# How the AI Engineering from Scratch Curriculum Is Structured: Complete 2025 Guide > Explore the AI Engineering from Scratch curriculum structure. Discover its 20 phases, 511 lessons, and unique 'build-it-then-use-it' approach. Your 2025 guide to mastering AI engineering. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: architecture - Published: 2026-08-26 --- **The AI Engineering from Scratch curriculum is organized as a hierarchical stack of 20 phases containing 511 lessons, each following a five-step "build-it-then-use-it" methodology with three customizable entry points and optional certification tracks.** The [rohitg00/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) repository provides a comprehensive, production-focused learning path that takes practitioners from low-level mathematics to deployed AI systems. According to the source code, this curriculum uses a strict folder hierarchy in `phases/` and consistent lesson beats to ensure repeatable learning outcomes across all artifacts. ## The 20-Phase Architecture The curriculum's backbone consists of **20 sequential phases** that function as broad topical blocks stacking logically upon one another. As visualized in the Mermaid diagram at [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) lines 78-99, these phases begin with environment setup and math foundations, progress through model engineering and agent systems, and culminate in production deployment. ### Phase Organization and Dependencies Each phase follows a zero-padded naming convention under `phases/-/`, creating strict dependency chains that explain why higher-level concepts work. The repository structure includes: - `phases/00-setup-and-tooling/` — Environment configuration and tooling - `phases/01-math-foundations/` — Linear algebra and mathematical prerequisites - `phases/14-agent-engineering/` — Core agent construction patterns - `phases/19-capstone-projects/` — End-to-end system integration This design allows learners to skip ahead if they master lower layers, while preserving the logical dependencies required for advanced topics. ### Phase 19: The Capstone Integration Phase 19 (`phases/19-capstone-projects/`) aggregates projects that combine earlier artifacts—prompts, skills, agents, and MCP servers—into cohesive end-to-end systems. Unlike standard lessons, these projects require synthesizing knowledge from multiple previous phases, validating production readiness according to the source documentation at lines 72-77. ## Lesson Structure and The Five Beats Within each phase, individual **lessons** follow a rigid organizational pattern documented at [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) lines 110-118. The repository contains **511 total lessons**, each designed as a self-contained unit producing reusable artifacts. ### Consistent Folder Layout Every lesson directory uses a standardized three-folder layout at `phases/-/-/`: - `code/` — Runnable implementations in up to four languages - `docs/` — Narrative content including [`en.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/en.md) files - `outputs/` — Generated artifacts, prompts, skills, agents, or MCP servers This predictable structure ensures that learners can navigate any of the 511 lessons without orientation overhead. ### The MOTTO to SHIP IT Learning Loop According to [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) lines 124-132, each lesson implements five specific beats: 1. **MOTTO** — The guiding principle or core concept 2. **PROBLEM** — The specific challenge being addressed 3. **CONCEPT** — Theoretical foundations and explanations 4. **BUILD IT** — Hands-on implementation phase 5. **USE IT** — Practical application and testing 6. **SHIP IT** — Production deployment and artifact generation This methodology creates a "build-it-then-use-it" learning loop that ensures concepts translate immediately to working code. ## Three Entry Points and Learning Paths The curriculum supports **personalized onboarding** through three distinct entry points defined in the `skills/` directory, bypassing linear progression for learners with existing expertise. ### Placement Tutor (start-learning) Located in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md), the placement tutor generates a personalized [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file. This skill assesses prior knowledge and recommends the appropriate phase slice, allowing practitioners to skip foundational material they already master. ```bash # Install and run the placement tutor (requires npx and skill-capable host) npx skills add rohitg00/ai-engineering-from-scratch learn start-learning ``` ### MCP and Agent-Skills Paths Alternative entry points target specific architectural patterns while respecting phase dependencies: - **MCP Path**: Defined in [`skills/learn-mcp/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn-mcp/SKILL.md), generates [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md) and focuses on Model-Context-Protocol fundamentals (covered in Phase 13, `phases/13-tools-and-protocols/`) - **Agent-Skills Path**: Defined in [`skills/learn-agent-skills/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn-agent-skills/SKILL.md), creates [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md) for immediate agent construction These paths optimize the sequence for specific career outcomes without breaking prerequisite chains. ## Certification Tracks The repository includes parallel certification pathways under [`certifications/claude/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/certifications/claude/README.md). These tracks map subsets of the 511 lessons and diagnostics to official Claude exam objectives while utilizing the same lesson infrastructure and folder layouts described in lines 74-82 of the main documentation. ## Practical Navigation Examples To interact with the curriculum structure directly: ```bash # Clone and run a Phase 1 linear algebra lesson git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` ```bash # Execute an MCP lab from Phase 13 python3 phases/13-tools-and-protocols/06-mcp-fundamentals/code/run_mcp.py ``` ## Summary - The **AI Engineering from Scratch curriculum** organizes content into **20 phases** and **511 lessons** with strict dependency chains visualized in [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) - Each lesson follows a **five-beat methodology** (MOTTO → PROBLEM → CONCEPT → BUILD IT → USE IT → SHIP IT) ensuring hands-on learning - **Three entry points** (placement tutor, MCP path, agent-skills path) generate personalized learning files ([`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md), [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md)) while respecting phase prerequisites - **Phase 19** contains capstone projects integrating prompts, skills, agents, and MCP servers into production systems - **Certification tracks** under `certifications/claude/` map lesson subsets to official exam objectives using identical infrastructure ## Frequently Asked Questions ### How many lessons are in the AI Engineering from Scratch curriculum? The curriculum contains **511 total lessons** distributed across 20 phases, as documented in [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) at lines 110-118. Each lesson includes runnable code in up to four languages, narrative documentation, and reusable artifacts. ### What is the difference between phases and lessons in the curriculum? **Phases** are broad topical blocks (20 total) that function as organizational containers following the `phases/-/` pattern, while **lessons** are individual learning units (511 total) containing specific implementations in subdirectories like `phases/01-math-foundations/01-linear-algebra-intuition/`. ### Can I skip phases if I already know the material? Yes. The curriculum supports **adaptive entry points** through the `start-learning` placement tutor skill, which generates a [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file recommending your optimal starting point. While you can skip ahead, the phase dependencies are designed to ensure you understand why higher-level concepts work, not just how to implement them. ### What are the five beats in each lesson? Every lesson follows the sequence: **MOTTO** (guiding principle), **PROBLEM** (specific challenge), **CONCEPT** (theory), **BUILD IT** (implementation), **USE IT** (application), and **SHIP IT** (deployment). This structure, visible in [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) lines 124-132, ensures a "build-it-then-use-it" learning loop that bridges theory and production code.