2. What skills will I gain from AI-native development training?

You can develop skills in AI-assisted product planning, natural-language coding, code generation, refactoring, application development, testing, and quality engineering. The course also covers coding agents, multi-step tasks, security practices, version control, and AI-native workflow design.

3. How is AI-native development different from traditional software development?

Traditional development workflows generally rely primarily on human-written code and established development tools, while AI-native development incorporates AI throughout activities such as planning, coding, testing, debugging, and refinement. The course examines how developers can combine AI capabilities with human review and engineering controls.

4. Does the course cover AI coding agents?

Yes. Learners explore coding agent concepts, agent-based development tasks, task delegation, context management, reviewing agent-generated changes, and coordinating multi-step development. The curriculum also addresses how coding agents can fit into broader development workflows.

5. What projects are included in the AI-native development course?

Projects include building an AI-native web application, creating an AI-powered feature, developing with coding agents, and testing and refining an application. Learners also deploy an AI-native product and create a repeatable AI-native development workflow.

AI-Native Development Course: Build with AI

Category IT & Software

Level Intermediate

Students 14 Already Enrolled

Review

(4.6)

AI-native development represents a modern approach to building software where artificial intelligence is integrated throughout product planning, coding, testing, and delivery. This course introduces the principles, tools, and workflows that help teams use AI as an active part of the software development process.

Learners explore AI-assisted product planning, requirements, architecture, code generation, natural-language iteration, refactoring, interfaces, backend services, databases, APIs, and AI-powered features. The curriculum also covers coding agents, task delegation, agent context, multi-step development, automated testing, debugging, vulnerability detection, and quality engineering.

Security and engineering practices remain central throughout the course, including secrets protection, sensitive data management, dependencies, licensing, version control, and human oversight. Through structured projects, learners build AI-native applications, develop AI-powered features, work with coding agents, test and refine software, and create repeatable AI-native development workflows.

AI-native development principles, tools, workflows, and engineering practices
AI-assisted product planning, requirements, architecture, and feature design
Natural-language coding, code generation, refactoring, and multi-file development
Application interfaces, backend services, databases, APIs, and AI features
AI coding agents, task delegation, context management, and coordination
Testing, security, quality engineering, deployment, and AI-native workflows

Basic familiarity with software development, programming, or digital technology is recommended. Previous experience with AI coding tools can be helpful but is not required to explore the core principles and workflows.

This course is designed for software developers, product engineers, technical founders, engineering teams, aspiring developers, and technology professionals interested in AI-native software development. It is also suitable for professionals seeking to incorporate AI tools and coding agents throughout the development lifecycle.

1.1 Understanding AI-Native Development
1.2 AI-Native Versus Traditional Development
1.3 Generative AI in Software Development
1.4 AI Coding Tools and Platforms
1.5 AI Development Capabilities and Limitations
1.6 Building an AI-Native Mindset

2.1 Defining Product Ideas with AI
2.2 Identifying User Problems
2.3 Creating Product Requirements
2.4 Designing Features with AI
2.5 Planning Application Architecture
2.6 Building AI-Assisted Development Plans

3.1 Writing Effective Coding Instructions
3.2 Generating Code with AI
3.3 Understanding AI-Generated Code
3.4 Iterating Through Natural Language
3.5 Refactoring with AI
3.6 Managing Multi-File Development Tasks

4.1 Creating Application Structures
4.2 Developing User Interfaces
4.3 Building Backend Services
4.4 Connecting Databases
4.5 Integrating External APIs
4.6 Adding AI-Powered Features

5.1 Understanding AI Coding Agents
5.2 Planning Agent-Based Development Tasks
5.3 Delegating Tasks to Coding Agents
5.4 Managing Agent Context
5.5 Reviewing Agent-Generated Changes
5.6 Coordinating Multi-Step Development

6.1 Generating Tests with AI
6.2 Debugging Through Natural Language
6.3 Reviewing AI-Generated Code
6.4 Detecting Software Vulnerabilities
6.5 Improving Code Quality
6.6 Automating Quality Checks

7.1 Protecting Application Secrets
7.2 Managing Sensitive Development Data
7.3 Applying Secure Coding Practices
7.4 Managing Dependencies and Licenses
7.5 Using Version Control with AI
7.6 Maintaining Human Oversight

8.1 Building an AI-Native Web Application
8.2 Creating an AI-Powered Feature
8.3 Developing with Coding Agents
8.4 Testing and Refining the Application
8.5 Deploying an AI-Native Product
8.6 Creating an AI-Native Development Workflow

An AI-native development course is suitable for software developers, product engineers, technical founders, engineering teams, aspiring developers, and technology professionals. It can help learners explore development workflows where AI plays an active role throughout the software lifecycle.

AI-Native Development Course: Build with AI

About AI-Native Development Course: Build with AI

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