Suggested Learning Paths

The three learning paths are also shown on the homepage.

Foundation Path

Expected prior knowledge: general programming familiarity.

Outcome: understand the four-area map and the baseline concepts needed for AI Engineering study.

1. What is AI Engineering?
2. The four-area skills map
3. Machine learning
4. Requirements and design
5. AI suitability

Builder Path

Expected prior knowledge: software engineering experience and basic AI literacy.

Outcome: design, evaluate and operate grounded AI application workflows.

1. Foundation models and LLMs
2. Context engineering
3. Grounding and RAG
4. Tool calling and MCP
5. Evals and error analysis

Agentic Development Path

Expected prior knowledge: comfort with repositories, tests and code review.

Outcome: use coding agents deliberately, safely and with evidence-based completion.

1. Coding assistants versus coding agents
2. The agent loop
3. Configuration, permissions and sandboxing
4. The ten-step working method
5. Multi-agent orchestration

The four top-level areas follow Andrew Ng’s AI Engineering Skills Map. Area 1 has been elaborated by Ng; detailed Areas 2-4 structures are this knowledge base’s reasoned extension.