How LLMs Work? | How Large Language Models Work | What Are LLMs? | LLMs Explained | Simplilearn

What if I told you every time you [music] talk to an AI, it's not actually thinking, it's just predicting the next word? [music and snorts] And yet somehow it feels intelligent. So, what's really going on here? How are your daily AI assistants like ChatGPT, Claude, Gemini, or any other AI model is actually [music] working? It's mind-blowing, right? Well, I am here to simplify that [music] for you. Welcome to Simplilearn, and today we will understand how LLMs [music] work. In this video, we will break this down in the simplest way possible. What exactly is a large language [music] model? How it actually works behind the scenes and why it feels so smart even though it's not thinking. [music] By the end, you'll understand LLMs better than 90% of the people using them. That said, if these are the type of videos you'd like to watch, then hit that subscribe button and hit that bell icon to get [music] notified whenever we post. Also, just so that you know, if you want to upskill yourself, master generative AI and artificial intelligence skills, and land your dream job or even grow in your career, [music] then you must explore Simplilearn's cohort of various generative AI courses and certifications. Simplilearn offers a variety of certification programs in collaboration with some of the world's leading universities. Through our courses, you will gain knowledge and work-ready experience in [music] skills like advanced Python, machine learning, generative AI, and over a dozen others. And that's not all, you'll also get the opportunity to work on multiple projects led [music] by industry experts working on top-tier data and product companies. After completing these courses, thousands of learners have transitioned [music] into AI and machine learning role as a fresher or moved on to a higher-paying job [music] and profile. If you're passionate about making your career in this field, then make sure to check out the link in the pinned comments and in the description box to find generative AI program that fits [music] your experience and areas of interest. Now, let's test your knowledge on LLM with a small quiz. [music] Why do LLM response sometimes vary for the same prompt? And [music] your options are: They access different databases each time, they randomly guess answers, they sample from probability distributions, or they learn in real time. Don't forget to leave your answers in the comment section below. Now, let's get started with the core idea. Imagine [music] this, you find a movie script, it shows a conversation between two characters, but the second character's response [music] is missing or somehow inaudible. Now, imagine you have the machine that can predict the most likely next [music] word. So, you start filling in the response one word at a time based on probability until a full answer is formed. [music] That's exactly how modern AI works. A large language model is essentially a system that predicts what word should [music] come next. Now, let's take a simplification break. It does not happen with one word of certainty, but it's [music] more like a probability distribution. For example, let's take a sentence, "The sky is blue." And let's leave the color blue and give it to the LLM. "The sky is and a blank space." Let the AI decide. The probable options are blue, which is 85%, green, which is 5%, [music] and falling is 2%. The model picks from these probabilities, sometimes [music] even randomly. That's why you slightly get a different answer each time and response feel more human. Now, let's dive into our next question. [music] Why do even AI seem intelligent? Now, here's the interesting part. If it's just predicting [music] words, why and how does it even feel so smart? It's because of two important factors, the scale and the patterns. Together, these models are trained on books, websites, code, conversations, and a lot more. To give you a possible perspective, [music] some models even have efficiently processed more text than a human could ever [music] read in thousands and lakhs of years. So, now what happens? The AI can learn different language structures, all possible permutation and combinations of every possible context, multiple reasoning patterns, and even tone and style of delivering a response. All this not because they understand, but because they have seen similar patterns before. Getting the vibes of Doctor [music] Strange already? It's not a magic, it's pure mathematics. Now comes the question, who even trained these models so well and how training works? Let's drop some light on these training. Think of it like teaching a student. Step one is to show a sentence. Let's say, [music] "AI is transforming the." Now, step two is to hide the [music] last word. Then we have step three, which is ask the model to guess. At first, it gives a not-so-sensible answer, but [music] then comes an important process, the backpropagation. This adjusts the model slightly every time it makes a mistake. [music] Over millions, billions, even trillions of examples, then the model improves. Think of it like this, it's not memorizing answers, it's learning patterns like questions usually end this way. Explanation usually follow this structure. Code behaves like this. So, when you ask something new, it constructs an answer based [music] on the learned patterns. All this was done word after word. Let's say I give you a sentence, "Learn with Simplilearn." AI used [music] to take this one word at a time, process it, and move to the next one. This was both slow and inefficient. Now, let's discuss something that revolutionized this whole process. The transformers. Not the Michael Bay [music] transformers, the one that Google invented. Google came up with this LLM engine and it is called the transformers. [music] Now, let's talk about the transformer architecture. Not too technical, let's keep it simple comparison. This changed everything in AI. Before transformers, [music] models read text word by word at a time, slow and limited context. After [music] transformers, models read everything at once, understand the relationship between the words. Now comes the real logic part. [music] Transformers use something called as attention. To keep it simple, here's a simple example. He went to the bat after sunset. The word bat is ambiguous. It could be a cricketer or baseball bat, or even a flying animal [music] referred as bat. Attention helps the model figure out. Now, look at the phrases. After sunset, this gives a strong signal. Bats, [music] which is animals, are active at night. Sports bats don't relate to time like this. So, the model learns towards [music] bat is equal to animal. Another variation is, "He picked up the bat and hit the ball." [music] Now, the context changes. Hit the bat is a sports action. Bat is supposed to [music] mean the sports equipment. Now, this is how LLM checks every possible variation and gives out the perfect answer suitable to context, and here it's about cricket. Here's something most people don't realize. AI doesn't actually see words, it sees numbers. Each word is converted into a vector or simply put a list of numbers. These numbers captures [music] the meaning, relationship, context, and then these vectors interact using attention. Since everything is just number of predictions, most of you can imagine it's all mathematical and [music] language model. Then why it is called large? This gravitates can be understood only once we get [music] how large the training model is. So, let's discuss why they are called large and all about the large in LLM. [music] These LLM models have billions of parameters, sometimes even hundreds of billions. A parameter is basically a dial in a system. More parameters is [music] equal to more ability to learn patterns. Here's the catch, no human [music] sets these parameters manually. They start at a random training, slowly adjust them until the model becomes useful. [music] That turns the tables and we see the transition from a model to a chatbot. Now, here's an important distinction. A raw LLM is not [music] a chatbot. To make it useful, we add the reinforcement learning with the human feedback or even RLHF for short. So, whenever [music] an AI rolls out responses, we as humans rate responses, flag bad answers, [music] and improve behavior. This helps AI learn and makes AI more helpful, safer, more aligned with users and even expectations. This continuous feedback [music] loop and learning process plays an important role because LLMs are now powering chatbots, [music] content creation, code generation, customer support, and data analysis, and a lot more [music] than you and I know. They're not just tools, they're becoming a core layer of modern software. Do they don't have any limitations? Did we [music] just create a fully functional AI that has no limits? Well, the answer for that question [music] can be no for now. Because LLMs as of now are facing some limitation, mostly in teams to build trust. Let's stay grounded for a while to understand this LLM. Don't really understand [music] everything all the time. They sometimes can hallucinate and end up in a gray area. LLMs depend [music] heavily on prompts, and if an incomplete or an inaccurate prompt is sent, they cannot complete the task [music] as expected. Yes, they are powerful, but not perfect. So, next [music] time you use AI, remember this. It's not thinking, it's not reasoning like a human. It's doing something simple and in some ways more powerful, predicting the next word at a massive scale. [music] If you found this useful, drop a like and subscribe for more AI breakdowns. And if you want to deeper dive into topics like prompt engineering, AI tools, or even building your chatbot, let me know in the comment section below. This is a Simplilearn's video. Until next time, thanks for watching and stay tuned for more.