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Dynamically update your Firebase AI Logic app with Firebase Remote Config
Stay organized with collections Save and categorize content based on your preferences.

When calling the Gemini API from your app using a Firebase AI Logic SDK, your request contains a number of parameters that control generative AI responses. These usually include the model name, the model generation configuration (maximum tokens, temperature, etc.), safety settings, system instructions, and prompt data.

In most cases, you'll want to change these on-demand or as needed for a number of scenarios:

Update your generative AI model without releasing a new app. You can upgrade to newer, stable model versions before earlier versions are decommissioned, drop to lower-cost or higher performance models based on your users' needs and attributes, or conditionally deploy the latest and greatest models to specific user segments (like beta testers).
Set the location where you access the model so that it's closer to your users.
A/B test different system instructions and prompts, then slowly roll out the winning experiment values to your users.
Use feature flags to quickly expose or hide generative AI features in your app.

Firebase Remote Config does all of this and more, letting you update parameter values as needed and conditionally for app instances that match characteristics you set in the Firebase console, without releasing a new version of your app.

This solution guide provides specific recommended use cases and describes how to add Remote Config to your generative AI app.

Jump to code implementation

Why use Firebase Remote Config with your app?

Firebase Remote Config lets you dynamically adjust your app's behavior without requiring app updates. This is especially powerful for apps that use generative AI, where rapid iteration and fine-tuning are crucial.

Essential use cases for Remote Config with generative AI apps

We recommend using Remote Config with Firebase AI Logic for the following essential use cases:

Upgrade to the latest model version without an app update: Use Remote Config parameters to change the model name as needed, so that you can upgrade to the latest version of your preferred Gemini model as soon as it's available.
Update system instructions and safety settings without an app update: Store system instructions and safety settings inside Remote Config parameters to ensure that you can change them on-demand if you discover issues after deployment.
Reduce risk and enforce AI safety: Use Remote Config rollouts to safely and gradually release generative AI changes to your iOS and Android users.

Advanced and recommended use cases for Remote Config with generative AI apps

After instrumenting your app with Remote Config and Google Analytics, you can explore advanced use cases:

Set location based on client location: Use Remote Config conditions to set the location where you access the model based on the client's detected location.
Experiment with different models: Quickly test and switch between various generative AI models, or even access different models for different user segments, to find the best fit for your specific use case.
Optimize model performance: Fine-tune model parameters, such as system prompt, maximum output tokens, temperature, and other settings.
Use different system instructions, prompts, and model configuration based on client attributes: When using Remote Config with Google Analytics, you can create conditions based on client attributes or custom audiences and set different parameters based on these attributes.

For example, if you're using generative AI to provide technical support in your app, you might want to set system instructions specific to the app platform to ensure accurate instructions are provided to your Android, iOS, and web platform users.
Personalize experiences for each user: Use Remote Config personalization with your mobile apps and games to automatically determine the optimum generative AI settings for each user.
Control costs: Remotely adjust which generative AI models are called, how frequently they are used, and dynamically configure maximum output token values based on user audience to reduce unnecessary costs.
Optimize app experience and results: Use A/B Testing with Remote Config with your mobile apps and games to test changes to generative AI parameters across different user segments to see how they affect key metrics like retention and revenue.

By instrumenting your generative AI app with Firebase Remote Config, you can build flexible, safe, and cost-effective AI-powered applications while creating delightful experiences for your users.

Add Firebase Remote Config to your app

In this solution guide, you'll use Firebase Remote Config to dynamically update parameters in your Android app that use the Firebase AI Logic SDK. You will learn how to:

Fetch and activate parameters like model names and system instructions from Firebase Remote Config.
Update your Gemini API calls to use the dynamically retrieved parameters, letting you switch between different models or modify system instructions without an app update.
Control parameters remotely, adjusting model behavior and capabilities as needed.

Prerequisites

This guide assumes that you're familiar with developing apps for your platform.

Before you begin, make sure that you do the following:

Complete the Firebase AI Logic getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the backend service for your chosen "Gemini API" provider, and create a model instance.
Enable Google Analytics in your Firebase project and add its SDK to your app (required for conditional targeting, like setting the location where you access the model based on the client device's location).

Step 1: Set parameter values in the Firebase console

Create a client Remote Config template and configure parameters and values to fetch and use in the app.

Open your Firebase project in the Firebase console. Then, from the navigation menu, expand Run and select Remote Config.
Ensure that Client is selected from the Client/Server selector at the top of the page.
Start a client template by clicking Create Configuration (or Add parameter if you've previously used client templates).

Define the parameters you want to control with Remote Config. For example:

Parameter name	Description	Type	Default value
`model_name`	Model name. See available model names.	String	`gemini-2.0-flash`
`system_instructions`	System instructions are like a "preamble" that you add before the model gets exposed to any further instructions from the end user to influence model behavior.	String	`You are a helpful assistant who knows everything there is to know about Firebase!`
`prompt`	Default prompt to use with your generative AI feature.	String	`I am a developer who wants to know more about Firebase!`
`vertex_location`	Only applicable if using Vertex AI Gemini API. Control the location to access the model. You can set conditions to configure this option based on client location detected by Google Analytics.	String	`us-central1`

When you've finished adding parameters, click Publish changes. If this is not a new Remote Config template, review the changes and click Publish changes again.

Step 2: Add and initialize Remote Config in your app

Add the Remote Config library and set up Remote Config within your app.

Swift

As part of Firebase AI Logic setup, you've already added the Firebase SDK to your app, but will also need to add Remote Config.

In Xcode, with the project open, navigate to File > Add Package Dependencies.
Select firebase-ios-sdk and then click Add package.
From the Project navigator, select your app > Targets > your app.
From the General tab, scroll to Frameworks, Libraries, and Embedded Content.
Click + and choose FirebaseRemoteConfig, then click Add.
Add the FirebaseRemoteConfig import to your code:
```
import FirebaseRemoteConfig
```
Inside the appropriate class for your app, initialize Firebase and add Remote Config to your main application logic.

Here, you'll include Remote Config and the Remote Config real-time listener as imports so that the app can fetch new values in real-time, and add a minimum fetch interval:
```
let remoteConfig = RemoteConfig.remoteConfig()
let settings = RemoteConfigSettings()
settings.minimumFetchInterval = 3600
remoteConfig.configSettings = settings
```
In the quickstart app, this would be inside VertexAISampleApp, within the AppDelegate class.

Note: In this example, the default fetch interval is 3600 seconds, but we recommend that you set a relatively low minimum fetch interval inside your code during development.

Kotlin

Add the Remote Config dependency to your module (app-level) Gradle file (usually app/build.gradle.kts or app/build.gradle):

dependencies {
    implementation(platform("com.google.firebase:firebase-bom:33.13.0"))
    implementation("com.google.firebase:firebase-ai")
    implementation("com.google.firebase:firebase-config")
    // ... other dependencies
}

Add Remote Config to your main application logic. Here, you'll initialize Remote Config and add a minimum fetch interval:
```
val remoteConfig: FirebaseRemoteConfig = Firebase.remoteConfig
val configSettings = remoteConfigSettings {
minimumFetchIntervalInSeconds = 3600
}
remoteConfig.setConfigSettingsAsync(configSettings)
```
Note: In this example, the default fetch interval is 3600 seconds, but we recommend that you set a relatively low minimum fetch interval inside your code during development.

Java

Add the Remote Config dependency to your module (app-level) Gradle file (usually app/build.gradle.kts or app/build.gradle):

dependencies {
    implementation(platform("com.google.firebase:firebase-bom:33.13.0"))
    implementation("com.google.firebase:firebase-ai")
    implementation("com.google.firebase:firebase-config")
    // ... other dependencies
}

Add Remote Config to your main application logic. Here, you'll initialize Remote Config and add a minimum fetch interval:

FirebaseRemoteConfig mFirebaseRemoteConfig = FirebaseRemoteConfig.getInstance();
FirebaseRemoteConfigSettings configSettings = new FirebaseRemoteConfigSettings.Builder()
    .setMinimumFetchIntervalInSeconds(3600)
    .build();
mFirebaseRemoteConfig.setConfigSettingsAsync(configSettings);

Web

Open your code in a text editor and import Remote Config:

import { getRemoteConfig } from 'firebase/remote-config';

Inside your primary function and after the Firebase app is initialized for Firebase AI Logic SDK, initialize Remote Config:
```
  // Initialize Remote Config and get a reference to the service
  const remoteConfig = getRemoteConfig(app);
```
Set a minimum fetch interval:
```
remoteConfig.settings.minimumFetchIntervalMillis = 3600000;
```
Note: In this example, the default fetch interval is 3600 seconds, but we recommend that you set a relatively low minimum fetch interval inside your code during development.

Dart

From your Flutter project directory, install and add Remote Config using the following command:
```
flutter pub add firebase_remote_config
```

Open ./lib/main.dart and add the import after the other imports you added to support Firebase AI Logic:

import 'package:firebase_vertexai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'package:firebase_remote_config/firebase_remote_config.dart';

Add _modelName, _systemInstructions, and _prompt variables to your app so that we can use them later:

late final String _modelName;
late final String _systemInstructions;
late final String _prompt;

Get the Remote Config object instance and set the minimum fetch interval to allow for frequent refreshes. Make sure to add this after Firebase is initialized.
```
  final remoteConfig = FirebaseRemoteConfig.instance;
  await remoteConfig.setConfigSettings(RemoteConfigSettings(
    fetchTimeout: const Duration(seconds: 3600),
    minimumFetchInterval: const Duration(seconds: 3600),
  ));
```
Note: In this example, the default fetch interval is 3600 seconds, but we recommend that you set a relatively low minimum fetch interval inside your code during development.

Unity

Add Remote Config to your Unity project, following these instructions.
Get the Remote Config object instance and set the minimum fetch interval to allow for frequent refreshes. Make sure to add this after Firebase is initialized.
```
var remoteConfig = FirebaseRemoteConfig.DefaultInstance;
const int MillisecondsPerSecond = 1000;
await remoteConfig.SetConfigSettingsAsync(new ConfigSettings() {
  FetchTimeoutInMilliseconds = 3600 * MillisecondsPerSecond,
  MinimumFetchIntervalInMilliseconds = 3600 * MillisecondsPerSecond
});
```
Note: In this example, the default fetch interval is 3600 seconds, but we recommend that you set a relatively low minimum fetch interval inside your code during development.

Step 3: Set in-app parameter values

You should set in-app default parameter values in the Remote Config object. This ensures that your app behaves as expected even if it cannot fetch values from the Remote Config service.

Swift

In the Firebase console, open Remote Config.
In the Parameters tab, open the Menu, and select Download default values.
When prompted, enable .plist for iOS, then click Download file.
Save the file in the your application directory.

If using the sample app, save it within FirebaseVertexAI/Sample/VertexAISample.
In Xcode, right-click on your app and select Add Files

If using the sample, right-click on VertexAISample and select Add Files to "VertexAISample".
Select remote_config_defaults.plist, then click Add.

Update your app code to reference the defaults file:

// Set default values
remoteConfig.setDefaults(fromPlist: "remote_config_defaults")

Kotlin

From the Firebase console, open Remote Config.
In the Parameters tab, open the Menu, and select Download default values.
When prompted, enable .xml for Android, then click Download file.
Save the file in your app's XML resources directory.
Update your main activity file to add the defaults after the configSettings you added previously:
```
// Set default values.
remoteConfig.setDefaultsAsync(R.xml.remote_config_defaults)
```

Java

In the Firebase console, open Remote Config.
In the Parameters tab, open the Menu, and select Download default values.
When prompted, enable .xml for Android, then click Download file.
Save the file in your app's XML resources directory.

Update your main activity file to add the defaults after the configSettings you added previously:

// Set default values.
mFirebaseRemoteConfig.setDefaultsAsync(R.xml.remote_config_defaults);

Web

You can set the default values directly in your code:

// Set default Remote Config parameter values
remoteConfig.defaultConfig = {
  model_name: 'gemini-2.0-flash',
  system_instructions:
    'You are a helpful assistant who knows everything there is to know about Firebase!',
  prompt: 'I am a developer who wants to know more about Firebase!',
  vertex_location: 'us-central1',
};

Dart

You can set the default values directly in your code:

remoteConfig.setDefaults(const {
  "model_name": "gemini-2.0-flash",
  "system_instructions": "You are a helpful assistant who knows everything there is to know about Firebase!",
  "prompt": "I am a developer who wants to know more about Firebase!",
  "vertex_location": "us-central1"
});

Unity

You can set the default values directly in your code:

await remoteConfig.SetDefaultsAsync(
  new System.Collections.Generic.Dictionary<string, object>() {
    { "model_name", "gemini-2.0-flash" },
    { "system_instructions", "You are a helpful assistant who knows everything there is to know about Firebase!" },
    { "prompt", "I am a developer who wants to know more about Firebase!" },
    { "vertex_location", "us-central1" }
  }
);

Step 4: Fetch and activate values

After setting defaults, add the following to fetch and activate values.

Swift

// Fetch and activate Remote Config values
remoteConfig.fetchAndActivate { status, error in
  if let error = error {
    print("Error fetching Remote Config: \(error.localizedDescription)")
  }
}

This should update the Remote Config object whenever a new Remote Config template is published.

Kotlin

// Fetch and activate Remote Config values
remoteConfig.fetchAndActivate()
      .addOnCompleteListener(this) { task ->
          if (task.isSuccessful) {
              val updated = task.result
              Log.d(TAG, "Remote Config values fetched and activated: $updated")
          } else {
              Log.e(TAG, "Error fetching Remote Config", task.exception)
          }
      }

Java

  // Fetch and activate Remote Config values
  mFirebaseRemoteConfig.fetchAndActivate()
    .addOnCompleteListener(this, new OnCompleteListener<Boolean>() {
        @Override
        public void onComplete(@NonNull Task<Boolean> task) {
            if (task.isSuccessful()) {
                boolean updated = task.getResult();
                Log.d(TAG, "Config params updated: " + updated);
            } else {
                Log.e(TAG, "Error fetching Remote Config", task.exception)
            }
          }
    });

Web

Add getValue and fetchAndActivate to your imports:

import { getValue, fetchAndActivate } from 'firebase/remote-config';

After the code you added to configure default Remote Config values, fetch and activate the config, then assign values to the modelName, systemInstructions, prompt, and vertexLocation constants.

// Fetch and activate Remote Config.
try {
  await fetchAndActivate(remoteConfig);
} catch(err) {
  console.error('Remote Config fetch failed', err);
}

console.log('Remote Config fetched.');

// Assign Remote Config values.
const modelName = getValue(remoteConfig, 'model_name').asString();
const systemInstructions = getValue(remoteConfig, 'system_instructions').asString();
const prompt = getValue(remoteConfig, 'prompt').asString();
const vertexLocation = getValue(remoteConfig, 'vertex_location').asString();

Dart

// Fetch and activate Remote Config.
remoteConfig.fetchAndActivate();

// Assign Remote Config values.
String? _modelName = remoteConfig.getString("model_name");
String? _systemInstructions = remoteConfig.getString("system_instructions");
String? _prompt = remoteConfig.getString("prompt");
String? _vertexLocation = remoteConfig.getString("vertex_location");

Unity

// Fetch and activate Remote Config values.
await remoteConfig.FetchAndActivateAsync();

Step 5: Add a real-time Remote Config listener

Add a real-time Remote Config listener to your app to ensure that changes you make to the Remote Config template are propagated to the client as soon as they're updated.

The following code updates the Remote Config object whenever a parameter value changes.

Swift

// Add real-time Remote Config
remoteConfig.addOnConfigUpdateListener { configUpdate, error in
  guard let configUpdate = configUpdate, error == nil else {
    print("Error listening for config updates: \(error?.localizedDescription ?? "No error available")")
    return
  }

  print("Updated keys: \(configUpdate.updatedKeys)")
  remoteConfig.activate { changed, error in
    guard error == nil else {
      print("Error activating config: \(error?.localizedDescription ?? "No error available")")
      return
    }
    print("Activated config successfully")
  }
}

Kotlin

Optionally, you can also configure an action inside the addOnCompleteListener activation:

      // Add a real-time Remote Config listener
      remoteConfig.addOnConfigUpdateListener(object : ConfigUpdateListener {
          override fun onUpdate(configUpdate : ConfigUpdate) {
              Log.d(ContentValues.TAG, "Updated keys: " + configUpdate.updatedKeys);
              remoteConfig.activate().addOnCompleteListener {
                  // Optionally, add an action to perform on update here.
              }
          }

          override fun onError(error : FirebaseRemoteConfigException) {
              Log.w(ContentValues.TAG, "Config update error with code: " + error.code, error)
          }
      }

Java

Optionally, you can also configure an action inside the addOnCompleteListener activation:

  // Add a real-time Remote Config listener
  remoteConfig.addOnConfigUpdateListener(new ConfigUpdateListener() {
      @Override
      public void onUpdate(ConfigUpdate configUpdate) {
          Log.d(ContentValues.TAG, "Updated keys: " + configUpdate.getUpdatedKeys());
                remoteConfig.activate().addOnCompleteListener(new OnCompleteListener<Boolean>() {
                  @Override
                  public void onComplete(@NonNull Task<Boolean> task) {
                      // Optionally, add an action to perform on update here.
                  }
              });
          }

      @Override
      public void onError(FirebaseRemoteConfigException error) {
          Log.w(ContentValues.TAG, "Config update error with code: " + error.getCode(), error);
      }
  });

Web

Real-time Remote Config listeners aren't supported for Web apps.

Dart

// Add a real-time Remote Config listener
remoteConfig.onConfigUpdated.listen((event) async {
  await remoteConfig.activate();
});

Unity

// Add a real-time Remote Config listener to automatically update whenever
// a new template is published.
// Note: the parameters can be anonymous as they are unused.

remoteConfig.OnConfigUpdateListener += (_, _) => {
  remoteConfig.ActivateAsync();
};

Step 6: Update the Gemini API requests to use Remote Config values

Click your Gemini API provider to view provider-specific content and code on this page.

Now that Remote Config is fully configured, update your code to replace hard-coded values with values sourced from Remote Config.

Swift

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
let modelName = remoteConfig.configValue(forKey: "model_name").stringValue
let systemInstructions = remoteConfig.configValue(forKey: "system_instructions").stringValue

let model = ai.generativeModel(
  modelName: modelName,
  systemInstruction: ModelContent(role: "system", parts: systemInstructions)
)

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
let userPrompt = remoteConfig.configValue(forKey: "prompt").stringValue

// To generate text output, call `generateContent` with the text input
let response = try await model.generateContent(userPrompt)
if let text = response.text {
  print(text)
}

Kotlin

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
val ai = Firebase.ai(backend = GenerativeBackend.googleAI())

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
val model = ai.generativeModel(
  modelName = remoteConfig.getString("model_name"),
  systemInstruction = content { text(remoteConfig.getString("system_instructions")) }
)

// To generate text output, call `generateContent` with the text input
// The text in the prompt will be sourced from Remote Config
val response = model.generateContent(remoteConfig.getString("prompt"))
print(response.text)

Java

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
FirebaseAI ai = FirebaseAI.getInstance(GenerativeBackend.googleAI());

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
GenerativeModel gm = ai.generativeModel(
        /* modelName */ remoteConfig.getString("model_name"),
        /* generationConfig (optional) */ null,
        /* safetySettings (optional) */ null,
        /* tools (optional) */ null,
        /* toolsConfig (optional) */ null,
        /* systemInstruction (optional) */ new Content.Builder().addText(
                remoteConfig.getString("system_instructions")).build(),
        /* requestOptions (optional) */ new RequestOptions()
);
GenerativeModelFutures model = GenerativeModelFutures.from(gm);

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
Content userPrompt = new Content.Builder()
        .addText(remoteConfig.getString("prompt"))
        .build();

// To generate text output, call `generateContent` with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(userPrompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        System.out.println(resultText);
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
const model = getGenerativeModel(ai, {
  model: modelName,
  systemInstruction: systemInstruction
});

// Wrap in an async function so you can use await
async function run() {
  // Provide a prompt that contains text
  // The text in the prompt will be sourced from Remote Config
  const userPrompt = prompt;

  // To generate text output, call `generateContent` with the text input
  const result = await model.generateContent(userPrompt);

  const response = result.response;
  const text = response.text();
  console.log(text);
}

Dart

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
final ai = await FirebaseAI.googleAI();

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
final model =
      ai.generativeModel(
        model: _modelName,
        systemInstruction: Content.system(_systemInstructions),
      );

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
final _userPrompt = [Content.text(_prompt)];

// To generate text output, call `generateContent` with the text input
final response = await model.generateContent(_userPrompt);
print(response.text);

Unity

// Initialize the Gemini Developer API backend service
// The Gemini Developer API doesn't support setting the location of a model
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` and add system instructions into its config
// Both the model name and the system instructions will be sourced from Remote Config
var modelName = remoteConfig.GetValue("model_name").StringValue;
var systemInstructions = remoteConfig.GetValue("system_instructions").StringValue;

var model = ai.GetGenerativeModel(
  modelName: modelName,
  systemInstruction: ModelContent.Text(systemInstructions)
);

// Provide a prompt that contains text
// The text in the prompt will be sourced from Remote Config
var userPrompt = remoteConfig.GetValue("prompt").StringValue;

// To generate text output, call `GenerateContentAsync` with the text input
var response = await model.GenerateContentAsync(userPrompt);
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

Step 7: Run the app

Build and run the app and verify that it works. Make changes to your configuration from the Remote Config page in the Firebase console, publish the changes, and verify the result.

Next steps

Learn more about Remote Config.
Add Google Analytics to your client code to enable targeting.
For mobile apps and games:
- Test different model settings with Remote Config and A/B Testing.
- Gradually release model parameter changes using Remote Config rollouts (iOS+ and Android only).
- Use Remote Config personalization to use machine learning to determine the best settings for individual users (iOS+, Android, and Unity only).

Dynamically update your Firebase AI Logic app with Firebase Remote Config Stay organized with collections Save and categorize content based on your preferences.

Why use Firebase Remote Config with your app?

Essential use cases for Remote Config with generative AI apps

Advanced and recommended use cases for Remote Config with generative AI apps

Add Firebase Remote Config to your app

Prerequisites

Step 1: Set parameter values in the Firebase console

Step 2: Add and initialize Remote Config in your app

Swift

Kotlin

Java

Web

Dart

Unity

Step 3: Set in-app parameter values

Swift

Kotlin

Java

Web

Dart

Unity

Step 4: Fetch and activate values

Swift

Kotlin

Java

Web

Dart

Unity

Step 5: Add a real-time Remote Config listener

Swift

Kotlin

Java

Web

Dart

Unity

Step 6: Update the Gemini API requests to use Remote Config values

Swift

Kotlin

Java

Web

Dart

Unity

Step 7: Run the app

Next steps

Dynamically update your Firebase AI Logic app with Firebase Remote Config
Stay organized with collections Save and categorize content based on your preferences.