AI Tools EXPLAINED: How to Use Them? (2025 Guide for Beginners)

Summary

Highlights

• AI tools are not all-knowing; each excels in specific tasks. 🤔
• Large language models work on patterns from vast text data. 📚
• Image generators begin with noise and refine images through diffusion. 🌌
• Prompting should be descriptive to guide models effectively. 🗣️
• Productivity AI tools help streamline tasks but offer limited creativity in inputs. 🗂️
• Collaborate with teams to leverage AI efficiently in content creation. 🤝

Key Takeaways

• Understanding AI can make you more efficient in using AI tools like Chat GPT and others. 🤖
• Most AI tools are exceptionally good at one task; they're not general smart entities. 🧠
• Prompting is key - be descriptive and detailed to get better results. ✍️
• Image and video generators work differently from language models; understanding their uniqueness helps in better use. 📸🎥
• AI tools enhance productivity, but choosing the right tool for each task is crucial. 🛠️
• Working with a team can boost your productivity and channel success on platforms like YouTube. 🎬

Overview

Chapters

• 00:00 - 00:30: Introduction to AI Tools The chapter titled 'Introduction to AI Tools' discusses the common misunderstanding of AI among users, particularly with tools like ChatGPT. The presenter aims to educate viewers on the basics of AI, covering different types of AI, their workings, and practical applications. By breaking down these concepts, the chapter aims to empower viewers to become proficient in using AI tools and navigate through the buzz surrounding AI.
• 00:30 - 02:00: Understanding AI Systems The chapter "Understanding AI Systems" aims to dispel common myths about AI and clarify what AI systems truly are. It explains that AI is often misunderstood as an all-knowing entity, when in reality, most AI applications today are designed to perform specific tasks very effectively. The narrative compares AI to a 'big umbrella' under which various forms like language models (e.g., ChatGPT), image generators, and robotics exist. The essential point is that AI systems are more specialized tools than generalized thinkers or 'brains.'
• 02:00 - 03:00: Neural Networks Explained This chapter delves into the concept of neural networks, exploring them as systems designed to mimic human-like intelligence. It highlights that while these networks can solve problems, recognize patterns, and make predictions, they fundamentally differ from human thinking. Neural networks lack feelings, consciousness, and spontaneous insights ('aha moments'). Instead, they operate by following pre-set plans and making successive predictions. The chapter also notes the fascination with neural networks, describing them as impressive but not magical. Additionally, viewers are informed about a website where reviews of various AI tools (AI TOS) tested in the video are posted.
• 03:00 - 04:00: Types of AI Tools Available The chapter discusses various types of AI tools that are currently accessible to consumers. These include large language models, image generators, audio generators, video generators and editors, voice assistants, and productivity AIs. Despite appearing different, these tools function on the same basic principles.
• 04:00 - 05:00: Training Neural Networks The chapter titled 'Training Neural Networks' explains that AI is not a thinking machine but a system of neural networks that learn patterns in data to make predictions and generate results. Neural networks function like layered filters, where each layer processes and refines the data further before passing it to the next layer, leading to a final output. Initially, neural networks are not intelligent; they require training, a process handled by developers.
• 05:00 - 06:00: Large Language Models & Transformers This chapter explores the inner workings of large language models and transformers, focusing on their training process. It describes how these models are fed vast amounts of data, such as text, photos, or videos. The models make guesses and adjust their internal parameters through a repetitive process of trial and error, gradually improving their ability to recognize patterns. This training occurs millions or even billions of times, after which the models become proficient in interpreting prompts and generating appropriate outputs.
• 06:00 - 07:00: Prompting Techniques for LLMs The chapter discusses the integration of AI in enhancing workflows, particularly in a YouTube agency. It emphasizes that while AI doesn't replace human effort, it boosts productivity. The chapter then introduces various large language models (LLMs) such as Chad GBT, Gemini, Claude, and others, highlighting that despite the frequent emergence of new models, they fundamentally operate using similar mechanisms—primarily Transformers, but at varying scales.
• 07:00 - 08:00: Image Generators & Diffusion Models The chapter discusses how transformers, a type of model in artificial intelligence, work by processing inputs such as questions. They calculate the relationships between keywords and predict the most probable outputs. An example provided is how a transformer might deduce that a 'wheel' corresponds to a 'circle' based on how the words relate within the model's trained data. The chapter emphasizes the role of vast amounts of data in these models' ability to make accurate predictions.
• 08:00 - 09:00: Choosing the Right Image Generator The chapter discusses the selection of an appropriate image generator. It explains the importance of how models can focus on key elements like 'shape' and 'wheel,' using attention mechanisms to prioritize important information over irrelevant data. This process is critical in various applications such as writing, coding, and data analysis. Unlike human understanding, these models interpret words as numbers, probabilities, and mathematics.
• 09:00 - 10:00: Building a YouTube Channel with AI The chapter discusses the nuances of prompting different language models (LMs) and how each model interprets them uniquely.
• 10:00 - 11:00: Prompting for Image & Video Generators The chapter emphasizes the importance of being descriptive and detailed when creating prompts for image and video generators. It highlights the need for providing all necessary context and requirements to achieve better results. The chapter encourages specifying what the output should look like, its length, target audience, and the desired style or tone. Being clear and explicit in prompts helps avoid ambiguity and improves the quality of the generated content.
• 11:00 - 12:00: Audio Generators: Music & Text-to-Speech The chapter explains strategies for improving AI model responses in the context of audio generators for music and text-to-speech. It highlights the importance of specifying the audience, format, language, and main ideas for clearer outputs. Roleplay is introduced as an effective method, urging users to instruct the model to act as an expert in a specific field to enhance the accuracy, relevance, and polish of responses. Additionally, setting boundaries on what the model should exclude is recommended to significantly impact the quality of the output. These strategies, when combined, can optimize the performance of audio-generating AI models.
• 12:00 - 13:00: Video Generators Explained The chapter 'Video Generators Explained' discusses differences between image generators and language models (LMs) in the AI world. While LMs focus on linguistic data, image generators are trained on visual data. The chapter also provides guidance on becoming proficient in designing prompts for AI models, with additional resources promised for subscribers.
• 13:00 - 14:00: Voice Assistants in Everyday Use The chapter explores the functioning of voice assistants in everyday contexts by explaining the underlying technology that powers them. A key aspect highlighted is how models get trained to understand and interact with visual elements. Millions of images paired with descriptions help the model learn patterns by recognizing pixel groupings that represent objects such as a cat or a tree. This training enables the model to comprehend what each word in a prompt means regarding pixel associations. Therefore, when a command like "generate an image of a Floy black hat with glowing green eyes" is given, the model doesn't merely retrieve an image from a database. Instead, it intelligently constructs the image based on the relationships it's learned.
• 14:00 - 15:00: Productivity AIs & Automation Tools In this chapter, the concept of image generation through AI using diffusion models is discussed. It explains that image generators do not create images from scratch but rather start with a 'blank canvas' composed of static noise. Through diffusion, this noise is gradually transformed into a detailed image. This process is what qualifies these systems as diffusion models. The chapter also highlights a technical aspect where the base image is a chaotic mix of black and white pixels which, when summed, yields a value of zero. This characteristic may contribute to AI-generated images sometimes feeling slightly 'off' or lacking in some way.
• 15:00 - 16:00: Conclusion: Effective Use of AI Tools The chapter focuses on understanding the nuances of AI-generated images, specifically how contrast and lighting can reveal their origins. It discusses popular image generators, highlighting DALL-E for its ease of use but noting that full features may require a subscription. Gemini, while capable, is less flexible or imaginative, whereas Doe Express offers user-friendly and adjustable features, though it might not always yield the desired creativity.

AI Tools EXPLAINED: How to Use Them? (2025 Guide for Beginners) Transcription

• 00:00 - 00:30 most people using Cad gbt and similar tools don't even understand what it really is and how it works understanding just the basics of AI can make you so much better using tools like Chad gbt image generators or even more advanced systems so 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 here's the problem AI is a total buzzword right now anything that seems remotely smart
• 00:30 - 01:00 gets labeled as AI whether it's a Chad mod autoc correct or even your fancy fridge that knows when you're out of milk people think AI is some kind of a all knowing Super Genius but the truth is that most AI systems today are just really good at doing one specific thing that's it they are toes not brains think of AI as a big umbrella underneath it you've got things like large language models that's Chad gbt image generators robots you name it at its core AI is
• 01:00 - 01:30 just a system designed to mimic humanlike intelligence can solve problems recognize patterns make predictions stuff that looks like thinking but it's not thinking like we do it has no feelings no consciousness 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 TOs mentioned I got you we have a website where we post reviews of all the AI TOS we test
• 01:30 - 02:00 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 that we can already use large language models image generators audio generators video generators and editors voice assistants and productivity AIS these are the core ones that most of us can use right now these TOS might seem wildly different but they all work on the same basic principles and just to set the record
• 02:00 - 02:30 straight what we call AI isn't some thinking machine it's actually neural networks let me explain add their core neural networks are systems that learn patterns in data and use those patterns to make predictions and generate results imagine them as a bunch of layered filters each layer process the data passes it to the next layer which refines it even more and so one by the end you get the final output now neural networks don't start smart they have to be trained that's where developers come
• 02:30 - 03:00 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 gets really good at recognizing patterns and generating results at that point it's ready to take prompts from you and turn them into
• 03:00 - 03:30 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 video about it is already on the channel now let's go over each type of neural networks you can use right now large language models Chad gbt Gemini Claude mral Gro feels like a new one pops up every week right here's the thing they all work basically the same way just at different scales how do they work Transformers
• 03:30 - 04:00 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 that into keywords like shape and wheel then it calculates how those words relate it looks at the data and think all right the next word with the highest probability here is circle boom it gives you the answer why does it get that right two main reasons first massive and data these models have read
• 04:00 - 04:30 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 like shape and wheel instead of random filler that same process works for anything these models do writing essays coding analyzing data you name it but they don't understand like we do for them there are no actual words it's all just numbers prob abilities and math when it
• 04:30 - 05:00 comes to prompting LMS you might think it's all about creating the perfect structure and nailing the right attributes and yeah that's true kind of but there's a Twist every model interprets prompts a little differently bigger models are way more forgiving Chad gbt for example is the best out there and you can basically talk to it in natural language same with Gemini but smaller models like mstl or Claude they might need need you to step up your prom
• 05:00 - 05:30 game and be a bit more structured that said the core roles of prompting are pretty much Universal first be descriptive models love big detailed prompts with all the context and requirements laid out don't skimp and explaining what you need tell the model what the output should look like how long it should be who's going to read it what style or tone to use everything the more you explain the better the results you don't win the model guessing what you want P it out be clear about the
• 05:30 - 06:00 audience the format the language and the main ideas Second Use roleplay it sounds simple but it's crazy effective telling the model to act like an expert in a certain field can dramatically improve the response Narrows down the data the model post from making the output more accurate more relevant and Polished and don't forget to set limits make sure the model knows what not to include this is another small tweak that can have a big impact and yeah you can pile all these
• 06:00 - 06:30 instructions into one big prompt if you're using a free version of the model but if you are in 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 [Music] it the second big player in the AI world is image generators these models operate totally differently from LMS sure they're also trained on massive data sets but instead of focusing words they
• 06:30 - 07:00 work with visual elements the model gets fed millions of images each paired with a description over time starts to understand patterns like how certain groups of pixels represent a cat or 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 Floy black hat with glowing green eyes it doesn't just pull an image from a database instead it uses the relationships it's
• 07:00 - 07:30 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 detailed image that's why these systems are often called diffusion models the Basse 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 generate image Imes often feel a little off they lack
• 07:30 - 08:00 natural contrast or highlight that stand out if you're wondering if an image was AI generated check the contrast 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 subscription Gemini can do images but it's not the most creative or customizable doe Express is user friendly with lots of tweakable controls but can sometimes
• 08:00 - 08:30 turn out odd results Med journey is the gold standard the web version is solid but using Discord unlocks more features though it requires a specific way of writing promts Runway is decent for images but shines in video generation we mostly use Smith journey in my YouTube agency and if you're thinking about integrating AI into your workflow here's my advice pick one toll 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
• 08:30 - 09:00 don't see the results they hope for and end up abandoning the Channel with real potential I almost fell into that trap when I was starting out so what changed I built a team trying to do everything on my own became exhausting and it started killing the fun bring people on board turn things around completely that initial team they are 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 my team can help you with
• 09:00 - 09:30 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 plan tailored to your goals our designers will create thumbnails that demand attention and our writers will whip up titles to get clicks need help with scripts we've got experienced script writers who can nail it for you not sure how to set up your studio our director can hop on a call and guide you to create a pro level setup on a budget we can even edit your videos and take
• 09:30 - 10:00 care of publishing all the 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 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
• 10:00 - 10:30 visuals colors elements composition textures and more think of your prompt as NeverEnding 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 squeezed every detail out of the image boom you've got yourself a Target prompt use that as a template to write your own why
• 10:30 - 11:00 does this matter because image generators can easily get wild with guesses if you're vague the results might completely miss the mark to avoid this add negatives to your prompt too for example if you don't want blurry edges muted colors or unnecessary objects just 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
• 11:00 - 11:30 text to speed generators and music generators while they serve different purposes they work in pretty much the same way both are trained in massive data sets either music tracks or voice recordings paired with transcriptions from there it's all about probabilities the models calculate sound waves for each fraction of a second based on patterns they've learned music generators like suo muar and refusion focus on understanding elements like Melody Rhythm Harmony an instrumentation
• 11:30 - 12:00 when you give them 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 figure out how to turn it into speech tools like Le Labs or speech easyy analyze each letter syllable and word to calculate how they should sound to together they then use
• 12:00 - 12:30 this data to synthesize natural sound and 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 not much prompting involved for music generators many TOS don't even have a typical prompt in box instead you're usually adjusting parameters like B BM style and
• 12:30 - 13:00 mood however some toes like sunno do let you write a text description of the song you want you can even have sunno generate lyrics combining its music generator with LMS and text to speech tag if you're using sunno keep it simple and to the point describe the music style the mood how it should feel and maybe the BBM no need for specific phrasing just focus on being clear as for text to speech TOS like 11 Labs there is really no prompting involved you past the text you want turned into
• 13:00 - 13:30 speech pick a voice and tweak the properties to suit your needs once it faster slower more energetic just accordingly some tools even let you clone your voice which is a cool extra but again no actual prompts 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 video These models are trained in massive data sets of videos is paired with descriptions
• 13:30 - 14:00 from this they learn patterns and how frames change the spatial relationships 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 start generating frames one by one starting with a base image for each frame 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 creting new content
• 14:00 - 14:30 toes like Sora hyper Runway and pickup fall into this category these toes generate frames from scratch following the patterns they've learned during trading for editing TOS like nid thisa and Flaky take a different approach they first process your prompt using LM to create a story line that story line is then broken down into scenes with keywords generated for each one the toll uses these keywords to search is built in footage Library selects relevant
• 14:30 - 15:00 clips and music generates a voice over using text to speech and stitches everything together into final video prompting for video generators is very similar to prompting for image generators but with an edit layer 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 with each other give as
• 15:00 - 15:30 much detail as you can but keep it simple and Vivid and don't over complicate 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 prompting involved most of the time you just provide a general description of the
• 15:30 - 16:00 video or the plot idea the AI takes it from there either handling everything automatically or giving you a few options to choose from these TOS are super intuitive and if you want to get the hang of them quickly we've got some great videos on the topic so definitely check those 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 about creating content as they are about understanding and acting on data so
• 16:00 - 16:30 honestly they're not that smart on their own most of the heavy lifting comes from transcribing voice requests and figuring out the best action to take they all work in three stages speech to text intent recognition and processing text to 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 assessment for example the new Siri not out yet is expected to come with real context
• 16:30 - 17:00 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 requests however you want and the assistant will figure out the rest no prompting structures no secret tips you just talk normally and hope for the best one more type of AI you can use right now is productivity best bed these smart TOS are popping up in all sorts of
• 17:00 - 17:30 apps helping you write organize and just get stuff 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 TOS that can rewrite paraphrase or adjust the length of your messages then there are platforms like Tas Cade they streamline managing workflows and processes simpli F collaboration and
• 17:30 - 18:00 overall keep you on top of your schedule these tools can generate project outlines assign tasks and track progress which is practically useful for remote teams and let's not forget AI powered CRM tools like HubSpot or pipe Drive they take the all boring CRM systems and flip them using AI to optimize your workflow on top of this there are tools like zapier or integromat that help connect different apps and automate tasks making your work like life smoother so whether you are drowning and
• 18:00 - 18:30 emails juggling tasks or managing customer relationships there's probably an AI toll 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 TOs and put the reviews on the website a.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 type out a detailed request and get a tailored response these productivity TOS are more
• 18:30 - 19:00 well locked in they're mostly Standalone setups and you're kind of stuck with the options they give you you press a few buttons choose what you want and boom that said no room for much creativity or flexibility there are TOS for almost everything presentation generators legal document analyzers recruitment screening tools coding assistance financial planners supply chain optimizers scientific research AIDS you name it but no matter the AI toll you're working with the golden rle stays the same be
• 19:00 - 19:30 detailed be descriptive and straight to the point clear inputs lead to better outputs and of course practice makes perfect and of course of course of course click the link in the description to partner up with the best YouTube team ever thanks for watching and I'll see you in the next video [Music]