# How the AI Engineering from Scratch Curriculum Is Structured: A Complete Guide > Discover the AI Engineering from Scratch curriculum structure. Explore 20 phases and 511 lessons built on a five-beat learning loop for reusable AI artifacts. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: getting-started - Published: 2026-09-02 --- **The AI Engineering from Scratch curriculum is organized into 20 progressive phases containing 511 lessons, each following a consistent five-beat learning loop (MOTTO → PROBLEM → CONCEPT → BUILD IT → USE IT → SHIP IT) that produces reusable artifacts like prompts, skills, agents, and MCP servers.** The `rohitg00/ai-engineering-from-scratch` repository implements a comprehensive, hierarchical learning system designed to guide learners from foundational mathematics to production-ready AI engineering. Understanding the curriculum structure helps you navigate the 511 lessons efficiently, whether you follow the standard progressive path or enter through specialized tracks like the Model-Context-Protocol (MCP) or Agent-Skills paths. ## The Hierarchical Architecture: 20 Phases and 511 Lessons The curriculum stacks **20 phases** sequentially, starting with tooling and math foundations and culminating in capstone projects. Each phase acts as a broad topical block containing numbered lessons that build logical dependencies while allowing learners to skip ahead if they master lower layers. ### Phase Organization In [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) (lines 78-99), the phase map visualizes this progression using a Mermaid diagram. Phases range from `00-setup-and-tooling` through `19-capstone-projects`, with intermediate blocks covering `01-math-foundations`, `14-agent-engineering`, and `13-tools-and-protocols`. The directory structure follows the pattern: ```bash phases/-/ ``` For example, linear algebra fundamentals reside in `phases/01-math-foundations/01-linear-algebra-intuition/`, while MCP fundamentals appear in `phases/13-tools-and-protocols/06-mcp-fundamentals/`. ### Lesson Folder Structure Each of the **511 lessons** follows a rigid three-folder layout (lines 110-118 in [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md)): - **`code/`** – Runnable implementations in up to four languages - **`docs/`** – The narrative content ([`en.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/en.md) files explaining concepts) - **`outputs/`** – Generated artifacts and results Every lesson produces a **reusable artifact**—either a prompt, skill, agent, or MCP server—that you can deploy in production systems. ## The Five-Beat Learning Loop in Every Lesson Individual lessons in the AI Engineering from Scratch curriculum do not rely on passive reading. Instead, they enforce a **"build-it-then-use-it"** methodology through five distinct beats documented in [`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md) (lines 124-132): 1. **MOTTO** – The guiding principle or core insight 2. **PROBLEM** – The specific challenge or use case addressed 3. **CONCEPT** – The theoretical foundation required 4. **BUILD IT** – Hands-on implementation of the solution 5. **USE IT** – Practical application of what you built 6. **SHIP IT** – Deployment and production considerations This structure ensures that every lesson produces a tangible artifact rather than abstract knowledge. ## Three Entry Points: Personalized Learning Paths The repository offers **three distinct entry points** to accommodate different learning backgrounds, as defined in the `skills/` directory (lines 64-71, 122-133). ### The Placement Tutor (`start-learning`) Run the placement tutor to generate a personalized [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file that maps your existing skills to the optimal starting phase: ```bash # Add the skill using npx (requires a skill-capable host) npx skills add rohitg00/ai-engineering-from-scratch # Invoke the tutor learn start-learning ``` The skill definition resides in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md). ### The MCP Learning Path For engineers focused on Model-Context-Protocol implementation, the MCP path generates [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md) and guides you through relevant phases in `phases/13-tools-and-protocols/`: ```bash learn learn-mcp ``` Configuration exists in [`skills/learn-mcp/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn-mcp/SKILL.md). ### The Agent-Skills Path Targeting autonomous agent development, this path creates [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md) and prioritizes `phases/14-agent-engineering/` and related agent-building content: ```bash learn learn-agent-skills ``` Defined in [`skills/learn-agent-skills/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn-agent-skills/SKILL.md). ## Capstone Projects and Certification Tracks ### Phase 19: Integration and Production **Phase 19** (`phases/19-capstone-projects/`) aggregates end-to-end projects that combine artifacts from earlier phases—prompts, skills, agents, and MCP servers—into production-ready systems (lines 72-77). This phase validates your ability to integrate disparate components into cohesive AI engineering solutions. ### Claude Certification Alignment The `certifications/claude/` directory contains tracks that map specific subsets of lessons and diagnostics to official Claude certification exam objectives. These tracks reuse the same lesson infrastructure (code/, docs/, outputs/) but filter the 511 lessons to those relevant for certification prep (lines 74-82). ## Navigating the Repository To begin exploring the curriculum structure locally, clone the repository and run a sample lesson: ```bash # Clone the curriculum git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch # Run Phase 1, Lesson 1: Linear Algebra Intuition python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py ``` To experiment with MCP fundamentals: ```bash # Run the MCP lab from Phase 13 python3 phases/13-tools-and-protocols/06-mcp-fundamentals/code/run_mcp.py ``` Key structural files to bookmark: - **[`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md)** – High-level overview, phase map, and lesson table of contents - **[`phases/00-setup-and-tooling/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/00-setup-and-tooling/README.md)** – Environment setup and tooling verification - **[`phases/14-agent-engineering/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/14-agent-engineering/README.md)** – Core agent construction methodologies - **[`certifications/claude/README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/certifications/claude/README.md)** – Certification track onboarding ## Summary - The **AI Engineering from Scratch curriculum** organizes content into **20 phases** and **511 lessons** with strict folder conventions (`code/`, `docs/`, `outputs/`). - Every lesson follows a **six-step beat structure** (MOTTO → PROBLEM → CONCEPT → BUILD IT → USE IT → SHIP IT) ensuring practical artifact creation. - Learners enter via **three personalized paths**: the placement tutor (`start-learning`), the MCP protocol track, or the Agent-Skills track, each generating specific learning map files. - **Phase 19** contains capstone projects integrating earlier artifacts, while the `certifications/claude/` directory provides exam-aligned lesson subsets. - The repository structure allows skipping lower phases if you have prerequisite knowledge, while preserving logical dependencies that explain advanced concepts. ## Frequently Asked Questions ### How many lessons are in the AI Engineering from Scratch curriculum? The curriculum contains **511 lessons** distributed across 20 phases. Each lesson resides in a folder following the pattern `phases/-/-/` and includes code implementations, narrative documentation, and output artifacts. ### What are the three learning paths available in the repository? The repository offers entry through the **placement tutor** (`start-learning` skill), which diagnoses your level and creates a [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file; the **MCP path** (`learn-mcp`), targeting Model-Context-Protocol development; and the **Agent-Skills path** (`learn-agent-skills`), focusing on autonomous agent construction. Each path filters the 511 lessons to relevant subsets. ### What is the "five-beat" structure mentioned in the lessons? The five-beat structure refers to the pedagogical sequence in every lesson: **MOTTO** (guiding principle), **PROBLEM** (challenge definition), **CONCEPT** (theory), **BUILD IT** (implementation), **USE IT** (application), and **SHIP IT** (deployment). This ensures learners produce production-ready artifacts rather than just theoretical knowledge. ### Where are the capstone projects located? Capstone projects reside in **`phases/19-capstone-projects/`**. This phase aggregates end-to-end systems that combine prompts, skills, agents, and MCP servers built in earlier phases, serving as the integration layer before certification or production deployment.