GenAI Foundations
Beginner
Build the foundation · 9 tutorials · 15-25 min each
Understand and build the baseline AI application patterns used across the rest of the site.
What is Generative AI and How It Works
Understand what generative AI actually does - not the hype, but the mechanism. How text, images, and code come out of a model and why it matters for your role.
Understanding Large Language Models (LLMs)
Tokens, context windows, temperature, and why hallucinations happen - the core mechanics every practitioner needs to know before building with AI.
How to Use APIs to Access AI Models
Make your first AI API call. Understand the difference between OpenAI, Anthropic, and Google APIs, and learn the request/response pattern that powers every AI application.
Writing Effective Prompts
The 4-part anatomy of a great prompt: Role, Task, Context, Format. Learn zero-shot, one-shot, and few-shot techniques that actually work in production.
Structured Input vs Structured Output
Why unstructured AI responses break your application and how to use JSON mode to get predictable, parseable output every time.
Generating Clean Structured Data Using Schemas
Use Pydantic and JSON Schema to constrain AI output to exactly the shape your code expects. No more parsing failures or unexpected fields.
Prompt Templates and Dynamic Prompts
Hard-coded prompts don't scale. Learn how to build reusable, testable prompt templates with variable substitution - from f-strings to LangChain PromptTemplate.
How LangChain Connects Everything Together
LangChain's LCEL syntax lets you chain prompt → model → parser in a single expression. Build your first AI pipeline in 10 lines.
How Real-World AI Applications Are Structured
The 4-layer architecture of production AI apps: UI, API, AI engine, and data. Where failures happen at each layer and how to design for resilience.