Generative AI Fundamentals
Build a clear mental model of the models, tokens, and prompts behind modern generative AI.
Start with the mental model behind generation: how models represent language, choose tokens, follow instructions, and fail. These lessons connect the vocabulary to small experiments you can run before building a larger application.
What is Generative AI?
Understand how generative models create new text, images, audio, and code from learned patterns.
8 min read →Transformers Architecture
Learn why attention and transformers became the foundation of most modern language models.
8 min read →Tokens, Context Windows, and Temperature
Make sense of the units models read, the context they can see, and the controls that change output variety.
8 min read →Prompt Engineering Best Practices
Write clearer prompts that give models the role, task, context, constraints, and output format they need.
8 min read →Generative AI Use Cases and Limitations
Match generative AI capabilities to practical use cases while understanding where human review remains essential.
8 min read →Prompt Patterns for Coding with AI
Learn prompt structures that get more accurate, reviewable code out of an AI coding assistant instead of confident-looking guesses.
7 min read →Prompt Patterns for Data Analysis
Structure prompts so an AI assistant helps you explore and summarize data accurately instead of inventing plausible-sounding numbers.
7 min read →Prompt Patterns for Writing and Editing
Get drafts and edits that sound like you and preserve your meaning, instead of generic AI-flavored prose.
6 min read →