want fresh AI news? Our agent scans headlines from across the web and gives you a clean, detailed summary so you're always in the loop. And yes, there's even a virtual version of me you can chat with. It's all part of our member experience. 63% discount for a year. There basically are two types of these generators from scratch tools like Sora, Runway or Hyper. Start with pure noise and create every frame from nothing, giving you proper clips with solid shadows, rippling fabric and smooth depth. The second type is assembly editors. Nvidia, VA, Fleek. These ones take another path. An LLM writes a quick script. The system grabs stock footage, adds an AI voice over and music, and trims everything to length. In short, one group invents each pixel, the other stitches readymade pieces together, and both slash the gap between a passing thought and a finished video.

Video generators are a new thing, but prompting for them is simple. Talk to a video model the same way you would brief a film crew. Again, five box formula first, add-ons later, raw task, context, constraints, format. On top, I mix in motion hints so the AI knows what moves and what stays put. Because video prompts get wordy, stick to one subject, one place, one camera move, unless the story truly needs more. Cram in too much and the model can lose the plot halfway through the clip. If the scene is complex, split it into beats. Make each shot on its own, then stitch them later in post. Remember to directly mention the style. Call the shot handheld for a bit of shake, steady cam for a glide, time-lapse to race the clouds, or slow-mo to stretch the action. We've just released a video with art styles you can try in image and video generation. Check it out. Now, with videos, negative cues matter way less. If you're worried about stray logos, say avoid brand marks. To keep one hero on screen, add single protagonist only. But I would rather not mention anything you don't want. Sometimes AI can edit because it was in the prompt regardless of how you write it. No one promised it was going to be easy, right? All that was about full generators because when you work with the assembly tools, you just have to keep prompts simple. Usually all you need is an idea for a video. No style data or action descriptions. These generators are really simple. So try to focus on the story rather than on visuals. Remember, video AI is still finding its legs. Faces can blur and fast moves may stutter. Don't expect perfection. Let it try until it gets everything right.

Outside the world of chat bots and image generators lives a whole world of singlepurpose models that hide in apps, dashboards, or even phones. Audio tools is the loudest example. texttospech ones like 11 Labs. Take a plain script, chop it into phonemes, guess the pitch and timing, then weave everything into one smooth waveform. Voice clonein with them needs only about a minute of reference audio to copy your accent and breathing pattern, which is great for dubbing videos. We use 11 Labs all the time and you never notice. Then there's music generators like Sunno, Udo, and Refusion that create new songs with lyrics from a simple prompt. Transcribing tools, note-taking tools, PDF editors with chat tools for designers, even plugins for developers. There's hundreds of tools you can try. All these tools feel different on the surface. Whether you're typing, clicking, or talking, but under the hood, they share the same habit. Shove input into numbers, spot patterns, and guess the next best output.

Every narrow focused AI tool speaks its own language. So the usual fivebox backbone bends into a custom formula for each one. A music model wants just the essentials. Genre, mood, tempo, length, and maybe a reference track. Something like energetic fun groove, 110 BPM, 30 seconds, no vocals, early '7s James Brown hits. It gives the tool all the data it needs. And sometimes there's even no window for prompts at all. So you end up picking options from the list. Textto speech systems like 11 Labs care mostly about the script of voice ID and pacing. There's a lot of built-in tools for controlling the generated voice and no prompting whatsoever. In some generators, you can pick the mood. In others, you hope for the best. And for all the email writers, PDF chatters, code, and helpers, you just need to be as direct as possible. No roles, no limits or formats, only the task and how you want it done. That's it. super simple, super direct. Ultimately, AI is easy to master. You just need to stay persistent, keep trying new techniques, and never settle for the results it gives. There's always room for improving your prompts, always. All that's left for you is to watch our guys in this channel. And be sure to check out our new generative AI course. Oh, and if you liked our call out and want to sponsor a video, write an email to collabinerdia. com. Thanks for watching and see you soon.