AI Engineering Patterns

Master MCP, CrewAI, Agentic AI, RAG, and Context Engineering for Production Systems

Comprehensive Coverage
Production Ready
Interactive Learning

Learning Path Overview

Complete journey through AI engineering patterns

Modern AI Engineering Landscape

Understanding the interconnected ecosystem of AI engineering tools and patterns

Learning Path: Foundation to Advanced

1

Start with MCP

Learn the foundational protocol that enables AI models to securely connect to external tools and data sources

2

Build RAG Systems

Enhance AI models with dynamic knowledge retrieval using vector databases and semantic search

3

Implement Agentic AI

Create autonomous AI systems that can plan, execute, and adapt their strategies

4

Orchestrate with CrewAI

Coordinate multiple AI agents working together on complex, multi-step tasks

5

Master Context Engineering

Optimize prompt design and context management for reliable, production-ready outputs

Why This Order?

Foundation First

MCP provides the communication layer everything else builds on

Data Integration

RAG adds dynamic knowledge before complex reasoning

Single Agent Logic

Master individual agents before orchestrating teams

Team Coordination

CrewAI orchestrates multiple agents effectively

Optimization

Context engineering optimizes everything

Production Stack

Context Engineering

Optimization Layer

CrewAI

Orchestration Layer

Agentic AI

Intelligence Layer

RAG

Knowledge Layer

MCP

Protocol Layer