Curriculum Progress Tracker

Use this page to track your progress through the AI Engineering Hub curriculum. Check off lessons as you complete them!

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Part 1: Understand AI

01. GenAI Foundations

• [ ] Prerequisites
• [ ] NLP to Transformers

02. AI Engineering Essentials

• [ ] API Keys & Setup
• [ ] Tokenization & Context Windows
• [ ] Prompt Engineering Essentials

03. Neural Networks & Deep Learning

• [ ] Perceptrons to MLPs
• [ ] Backpropagation Calculus

04. Transformers & Attention

• [ ] Attention Math
• [ ] Multi-Head Attention
• [ ] Complete Transformer Architecture

05. Large Language Models

• [ ] Pre-training & Tokenization
• [ ] Tokenization Internals

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Part 2: Build Applications

06. RAG — Retrieval Augmented Generation

• [ ] Introduction to RAG Systems
• [ ] Vector Databases
• [ ] Chunking Strategies
• [ ] Hybrid Search
• [ ] Graph RAG

07. AI Agents

• [ ] Introduction to Agents
• [ ] ReAct Pattern
• [ ] Planning & Reasoning
• [ ] Building an Agent from Scratch
• [ ] Agent Frameworks

08. Agent Harness & Runtime

• [ ] Harness Engineering
• [ ] Model Context Protocol (MCP)

09. Multi-Agent Systems

• [ ] Orchestrator-Worker Pattern
• [ ] Supervisor & Router Patterns

10. Vector Databases Deep Dive

• [ ] Indexing & Schemas

11. Prompt Engineering Mastery

• [ ] Structured Outputs & Pydantic

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Part 3: Production

12. LLMOps & Production Systems

• [ ] Model Deployment Patterns
• [ ] Local LLM Inference & Serving

13. LLM Evaluation & Quality

• [ ] Why LLM Evals Matter
• [ ] LLM-as-a-Judge
• [ ] Agent Trajectory Evals

14. AI Safety & Ethics

• [ ] Foundations of AI Safety
• [ ] Prompt Injection Security
• [ ] Red Teaming & Adversarial Testing

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Part 4: Advanced & Hands-On

15. Fine-Tuning & Custom Models

• [ ] When and Why to Fine-Tune
• [ ] LoRA and PEFT

16. Capstone & Portfolio Projects

• [ ] Project 1: Enterprise RAG Assistant
• [ ] Project 2: Autonomous Agent Platform