AI-Native Development

AI/ML Engineering Track | Phase 1

Best for: engineers who want to become effective users and operators of modern AI coding tools, not just casual prompt users.

This phase is about practical leverage: tool choice, local models, CLI agents, IDE agents, MCP, and runtime control patterns.

Modules

#	Module
1.1	AI Coding Tools Landscape
1.2	Local Models for AI Coding
1.3	Claude Code & CLI Deep Dive
1.4	Agent-First IDEs
1.5	CLI AI Coding Agents
1.6	Prompt Engineering Fundamentals — moved to AI Engineering Foundations
1.7	AI-Powered Code Generation
1.8	AI-Assisted Debugging & Optimization
1.9	Building with AI Coding Assistants
1.10	Anthropic Agent SDK and Runtime Patterns

How To Read This Phase

Suggested path:

1. start with the tooling landscape
2. decide whether you are local-first, cloud-first, or hybrid
3. learn CLI and IDE agent workflows
4. understand MCP and runtime patterns only after the basic workflows make sense

If you want the shortest useful route:

• AI Coding Tools Landscape
• Claude Code & CLI Deep Dive
• CLI AI Coding Agents
• Anthropic Agent SDK and Runtime Patterns

Boundary With The Top-Level AI Track

This phase is easy to confuse with AI-Native Work, but they are not the same layer.

• AI-Native Work teaches how to use AI productively and responsibly in daily work
• AI-Native Development teaches how engineers evaluate tools, run coding agents, control runtimes, and build implementation habits around them

If you still mainly need:

• better prompting judgment
• workflow discipline
• human-in-the-loop habits
• safer use patterns

go back to AI-Native Work first.

For deep architectural treatment of prompts, context boundaries, and harness orchestration, see AI Engineering Foundations.

After This Phase

• go to Generative AI if you want stronger model intuition
• go to Frameworks & Agents if you want to design full agent systems instead of just using coding agents