AI Building

AI Building | 4 modules | ~8-12 hours

Overview

This section closes the gap between using AI tools and building AI-powered systems.

It is for learners who already understand basic AI literacy and want to move from:

• using chat tools
• verifying outputs
• designing safer workflows

to:

• building simple AI features
• choosing sane architecture patterns
• understanding where APIs, context, tools, and retrieval fit together

This is not yet full AI/ML engineering.

It is the bridge layer between AI-Native Work and AI/ML Engineering.

Modules

Module	Topic
1.1	From Chat To AI Systems
1.2	Models, APIs, Context, and Structured Output
1.3	Tools, Retrieval, and Boundaries
1.4	Evaluation, Iteration, and Shipping v1

Outcome

By the end of this section, you should be able to:

• explain the difference between using AI and building an AI feature
• choose between plain prompting, structured output, tools, and retrieval
• recognize when a workflow needs evaluation instead of more prompting
• sketch a small but sane first version of an AI-powered product feature

Recommended Route

AI Foundations
   |
AI-Native Work
   |
AI Building
   |
AI/ML Engineering (optional deeper path)

After This Section

Choose the next route based on your goal:

Goal	Next Step
build practical applications first	Open Models & Local Inference
understand local models and runtimes before deeper engineering	Open Models & Local Inference
apply AI directly to Kubernetes and platform practice	AI for Kubernetes & Platform Work
understand LLM internals better	AI/ML Engineering: Generative AI
design full agent systems	AI/ML Engineering: Frameworks & Agents
operate AI systems in production	AI/ML Engineering: MLOps & LLMOps