How Modern Generative AI Works: From Attention to Transformers
Understand the building blocks behind attention, embeddings, and transformer models
Created by Rahul Kaundal
Get a clear understanding of how modern generative AI models work by exploring the key building blocks behind attention, embeddings, and transformer architectures. You will learn how these components interact to process language and produce context-aware outputs.
Packt | Aug 2026 | 87 min
What You Will Learn
You will start by learning about the evolution of language models, then move step by step through tokenization, embeddings, and attention. Each section builds on the last, using practical examples and walkthroughs to help you connect the pieces and understand the full transformer architecture.
Key Features
- Break down tokenization, embeddings, and attention into simple, actionable concepts
- Interpret self-attention and follow data flow through encoder and decoder structures
- Connect model components to see how transformers create meaningful language output
Target Audience
This course is ideal for developers, data professionals, technical managers, students, and anyone curious about AI who wants a strong conceptual foundation in modern language models. If you have some familiarity with basic machine learning or NLP terms, you'll find the material especially accessible, but beginners are welcome.
