Claude API Tutorial: From Zero to Hero 🚀

Welcome to the most comprehensive hands-on tutorial for mastering the Claude API ecosystem! This tutorial takes you from your first API call to building production-ready AI agents and applications.

What You'll Learn

This tutorial covers everything you need to become proficient with Claude:

• Claude API Fundamentals - Make your first calls and understand core concepts
• Token Optimization - Minimize costs while maximizing performance
• Advanced Features - Vision API, function calling, and structured outputs
• RAG Systems - Build knowledge-intensive applications with retrieval
• MCP (Model Context Protocol) - Create custom tool integrations
• Claude Code CLI - AI-assisted development workflows
• AI Agents - Build autonomous systems that solve complex tasks
• Production Deployment - Security, monitoring, and best practices

Who This Is For

• Developers wanting to integrate Claude into applications
• AI Engineers building intelligent systems
• Product Managers understanding AI capabilities
• Researchers exploring LLM applications
• Anyone curious about practical AI development

Tutorial Structure

This is a hands-on, project-based tutorial. Each module includes:

• Clear learning objectives
• Conceptual explanations
• Working code examples
• Practical exercises
• Real-world projects

Total Time: 8-12 hours (self-paced)

Quick Start

1. Prerequisites

• Python 3.9+ or Node.js 18+
• Basic programming knowledge
• Anthropic API key (get one here)

2. Setup

Windows 11 Users: Start with Module 0: WSL2 Setup for complete WSL2, Linux, and Python configuration.

macOS/Linux Users:

# Clone the repository
git clone https://github.com/yourusername/LLM-API-tutorial.git
cd LLM-API-tutorial

# Follow the detailed setup guide
cat SETUP.md

3. Choose Your Path

• Fast Track (4-6 hours): Core skills - API basics, optimization, RAG, and agents
• Complete Course (8-12 hours): All modules including MCP, Claude Code, and production
• Specialization Tracks: Focus on RAG, agents, or dev tools

See TUTORIAL_PLAN.md for detailed curriculum.

Module Overview

Module 0: WSL2 Setup & Python for LLMs

Windows 11 users start here! Complete guide to setting up WSL2, Linux essentials, Python environment, and CLI tools for LLM development.

Module 1: Foundation

Get started with your first Claude API calls and understand fundamental concepts.

Module 2: Token Optimization

Learn to minimize costs with prompt engineering, caching, and streaming.

Module 3: Advanced Features

Master vision API, function calling, and multi-modal applications.

Module 4: RAG Systems

Build retrieval-augmented generation for knowledge-intensive tasks.

Module 5: MCP Integration

Create custom Model Context Protocol servers for tool integration.

Module 6: Claude Code CLI

AI-assisted development workflows and automation.

Module 7: AI Agents

Build autonomous agents that reason, plan, and execute complex tasks.

Module 8: Production Best Practices

Deploy secure, scalable, monitored AI applications.

Module 9: Capstone Project

Apply everything you've learned in a real-world project.

Learning Paths

Path A: RAG & Knowledge Systems

Perfect for building intelligent search and Q&A systems.

Modules: 1 → 2 → 4 → 8

Path B: Agent Development

Master autonomous AI systems and complex task automation.

Modules: 1 → 2 → 3 → 7 → 8

Path C: Dev Tools & Automation

Build AI-powered development tools and workflows.

Modules: 1 → 2 → 5 → 6 → 8

Repository Structure

LLM-API-tutorial/
├── 00-wsl2-setup/          # Windows 11 users start here!
├── 01-basic-api/           # Basic API usage
├── 02-optimization/        # Cost and performance
├── 03-advanced-features/   # Vision, tools, functions
├── 04-rag/                 # Retrieval systems
├── 05-mcp/                 # Protocol integration
├── 06-claude-code/         # CLI workflows
├── 07-agents/              # Autonomous systems
├── 08-production/          # Deployment & ops
└── 09-capstone/            # Final project

Each module contains:

• README.md - Module overview and concepts
• exercises.md - Hands-on challenges
• Working code examples
• Solution guides

What Makes This Tutorial Different?

✅ Hands-on & Practical - Every concept includes working code ✅ Production-Ready - Real patterns you'll use in production ✅ Cost-Conscious - Token optimization throughout ✅ Up-to-Date - Latest features and best practices ✅ Complete Ecosystem - API, MCP, Claude Code, agents ✅ Progressive - From beginner to advanced ✅ Self-Paced - Learn at your own speed

Prerequisites

Required Knowledge:

• Basic Python or JavaScript
• Command line basics
• Understanding of APIs and HTTP

Software Needed:

• Python 3.9+ or Node.js 18+
• Code editor (VS Code recommended)
• Git
• Anthropic API key

Optional but Helpful:

• Docker (for deployment modules)
• Basic understanding of machine learning concepts
• Experience with async programming

Getting Help

• Issues: Open a GitHub issue
• Discussions: Join the community discussions
• Documentation: Check Anthropic docs
• FAQ: See each module's README

Cost Considerations

This tutorial is designed to be affordable:

• Most exercises run on Claude Haiku (lowest cost)
• Token optimization emphasized throughout
• Estimated total cost: $2-5 for all exercises
• Tips for using free tier credits

See SETUP.md for budget planning.

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

• Report bugs or issues
• Suggest improvements
• Add exercises or examples
• Share your capstone projects

License

MIT License - see LICENSE for details.

Acknowledgments

Built with guidance from:

• Anthropic documentation and examples
• Community feedback and contributions
• Real-world production experiences

Next Steps

1. Read SETUP.md to configure your environment
2. Review TUTORIAL_PLAN.md for the full curriculum
3. Start with Module 1: Foundation
4. Join our community and share your progress!

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Ready to become a Claude API expert? Let's start building! 🚀

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