Large Language Models (LLMs) may seem complex, but their core workflow can be understood through a few key components: 1. Tokenizer** — Converts text into tokens that the model can process. 2. Token Embeddings** — Transforms tokens into numerical vectors that capture meaning and relationships. 3. Transformer Layers** — The heart of modern LLMs, using **Self-Attention** and **Feed-Forward Networks** to understand context. 4. Multi-Head Attention** — Allows the model to focus on different parts of the input simultaneously. 5. LM Head** — Converts the model's internal representation into probabilities for the next token. 6. Detokenizer** — Converts the generated tokens back into human-readable text. The fascinating part is how these components work together repeatedly to transform a simple text prompt into a meaningful response. Understanding this architecture is a great starting point for anyone learning Generative AI, Transformers, LLMs, and AI Engineering Visualized and developed by Arun Pandey