How ChatGPT ACTUALLY Works | LLMs Explained From Scratch | AI Full Course

#ai #aifullcourse #llm what you will learn in this video: what is an llm what does large language model actually mean how ai processes human language what are tokens and tokenization how llms learn from huge amounts of text how llm training works what are learned parameters how next token prediction works how an llm generates a complete answer how context affects an llm response what is the attention mechanism why attention is important for language models evolution from rnn to lstm to transformers what are transformers what is the attention is all you need paper what does gpt actually stand for how generative pre-trained transformers work prompt to llm to response does chatgpt actually understand what we say why llms can generate intelligent-looking responses why llms can hallucinate the limitations of large language models how llms connect with generative ai, rag and ai agents What actually happens when you type a question into ChatGPT and get an answer in seconds? Does ChatGPT really understand what you are saying, or is something else happening behind the scenes? You will learn what an LLM is, how language is converted into tokens, how LLMs learn from massive amounts of text, and why next-token prediction is the fundamental idea behind generating responses. We will also explore how a single token prediction turns into complete sentences, paragraphs, and conversations. This video also explains context and the attention mechanism, and how AI evolved from RNNs and LSTMs to the Transformer architecture that powers modern language models. You will understand what Transformers are, what GPT stands for, why the 2017 Transformer breakthrough was so important, and how a prompt is transformed into a response. Most importantly, we will answer a question that many people have: Does ChatGPT actually understand what we say? We will explore why LLMs can appear incredibly intelligent while still having limitations such as hallucinations.