AI-assisted Android App Development - Gen AI (Vibe Coding)

Explore how AI and large language models work, and discover the best tools for coding and mobile development in this introductory section.

Define artificial intelligence as a field building systems that perform tasks requiring human intelligence; narrow AI handles tasks like predicting next word and code completion, while AI remains science fiction.

Explore how large language models work by predicting the next token with probabilistic neural networks, like autocomplete, trained on enormous datasets, and they lack true reasoning.

AI models are powerful, yet not infallible; they excel at summarizing bugs, explaining code, and scaffolding, while hallucinations and fabricated APIs remind engineers to stay in control.

Master ai tooling by comparing llms like Claude Sonnet and gpt, selecting between web apps and ide-based tools, and using the cursor with Claude Sonnet for coding.

Master prompting strategies to extract precise information from the LMS by learning prominent prompting techniques, proper prompt construction, and common pitfalls to avoid for optimal results.

Learn to be effective with AI by mastering prompts, supplying rich context, and selecting the right tools to optimize time and cost while boosting productivity.

Explore three prompting types—zero shot, few shot, and chain of thought—and learn how to craft prompts for Jetpack Compose login screens and step by step code refactoring.

Debug prompts to curb hallucinations by clarifying context, using official Android APIs, and grounding with real examples; specify output format and iterate to return only a Kotlin function.

Be specific to avoid vague outputs, define a role at the session start, and specify the expected format with examples. Test and iterate while breaking tasks into simple steps.

Learn to craft effective coding prompts with concise sentence answers and clear prompt structures. Manage verbosity across LMS, favor simple implementations, and use reasoning prompts to handle complex requests.

Learn to craft clear, structured prompts for Android development with Kotlin and Jetpack Compose, applying role-based prompts, specific outputs, and concrete examples to improve prompt success.

Begin coding in the third section of ai-assisted android app development by applying AI tools and prompting techniques to produce tangible results.

Learn to use cursor, an AI-powered IDE, to write code with agent mode, ask mode for questions, and manage project context for Android app development.

Configure cursor settings and model pools, including max mode, to tailor prompts for Android development; learn to apply user rules with build commands like Gradle w assemble debug.

Initialize a native Android app with Android Studio, set up an empty activity, configure min sdk 26, and scaffold the project using Kotlin DSL for Gradle, while avoiding AI hallucinations.

Design the MVP’s architecture first, balancing AI guidance with human decisions to clearly communicate system intent. Collaborate with AI tools and a human architect to decide the right mobile architecture.

Guide an MVP native Android app architecture by assuming a senior Android developer persona, define features to fetch articles and sources from APIs, and summarize articles.

Explore clean architecture for Android apps, with presentation, domain, and data layers, MVVM and MVI, repository pattern, and dependency injection, plus a feature-based streaming architecture for scalability.

Evaluate when a domain layer adds value in a MVP Android app, highlighting transformations and business logic in use cases vs simple delegation, with repository interfaces for dependency inversion.

Explore a simple three-layer Android architecture (UI, presentation, data) with a repository-driven MVP, view models and state flows, plus feature-based packaging endorsed by screaming architecture for clarity.

Architect the app inside the cursor context with careful prompts, adopt feature-based packaging (articles with UI, presentation, data), and use a single source of truth via repository implementations.

Learn to package a prompt to scaffold package structure for the articles feature in an ai-assisted android app, exploring prompt design, context optimization, and iterative command execution with a cursor.

Commit after every change you build and run in Android Studio to quickly locate where derailed code began, keep many small commits, and ensure the git repository is initialized.

Walk through the backend API for the AI-assisted android app development, fetch US business headlines via the News API, and securely manage and reference the API key in Gradle properties.

Build the article data layer with models, API interface, repository, and a dependency-injection network module using Moshi and Retrofit; parse date and image URL with adapters.

Choose the right AI tool to debug a build error, fix the import build config, and optimize pricing by comparing cursor, Gemini, and cloud for debugging strategies.

Implement the presentation layer by building an article view model that maps repository data to article models and exposes a state flow with loading, success, and error states.

Explain how the article data class represents the presentation model and how the view model uses state flow to expose a single UI state for loading, success, and error.

Learn how to build a Jetpack Compose article screen with an image loaded from the web and choose a third-party library like coil guided by Gemini.

Implement the articles screen in an Android app with Hilt dependency injection, adding the application class, manifest entry, and Coil for image loading, then display articles in a lazy column.

Troubleshoot the articles feature in android app development by syncing gradle and enabling hilt and ksp, then make article description and image url nullable and add internet permission.

Improve error handling in the article view model by displaying a user-friendly message. Log the technical error to Logcat with timber for debugging.

Transform article dates into a user-friendly presentation using a view model that outputs today, yesterday, or days ago, and validate the logic with real and mocked data.

Refactor the articles feature by moving presentation logic from the article view model into the article model, using a small, focused prompt to guide the change.

Refactor the article feature with AI by moving presentation logic into the article model, review and test the app, and commit changes while learning how prompts and AI influence results.

Learn about three context types—short-term memory, long-term memory, and environment context—and how switching chats or sessions affects retention across AI tools, emphasizing tool-agnostic memory concepts.

Master context switching by recognizing memory limits and keeping context healthy while passing critical information to new chats. Start new prompts when hallucinations occur or tasks change.

Master switching cursor chats while preserving essential context, carrying architecture details across tasks as you implement a sources feature with CRUD operations using UI, view model, repository, and API.

Locate and gather the most important session context and prompts, copy them to a text file, and organize by project objective, architecture, and technology stack for future sessions.

Define and apply cursor rules to manage context, using text or md files, describe how to feed LMS context to prompts, and implement an application overview rule that always applies.

Explore token optimization strategies to balance response time and cost when using AI tools like Cursor and GPT, including per-token pricing, context management, and splitting context into targeted files.

Learn to segregate project rules by context and type, creating architecture, technology stack, overview, and state rules to support refactoring and LMS updates.

Segregate and apply layer-specific rules for UI, data, and presentation layers, organizing libraries and compatibility guidance to ensure clean, targeted rule sets.

Implement bottom navigation with two items (articles and sources) using Jetpack Compose. Update the main activity to host the navigation and set the articles as the start destination.

Implement the sources feature across data, presentation, and user interface layers. Define the source model, API, and repository, and wire the view model with loading states and error messages.

Capture sprint learnings to update the rules, implement a single network module that provides all apis to the dependency graph, and source api keys from the build config, then commit.

Demonstrate an AI feature using a floating action button to send article titles to GPT, generate a daily summary, and determine investing sentiment (positive, negative, or neutral) with a disclaimer.

Learn to integrate the OpenAI GPT model into an Android app by obtaining an API key, selecting a model, and calling the chat completions endpoint with system and user messages.

Learn to integrate the GPT model into an Android app by obtaining an OpenAI API key or mocking the API for testing.

Define a layered architecture that keeps control, introducing a GPT API and an article API, with a dedicated AI repository to orchestrate requests under the single responsibility principle.

Learn to implement GPT integration in an Android app, format API responses as JSON, review response structure and pricing, while mocking the API and wiring eye repository with GPT API.

Implement the AI repository class with dependency injection for the GPT API, providing a suspend function to generate an article summary and investment sentiment from article titles.

Learn to configure two Retrofit instances for different domains by using custom qualifiers and dependency injection, enabling integration of a news API and the OpenAI GPT API.

Test the AI feature by building and running the app, fix inputs. The bottom modal shows AI-generated market summaries with neutral sentiment, powered by a ChatGPT data layer.

Learn to add a loader inside the fab for AI summaries, update the article UI state and view model, and display a model sheet with the summary on success.

Implement the login and signup user interface screens and their navigation in the Android app using prompts, with screenshots, without backend integration.

Implement login and sign up screens with Jetpack Compose, configure NavHost routes with login as the start destination, and validate the UI flow from login to main app.

Fine-tune the auth screens by aligning outline text fields, spacing, and graphics to the screenshots, ensure back navigation on sign up, and test login and sign-up flows in Android Studio.

Finish the ai-assisted android app development course and learn to use ai tools, craft effective prompts, build a full android app with ai-written code, and integrate a GPT model.

Master clean mobile architecture in an AI-driven world and explore books, courses, and resources to stay connected via LinkedIn and YouTube with Petros Efthymiou.