Large Language Models Demystified: Transformers, RAG, Agents

About this course

Have you ever wondered what really happens inside ChatGPT, Claude, or Gemini after you press Enter? This course takes you inside a Large Language Model, step by step, and explains the mechanisms in plain language, without hiding behind buzzwords.

You will start with the basics of AI, machine learning, and language modeling. Then you’ll build the math you need (vectors, probability, gradients, backpropagation) so that nothing feels like magic. From there, you’ll follow the full journey of a prompt: tokenization, embeddings, the Transformer architecture, self-attention, positional encoding, and how models are trained at scale.

You’ll also learn how a raw base model becomes a helpful AI assistant through fine-tuning, RLHF, DPO, and alignment. You’ll see how answers are generated token by token with temperature, top-k, top-p, and the KV cache. Finally, you’ll explore modern topics such as in-context learning, RAG, tool use and AI agents, Mixture-of-Experts, hallucinations, bias, security, evaluation, quantization, LoRA, and the future of LLMs.

What you’ll learn

How LLMs work from input text to final output

The math behind neural networks, explained simply

How tokenization and embeddings turn text into numbers

How self-attention and multi-head attention work

How LLMs are trained, fine-tuned, and aligned

How decoding strategies shape the model’s answers

How RAG, tools, and agents extend what LLMs can doWhy LLMs hallucinate, and how they are evaluated and made faster

Who this course is for

Students, developers, data professionals, and curious learners who want to truly understand LLMs, not just use them.

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