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🧠 AI Engineering — RAG, MCP & AI Agents

From fundamentals to production deployment — complete guides, implementations, and interview preparation for AI engineering systems

📦 Modules

Module Description Key Topics
📚 RAG Retrieval-Augmented Generation Pipeline architecture, embeddings, retrieval strategies, production scaling, fine-tuning
🔌 MCP Model Context Protocol Protocol mechanics, server implementation, RAG integration, production deployment
🤖 Agents AI Agent Engineering Agent architecture, orchestration, tool-use loops, multi-agent systems, production guardrails
🏭 Production AI Production AI Engineering & Interview Prep RAG debugging, hallucination detection, cost optimization, latency debugging, enterprise agents, MCP security, multi-agent workflows
🔧 Harness & Loop Engineering Production Scaffolding & Agentic Loops Evaluation/agent harnesses, guardrails, sandboxing, prompt-injection containment, verification loops, ReAct patterns, loop safety, termination logic
🧬 LLM Internals How Claude and Claude Code Work Prefill/decode and KV cache, sampling, tokenization, Messages API request/response cycle, prompt caching, prompting, cost optimization

🏗️ Architecture Overview

┌────────────────────────────────────────────────────────────────────┐
│                        AI AGENT (Host)                              │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │  Planning → Memory → Tool-Use → Execution → Feedback        │ │
│  └──────────────────────────┬───────────────────────────────────┘ │
│                             │                                     │
└─────────────────────────────┼─────────────────────────────────────┘
                              │ MCP Protocol (JSON-RPC 2.0)
          ┌───────────────────┼────────────────────┐
          │                   │                    │
          ▼                   ▼                    ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│   RAG Pipeline   │ │   MCP Server     │ │   External APIs  │
│  (Knowledge)     │ │  (Tools/Actions) │ │  (3rd Party)     │
└──────────────────┘ └──────────────────┘ └──────────────────┘

🚀 Quick Start

# RAG — Index documents and query
cd rag/
pip install -r implementation/requirements.txt
python implementation/main.py --index --docs ./data/
python implementation/main.py --query "What is RAG?"

# MCP — Start servers
cd ../mcp/
python -m servers.calculator_server   # Test the calculator MCP server
python -m clients.python_client --server calculator --list  # List tools
  1. RAG Fundamentals → Understand knowledge retrieval architecture
  2. MCP Fundamentals → Learn protocol mechanics and primitives
  3. MCP Implementation & RAG → Build servers and connect RAG
  4. MCP Production Architecture → Enterprise security, deployment
  5. Agents → Orchestration, tool-use loops, production guardrails
  6. Production AI → Debugging, optimization, and enterprise architecture for production systems
  7. Harness Engineering & Loop Engineering → Production scaffolding, loop design
  8. Interview Questions → Prepare for Staff/Principal-level interviews