Build real Android apps faster using AI tools like Cursor, Claude Sonnet, GPT, Copilot and Gemini in your daily workflow

What you'll learn

Develop an android app with the help of AI

Integrate AI as a feature to an Android app

Use cursor IDE to boost your productivity

Pick the right AI for the right task

Vibe coding

Requirements

Android development experience is nice to have but not a must: You will learn everything you need to know

Description

The AI-Assisted Android development by Petros Efthymiou.Learn how to leverage the best AI tools to build native Android apps really fast.AI is everywhere, your feed is full of posts about ChatGPT, Copilot, and how developers are 10x more productive.But when it's time to actually build an Android app using AI. you're on your own.Which tools should you use?How do you prompt effectively?How do you get AI to follow Clean Architecture?Can AI write Compose UI? Should it?Can you trust its code? How do you debug it?Most courses completely ignore this.They teach Android development the same way they did five years ago, as if AI doesn't exist.But the game has changed.This course is your roadmap to building Android apps with AI as your pair programmer—from day one, in real-world conditions.What You'll Build & LearnTogether, we'll build a real production-level Android app, powered by:Clean ArchitectureJetpack ComposeHILT for dependency injectionCoroutines & StateFlow for async state handlingRetrofit for networkingBut here's the twist:We won't just build it manually.We'll build it side-by-side with AI tools that accelerate your development process and act as your intelligent coding partners.You'll learn how to prompt like a pro, avoid common pitfalls, and truly collaborate with:CursorGitHub CopilotChat GPTClaudeGeminiAnd more.We'll even take things further and integrate generative AI as a feature inside our app—because the future of mobile development is not just building apps with AI, but building apps that use AI.Why Learn from Me?I'm Petros Efthymiou, a senior mobile engineer, author, and instructor with 11+ years of real-world experience in startups and multinational companies.I've trained 100K+ developers via Udemy, Amazon best-sellers, and live workshopsCreator of "Android TDD Masterclass", a top-rated Android Udemy courseAuthor of "Clean Mobile Architecture", a best-selling book that's helped thousands of devs level upCurrently working as Mobile Trainer at Backbase, training:Internal R&D engineersProfessional services teamsThird-party developersOver the past 3 years, I've embedded AI tools into my daily workflow, building real products and discovering what truly works—and what doesn't.This course distills all that experience into a step-by-step, production-focused learning path so you can build faster, smarter, and more confidently with AI.Why is it important?Because the way we write software is fundamentally shifting.Developers who know how to collaborate with AI tools will build faster, ship smarter, and outpace those who don't.This isn't about replacing developers. It's about amplifying them.You'll still need architectural thinking, design skills and debugging abilities but AI helps you:Write code faster without skipping best practicesOffload boilerplate and focus on the hard problemsCatch edge cases early by asking better questionsUse AI not just to code, but to think alongside youSoon, AI-assisted development will be the norm.The sooner you master it, the further ahead you'll be—both technically and professionally.This course is here to get you there.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure

Lecture 3 AI Productivity boost

Lecture 4 AI Power demonstration

Section 2: Introduction to AI and AI Tooling

Lecture 5 Section Intro

Lecture 6 What is AI?

Lecture 7 Generative AI

Lecture 8 How LLMs work

Lecture 9 AI Capabilities & Limitations

Lecture 10 When to trust AI and when not to

Lecture 11 AI Tooling

Lecture 12 Tooling installation

Section 3: Prompt Engineering 101

Lecture 13 Section Intro

Lecture 14 How to be Effective with AI

Lecture 15 Prompting Types

Lecture 16 RTF Prompting Framework

Lecture 17 C.O.D.E Prompting Framework

Lecture 18 Prompt Debugging and Refinement

Lecture 19 Prompting Do's and Dont's

Lecture 20 Common coding prompts

Lecture 21 Improving prompts

Section 4: AI-Powered Coding Workflow: From Idea to Production App with AI

Lecture 22 Section Intro

Lecture 23 Cursor Walkthrough

Lecture 24 Cursor Rules

Lecture 25 Initialize Project

Lecture 26 How to make Architectural decisions

Lecture 27 Architecture Prompt

Lecture 28 Architecture Prompt (Cont)

Lecture 29 Application Architecture

Lecture 30 Domain Layer

Lecture 31 Architecture Summarize

Lecture 32 Architecture Prompt

Lecture 33 Architecture Prompt (cont)

Lecture 34 Packaging prompt

Lecture 35 Tech stack prompt

Lecture 36 Tech stack prompt (cont)

Lecture 37 Commit after testing

Lecture 38 Backend API Walkthrough

Lecture 39 Data Layer prompt

Lecture 40 Data Layer clear up

Lecture 41 Picking the right AI to debug

Lecture 42 Presentation Layer prompt

Lecture 43 Presentation Layer implementation

Lecture 44 Consulting on third party libraries

Lecture 45 Articles Screen prompt

Lecture 46 Articles Screen implementation

Lecture 47 Articles Feature troubleshooting

Lecture 48 Improved error handling

Lecture 49 Adding complex Presentation logic

Lecture 50 Refactoring with AI prompt

Lecture 51 Refactoring with AI result

Section 5: How to Keep the AI Focused: Context

Lecture 52 Section Intro

Lecture 53 Types of Context

Lecture 54 When to switch Context

Lecture 55 Do we need to switch Context now?

Lecture 56 Summarize current State prompt

Lecture 57 Gathering important Session Context

Lecture 58 Cursor Project Rules

Lecture 59 Token Optimization

Lecture 60 Rules Logical Segregation

Lecture 61 Segregating Rules to Layers

Lecture 62 Automatic Context Inclusion

Lecture 63 Bottom Navigation prompt

Lecture 64 Bottom Navigation implementation

Lecture 65 Sources Feature prompt

Lecture 66 Sources Feature implementation

Lecture 67 Sources Feature implementation (Cont)

Lecture 68 Fixing and Testing the Sources Feature

Lecture 69 Updating the Rules based on the session Learnings

Section 6: Making Your App AI-Powered: Build Your First Smart Feature

Lecture 70 Section Intro

Lecture 71 AI Feature requirements

Lecture 72 Steps to integrate the GPT model

Lecture 73 GPT API key

Lecture 74 AI Architecture

Lecture 75 How to implement the GPT integration

Lecture 76 AI Feature Data Layer implementation

Lecture 77 Repository implementation

Lecture 78 Constructing multiple Retrofit instances

Lecture 79 UI & Presentation Layers prompt

Lecture 80 UI & Presentation Layers implementation

Lecture 81 Testing the AI feature

Lecture 82 Trying out other Generative Models

Lecture 83 Adding a loader to the FAB

Lecture 84 Auth Feature prompt

Lecture 85 Auth Screens implementation

Lecture 86 Fine tuning the Auth Screens

Lecture 87 Congratulations

Lecture 88 Bonus Lecture

Android developers,People interested in Android development,Devs interested in AI-assisted development,Devs interested in vibe coding,People who want to build a mobile product