How nalini-krishna/ai-engineering-from-scratch works
This repository is an open AI-engineering curriculum with a companion website and certification tracks. The principal learner workflow is to choose a learning route, study a lesson, run its code, and keep evidence of the result. The graph reflects the README’s documented learning flow and major curriculum branches; API and frontend wiring is limited because their implementation was not sampled.
Main entry points: app.js Curriculum Site · 11-llm-engineering LLM Engineering · 01-math-foundations Foundations · 02-ml-fundamentals ML Foundations
Components 17
Curriculum
- Foundations
phases/01-math-foundations - ML Foundations
phases/02-ml-fundamentals - Deep AI
phases/03-deep-learning-core - LLM Engineering
phases/11-llm-engineering - Agents
phases/14-agent-engineering - Tools Protocols
phases/13-tools-and-protocols
Website
- Curriculum Site
site/app.js - Lesson API
api/lesson.js - Certification API
api/certification.js - Markdown API
api/markdown.js - Markdown v1 API
api/v1/markdown.js
Certifications
- Claude Track
certifications/claude - MCPA Track
certifications/mcpa
Applied Labs
- Capstones
phases/19-capstone-projects - Runnable Lesson Code
phases/11-llm-engineering/01-prompt-engineering/code/prompt_engineering.py - RAG System Lab
phases/19-capstone-projects/69-end-to-end-rag-system/code/main.py
Other components
- Learner
Connections 16
- Learner → Curriculum Site: browses curriculum
- Learner → Foundations: studies lessons
- Learner → Claude Track: prepares for
- Learner → MCPA Track: prepares for
- Curriculum Site → Lesson API: lesson requests
- Curriculum Site → Certification API: certification requests
- Curriculum Site → Foundations: presents curriculum
- Foundations → ML Foundations: continues learning
- ML Foundations → Deep AI: continues learning
- Deep AI → LLM Engineering: continues learning
- LLM Engineering → Agents: continues learning
- LLM Engineering → Tools Protocols: continues learning
- Learner → Runnable Lesson Code: runs lesson code
- Learner → RAG System Lab: builds capstone
- Lesson API → Markdown API: uses markdown
- Markdown v1 API → Markdown API: versioned access