AIAI-Native Engineering

Claude CodeMCPAgentic DevAI-Native

Learn AI-native engineering to build software with AI as your core collaborator. From Claude Code and MCP servers to agentic workflows and team practices.

Free Tutorial

Learn AI-Native Engineering - Claude Code, MCP & Agentic Development

Master AI-native engineering from fundamentals to advanced agentic workflows. Learn to leverage Claude Code, build MCP servers, adopt multi-model strategies, and transform your development process with AI-powered tools and practices.

Prerequisites

Before learning AI-native engineering, you should have solid programming fundamentals and be comfortable with JavaScript/TypeScript and Node.js development.

JavaScript Tutorial Node.js Tutorial

What You'll Learn

✓ AI-native development fundamentals
✓ Claude Code mastery
✓ Writing effective CLAUDE.md
✓ MCP server development
✓ Multi-model strategies
✓ AI-augmented code review
✓ Testing with AI
✓ Large-scale refactoring
✓ Security best practices
✓ AI-native team workflows

Course Topics

Lesson 1

Beginner

15 min

What is AI-Native Engineering?

Understand the paradigm shift from traditional development to AI-native engineering, the 3 phases of AI coding, and why this changes everything about how software gets built

Getting Started with Claude Code

Install and configure Claude Code, Anthropic's agentic coding CLI that reads your codebase, plans changes, and executes multi-file edits autonomously

Core Claude Code Workflows

Master the daily workflows that 10x your productivity: bug fixing, feature implementation, refactoring, code review, test writing, and documentation with Claude Code

Writing Effective CLAUDE.md Files

Master the art of writing CLAUDE.md files that transform AI output quality by giving Claude deep context about your project architecture, conventions, and requirements

Prompt Engineering for Code Generation

Learn the specific techniques for writing prompts that produce high-quality, production-ready code — from vague instructions to precise, effective directives

Agentic Development Patterns

Master the patterns of agentic development: autonomous planning and execution, parallel agents, background agents, and the trust-but-verify workflow

Model Context Protocol (MCP) Deep Dive

Understand MCP — the open protocol that connects AI models to external tools, databases, and services — and learn to build custom MCP servers

Cursor and AI-Native IDEs

Compare and master the major AI-native IDEs including Cursor, GitHub Copilot, Windsurf, and understand when to use each tool for maximum productivity

Multi-Model Strategy for Engineers

Learn when and how to use different AI models — Claude, GPT-4, Gemini, and local models — for different engineering tasks to maximize quality and minimize cost

AI-Augmented Code Review

Learn to use AI as a first-pass code reviewer that catches bugs, security issues, and style violations — while understanding what AI reviews miss

AI-Powered Testing Strategies

Learn to use AI to generate comprehensive test suites, identify untested code paths, create edge case tests, and maintain test quality at scale

Large-Scale Refactoring with AI

Learn how to use AI for large-scale refactoring — migrating JavaScript to TypeScript, extracting components, renaming across codebases, and executing the strangler fig pattern

Debugging with AI Assistants

Master the art of describing bugs to AI, using AI to trace complex code paths, analyze logs, and solve problems that would take hours of manual debugging

Architecture Decisions in the AI Era

How AI changes architectural decisions — why simpler architectures win, premature abstraction is more costly, and the monolith-first approach makes more sense than ever

Documentation and Knowledge Management with AI

Use AI to generate, maintain, and evolve documentation — from README files and API docs to Architecture Decision Records and code comments

AI-Native Git Workflows

Transform your git workflow with AI — from commit message generation and PR descriptions to merge conflict resolution and branch management strategies

Security in AI-Native Development

Understand and mitigate the security risks of AI-native development — from secret leakage and vulnerable code generation to supply chain risks and sandboxing

AI Pair Programming Best Practices

Master the human-AI pair programming dynamic — when to lead, when to follow, how to maintain flow state, and the daily rhythm of an AI-native engineer

Managing AI-Generated Code Quality

Learn to maintain code quality when AI writes most of the code — ownership principles, review checklists, common AI code smells, and consistency practices

Building Internal AI Tools and Agents

Learn when and how to build custom AI tools for your team — from Slack bots that answer codebase questions to domain-specific code generators

AI-Native Team Practices

Build an AI-native engineering culture — from shared CLAUDE.md standards and AI tool policies to hiring for AI literacy and measuring team AI maturity

Cost and Performance Optimization

Understand AI API pricing, optimize token usage, implement caching strategies, and make informed decisions about when to use expensive vs cheap models

AI in CI/CD Pipelines

Integrate AI into your CI/CD pipelines for automated code review, intelligent test generation, build failure analysis, and AI-powered deployment decisions

The Future of AI-Native Engineering

Where AI-native development is heading — autonomous agents, the product engineer role, skills that will matter in 3-5 years, and how to stay relevant

Career Guide: Becoming an AI-Native Engineer

A complete career roadmap for AI-native engineering — skills assessment, 90-day learning plan, portfolio projects, interview preparation, and career progression

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Frequently Asked Questions

What is AI-native engineering?

AI-native engineering is a development approach where AI tools like Claude Code are integral collaborators in the software development process, not just assistants. Engineers direct and review AI-generated code rather than writing every line manually.

Do I need to know how to code to be an AI-native engineer?

Yes. AI-native engineering amplifies coding skills, it doesn't replace them. You need strong fundamentals to effectively direct AI tools, review generated code for correctness and security, and make architectural decisions.

What is Claude Code?

Claude Code is Anthropic's agentic CLI tool that can read your entire codebase, plan changes, write code across multiple files, run tests, and iterate autonomously. It operates in your terminal and integrates with your existing development workflow.

What is MCP (Model Context Protocol)?

MCP is an open standard by Anthropic that lets AI tools connect to external data sources and tools through a standardized client-server protocol. Think of it as "USB-C for AI" — one protocol to connect any AI to any tool.

Ready to Learn AI-Native Engineering?

Begin your AI-native engineering journey with the fundamentals. You'll learn what AI-native development is, why it matters, and how to set up your environment for AI-powered workflows.

Start Learning AI-Native Engineering →