Build AI Chatbot into Android App Big Nerd Ranch Style

Integrating an AI-powered chatbot into your Android app is exciting—and doing it with the Big Nerd Ranch approach ensures you learn by building and hugging best practices every step of the way. This guide walks through hands-on creation using Kotlin, Retrofit, Jetpack, and a free AI API.

Why Follow Big Nerd Ranch Style?

Big Nerd Ranch emphasizes:

Hands-on learning with real app-building
Clearly organized architecture—what goes in Activities, ViewModels, Repositories
Pro-level tooling like Retrofit, coroutines, dependency injection

By following this method, not only do you end up with a working chatbot, but you also internalize clean Android development patterns.

What Tools and Technologies You’ll Use

Here’s a quick snapshot:

Kotlin – modern, concise language
Android Studio – IDE with Jetpack support
Retrofit + OkHttp + Coroutines – fast, async HTTP
Jetpack – ViewModel, LiveData, Room
Dependency Injection – Hilt (or manual)
AI Service – OpenAI with free trial or an open-source alternative
External link: OpenAI docs: https://beta.openai.com/docs/

Step 1: Set Up Your Android Project

Start a clean Android project:

Create a new Kotlin project in Android Studio.
Set up modules/packages:
- ui.chat
- data.remote
- data.local
- domain

In your build.gradle:

gradleCopyEditimplementation "com.squareup.retrofit2:retrofit:2.9.0"
implementation "com.squareup.retrofit2:converter-moshi:2.9.0"
implementation "org.jetbrains.kotlinx:kotlinx-coroutines-android:1.6.0"
implementation "androidx.lifecycle:lifecycle-viewmodel-ktx:2.4.1"
implementation "androidx.room:room-ktx:2.4.2"
implementation "com.google.dagger:hilt-android:2.42"
kapt "com.google.dagger:hilt-compiler:2.42"

Step 1.1: Adding Retrofit and Coroutines

Define your API interface:

kotlinCopyEditinterface ChatApi {
  @POST("chat/completions")
  suspend fun sendMessage(@Body prompt: Prompt): ChatResponse
}

Set up Retrofit with a coroutine adapter:

kotlinCopyEditval retrofit = Retrofit.Builder()
  .baseUrl("https://api.openai.com/v1/")
  .addConverterFactory(MoshiConverterFactory.create())
  .build()
val api = retrofit.create(ChatApi::class.java)

Step 1.2: Configuring Dependency Injection

Using Hilt:

kotlinCopyEdit@Module
@InstallIn(SingletonComponent::class)
object NetworkModule {
  @Provides
  fun provideRetrofit(): Retrofit = ...
  @Provides
  fun provideChatApi(retrofit: Retrofit): ChatApi = retrofit.create()
}

Inject ChatApi in your ChatRepository.

Step 2: Designing the Chat UI

Define a RecyclerView with two view types—user and bot message.

Message(val text: String, val isUser: Boolean, val timestamp: Long)
ChatAdapter uses ListAdapter<Message>

In activity_chat.xml, include:

xmlCopyEdit<RecyclerView android:id="@+id/recycler_view" .../>
<EditText android:id="@+id/input_text" .../>
<Button android:id="@+id/send_button" .../>

Step 2.1: ViewModel–LiveData Integration

In ChatViewModel:

kotlinCopyEditval messages = MutableLiveData<List<Message>>()
fun sendMessage(text: String) = viewModelScope.launch {
  val userMsg = Message(text, true, System.currentTimeMillis())
  _messages.postAdd(userMsg)
  val response = repository.sendToAi(text)
  _messages.postAdd(Message(response, false, System.currentTimeMillis()))
}

Observe in your Activity/Fragment and update the adapter.

Step 3: Connecting to an AI Service

Register at OpenAI and obtain an API key (you can use their free trial). Other open-source alternatives include the Hugging Face Inference API.

When calling:

kotlinCopyEditsuspend fun sendToAi(prompt: String): String {
  val response = chatApi.sendMessage(Prompt(...))
  return response.choices.first().text
}

Handle ApiException for rate limiting or server errors.

Step 3.1: Handling Input/Output

Wrap your API result in a sealed class:

kotlinCopyEditsealed class Result<out T> {
  data class Success<out T>(val data: T): Result<T>()
  data class Error(val exception: Throwable): Result<Nothing>()
}

Use this to show loading indicators and retry options in the UI.

Step 4: Chat Logic Architecture

Use MVVM:

ChatRepository – handles network and local DB saves
ChatUseCase – features to send a message and retrieve history
ChatViewModel – orchestrates logic and updates UI

Step 4.1: Storing Chat History

Set up Room:

kotlinCopyEdit@Entity data class MessageEntity(...)
@Dao interface MessageDao {
  @Insert suspend fun insert(vararg msgs: MessageEntity)
  @Query("SELECT * FROM messages ORDER BY timestamp")
  fun getAll(): Flow<List<MessageEntity>>
}

In ViewModel, collect MessageDao.getAll() to populate UI on launch.

Step 5: UI Enhancements

Show a “typing…” indicator when awaiting response
Include message timestamps formatted with DateUtils.getRelativeTimeSpanString()
Add emoji support via EmojiCompat

Step 6: Testing and Polish

Unit tests for ViewModel logic
Repository tests mocking ChatApi with Mockito
UI tests using Espresso to verify send/receive flow

Also read-Top 5 AI Features in 2025 Smartphones That Actually Matter

Q&A Section

Q1: Which AI service is free?
OpenAI offers a free trial; you can also explore free Hugging Face models: https://huggingface.co/models

Q2: Is Hilt mandatory?
Not mandatory—Big Nerd Ranch style encourages modular architecture, and Hilt simplifies DI dramatically.

Q3: Can I support images in chat?
Yes. Send image prompts through DALL·E or similar, store URLs in your Message model, and use Glide to load images in RecyclerView.

Q4: What about offline usage?
Implement a local model using TensorFlow Lite for limited capabilities.

Q5: How to prevent UI blocking?
Always call network and DB operations inside viewModelScope.launch or background dispatchers.

Q6: Can we secure the API key?
Never commit keys in code. Use Android NDK or store in a remote backend that you call securely.

Conclusion & Next Steps

By adopting the Big Nerd Ranch style, you’ve built a clean, testable, and modular AI chatbot module for Android using Kotlin, Retrofit, Jetpack, and an AI backend. You’ve learned:

Project structure and dependency setup
UI design and RecyclerView patterns
Network integration with Retrofit + coroutines
DI with Hilt and architecture layers
Local storage with Room
Testing best practices

Next steps you can take:

Add conversation context handling and memory
Support offline mode with on-device models
Build a backend proxy to protect your API key
Extend the UI with voice input, attachments, or chat themes