Claude AI Mastery

What you're asking for is essentially a complete Claude Mastery course. To do it properly, I'd structure it like a professional book rather than a short tutorial.

Proposed Course

Claude Mastery (2026 Edition)
Module 1 — Fundamentals
Introduction to Claude
Claude Ecosystem
Claude Models
Claude Pricing
Claude UI
Claude Projects
Claude Artifacts
Claude Memory
Claude Thinking
Claude Plans
Claude Settings
Module 2 — Prompt Engineering
Prompt Engineering Basics
Zero-shot Prompting
Few-shot Prompting
Chain of Thought
XML Prompting
Role Prompting
Persona Design
Output Formatting
Prompt Templates
Prompt Chaining
Self Reflection
Long Context Prompting
Context Compression
Prompt Debugging
Module 3 — Claude Code
Installing Claude Code
CLI Basics
Slash Commands
Context Management
File Operations
Git Integration
Multi-file Editing
Refactoring
Testing
Debugging
Large Projects
Custom Commands
Hooks
Settings
Best Practices
Module 4 — Claude API
API Overview
Authentication
Messages API
Streaming
Images
Vision
PDFs
Files
Rate Limits
Error Handling
Token Usage
Cost Optimization
Batch Processing
Production Deployment
Module 5 — MCP (Model Context Protocol)
Introduction
Internal Architecture
MCP Server
MCP Client
Resources
Prompts
Tools
Transport Layer
JSON RPC
Security
Authentication
Production Examples
Debugging MCP
Module 6 — Tool Calling
Function Calling
Tool Use
Multiple Tools
Web Search
Databases
APIs
Local Files
Automation
Agent Toolchains
Security
Module 7 — AI Agents
Agent Basics
Planning
Memory
Reflection
Multi-agent Systems
Agent Loops
Autonomous Coding
RAG
Vector Databases
Long-running Agents
Production Architecture
Module 8 — Advanced Claude
Extended Thinking
Context Window
Token Optimization
Performance
Scaling
Enterprise Usage
Monitoring
Logging
Cost Reduction
Reliability
Security
Future Features
Module 9 — Production Engineering
CI/CD
Docker
Kubernetes
AWS
Azure
GCP
GitHub Actions
Observability
Monitoring
Alerting
Disaster Recovery
High Availability
Module 10 — Interview Preparation
Beginner Questions
Intermediate Questions
Advanced Questions
System Design
Coding Round
Architecture Round
HR Round
Mock Interview
Module 11 — Projects
AI Chatbot
Coding Assistant
Research Assistant
Documentation Generator
Email Assistant
PDF Analyzer
MCP Server
AI IDE
AI Agent
Enterprise Assistant
Every chapter will include

Each Markdown file will consistently contain:

Learning objectives
Prerequisites
Theory
Internal working
Architecture explanation
ASCII diagrams
Flowcharts
Commands
CLI examples
API examples
Python examples
JavaScript examples
TypeScript examples
Bash examples
Real-world examples
Production examples
Best practices
Performance tips
Security notes
Common mistakes
Troubleshooting guide
FAQs
Interview questions
Hands-on labs
Mini project
Exercises
Quiz
Cheat sheet
Summary
Further reading
Revision notes
Glossary
Challenge problems
Pro tips
Hacks (safe, documented productivity techniques)
Workarounds (for common limitations or workflows)
Version differences (where applicable)
Deliverables

The complete course would include:

Around 130 Markdown (.md) files (one per chapter)
A main README.md
A table of contents with links
Mermaid diagrams and ASCII diagrams
Code examples in multiple languages
Practice labs and solutions
Production case studies
Interview question bank
Final capstone projects