This section treats generative AI as trained models that produce new content in response to input. The path starts with language-model foundations and then moves to text, image, music, and video generation. It focuses on how output is produced and how to compare approaches rather than on operating a particular product.
Learn the language-model foundation in order
Start with What Is an LLM? to see how text becomes tokens and how a model predicts the next token from context. Continue to Transformer Models for attention, relationships within a sequence, and the roles of encoders and decoders.
With that foundation, BERT vs. GPT compares a design that builds representations of text with one that generates a continuation. Reasoning Models then separates ordinary generation from models or execution modes that spend additional computation on multistep problems.
Choose a generation approach from the intended output
Read How Text Generation Works to follow the selection of each next token and compare ways to favor repeatability or variety.
Visual and audio media require different forms of consistency:
- How Image Generation Works: understand denoising, conditioning, and partial editing
- How Music Generation Works: connect waveforms, symbolic music, and audio tokens with generation methods
- How Video Generation Works: separate consistency within each frame from consistency over time
Choose models from the constraints of the work
Generative AI Models and Intelligence Metrics separates service names from model names and compares quality, speed, cost, long-input handling, tool use, and verification. It helps avoid choosing from a single benchmark or a vague idea of “intelligence.”
After understanding generation, continue to Generative AI Engineering. That section covers instructions, context, tools, permissions, verification, and the conditions that stop repeated work.