AI-Powered Android Apps with GPT-3

Create your OpenAI API account by signing up with Google or other options, verify your phone number, and review the platform overview with $5 free credits for three months.

Explore the OpenAI API documentation on platform.openai.com to learn GPT-3 concepts, completions, best practices, and practical use cases like grammar correction, emoji translation, and natural language to SQL.

Explore the OpenAI playground to experiment with prompts, temperature, and maximum length. Discover how prompt engineering shapes GPT-3 responses, from emotion detection to humor, and monitor usage for backend work.

Create a next.js project with TypeScript and ESLint, enable the app router and customize import aliases, install the OpenAI library, and simplify pages and styles for a GPT-3 backend.

Generate an OpenAI API key from platform.openai.com, securely store it in a dot local file, and prepare your nextjs backend to connect to the OpenAI API.

Open Visual Studio Code, create api/q-a/route.tsx, and implement a get route that reads a question and uses OpenAI to respond as an Android instructor.

Test Nextjs server with npm run dev and postman, sending GET requests to Q queries, explore Kotlin val, and verify 400 bad request for missing params before building Android client.

Create an Android app connected to a backend that retrieves GPT-3 generated answers and set up the project with an empty compose activity, gradle configurations, and essential dependencies.

Set up a clean mvvm-based Android project structure with data, domain, and presentation layers, including local, remote, repository, and dependency injection packages, plus a use case package, util, and components.

Refine app styling by updating colors and themes: remove extra colors in colors.xml, keep black and white, set status bar to black in themes.xml, and run to verify.

Define a retrofit q&a api interface with a suspend get request and a query parameter, plus a base url constant and a generic resource class for loading, success, and error.

Create a room database for Q&A: define a Q&A data class with id as primary key, annotate as entity, implement a DAO with insert, delete by id, and get all.

Implement a q&a repository by linking the domain and data layers with a dao and api, include suspend and flow-based operations, and prepare for Dagger Hilt injection.

Implement Dagger Hilt dependency injection by creating an app module, providing the Q&A database, DAO, Retrofit API, and repository, and configuring the application class and manifest for internet access.

Set up navigation for the Kotlin Android assistant app by implementing a sealed Screen with routes for Q&A list, ask question, and Q&A detail, and wiring a nav host.

Create a Q&A list view model in Kotlin with get Q&A's and delete by id use cases, injecting a repository, and implement a compose UI with a LazyColumn.

Build the ask question screen for an ai-powered android app by implementing a get answer use case with repository, coroutines flow, error handling, and a compose UI with text bubbles.

Build a GPT-3 powered Android Q&A screen with a repository-backed use case, view model, and compose UI. Implement back navigation to the list screen.

Celebrate completing the AI-powered Android apps with GPT-3 course and showcase your ability to fuse AI integration with Android development. Carry this knowledge into future innovative apps.