Learning path

Learn to build with AI

A builder path through LLMs, RAG, agents, APIs, and small shipping projects — interactive labs included.

Intended learner: Developers moving into LLM apps and ML engineers leveling up
Prerequisites: Basic programming; Python recommended; Comfort running local tools and reading errors
Recommended pace: 24 weeks · 5 hr/week · 50-minute sessions
Core spine: 119 lessons · ~18.8 hrs
Difficulty: Advanced
Outcome: Build and deploy a RAG assistant with tools, evals, tracing, and controlled release.
Capstone project: Ship a production-shaped AI app — A deployed RAG or agent app with evals, tracing, guardrails, and a controlled release.

Core progress0/119 · 0%

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In the wild

Catalog picks that match this path — explore products and vendors alongside the curriculum.

Tools

Cursor — Ship features faster with an AI-native editor.
n8n — Wire agents and APIs into real workflows.

Companies

Anthropic — Strong long-context models for production assistants.
OpenAI — APIs many production stacks still start with.

All tools · All companies

Optional self-check

Find your best starting phase

Use this short self-check to skip familiar foundations without hiding the complete roadmap.

Check only what you can already do without step-by-step help.I can demonstrate the ai literacy outcomes with an example of my own.I can demonstrate the how ai systems work outcomes with an example of my own.I can demonstrate the practical ai use outcomes with an example of my own.I can demonstrate the build and ship outcomes with an example of my own.

0/4 signals · Start from the foundations

Recommended entry: AI literacy. This does not mark lessons complete; use it to challenge out of familiar material.

Start at this phase →

Core roadmap

Learn in phases, build as you go

Open a module when you are ready. The full lesson sequence is still preserved for resume and next-lesson navigation.

Phase 1

AI literacy

Recognize AI, its limits, and the safety decisions that come first.

Foundations and limitsBuild reliable judgment about where AI helps and where it fails.5 lessons

Phase 2

How AI systems work

Build a working mental model of language models, retrieval, tools, and agents.

Model foundationsConnect system behavior to the technical ideas underneath it.12 lessons

Builds on: AI literacy

Retrieval and toolsConnect system behavior to the technical ideas underneath it.12 lessons

Builds on: AI literacy

Agents and evaluationConnect system behavior to the technical ideas underneath it.10 lessons

Builds on: AI literacy

Phase 3

Practical AI use

Prompt, verify, and apply AI to useful work without outsourcing judgment.

Prompt foundationsPractice a repeatable workflow and keep a human verification step.12 lessons

Builds on: How AI systems work

Phase 4

Build and ship

Turn the concepts into tested projects and portfolio evidence.

Python foundationsComplete hands-on labs and keep evidence of what works.9 lessons

Builds on: Practical AI use

Classical ML & visionComplete hands-on labs and keep evidence of what works.9 lessons

Builds on: Practical AI use

LLM apps & agents · Part 1Complete hands-on labs and keep evidence of what works.12 lessons

Builds on: Practical AI use

LLM apps & agents · Part 2Complete hands-on labs and keep evidence of what works.12 lessons

Builds on: Practical AI use

Ship & operations · Part 1Complete hands-on labs and keep evidence of what works.12 lessons

Builds on: Practical AI use

Ship & operations · Part 2Complete hands-on labs and keep evidence of what works.10 lessons

Builds on: Practical AI use

CapstonesComplete hands-on labs and keep evidence of what works.4 lessons

Builds on: Practical AI use