[Android docs] Minor update to existing sections

docs/source/using-executorch-android.md

@@ -1,16 +1,26 @@
 # Using ExecuTorch on Android
 
-To use from Android, ExecuTorch provides Java API bindings and Android platform integration, available as a AAR file. The ExecuTorch C++ APIs can also be used from Android native.
+To use from Android, ExecuTorch provides Java/Kotlin API bindings and Android platform integration, available as an AAR file.
+
+Note: This page covers Android app integration through the AAR library. The ExecuTorch C++ APIs can also be used from Android native, and the documentation can be found on [this page about cross compilation](https://pytorch.org/executorch/main/using-executorch-building-from-source.html#cross-compilation).
 
 ## Installation
 
-We provide a prebuilt Android library (AAR), `executorch.aar` for both generic (image/audio processing) and LLAMA use case.
+We package all ExecuTorch Android libraries into an Android library (AAR), `executorch.aar` for both generic (image/audio processing) and LLM (LLaMA) use case. In each release, we will upload the prebuilt AAR artifacts. Users can also build the AAR from source.
+
+### Contents of library
 
-## Contents of library
-- `executorch.aar`
-  - [Java library](https://github.com/pytorch/executorch/tree/main/extension/android/src/main/java/org/pytorch/executorch)
-  - JNI contains the JNI binding for the corresponding Java code, and ExecuTorch native library, including core ExecuTorch runtime libraries, XNNPACK backend, Portable kernels, Optimized kernels, Quantized kernels, and LLAMA-specific Custom ops library.
-    - Comes with two ABI variants, arm64-v8a and x86\_64.
+The AAR artifact contains the Java library for users to integrate with their Java/Kotlin application code, as well as the corresponding JNI library (.so file), which is loaded by the Java code during initialization.
+
+- [Java library](https://github.com/pytorch/executorch/tree/main/extension/android/src/main/java/org/pytorch/executorch)
+- JNI contains the JNI binding for the corresponding Java code, and ExecuTorch native library, including
+  - core ExecuTorch runtime libraries
+  - XNNPACK backend
+  - Portable kernels
+  - Optimized kernels
+  - Quantized kernels
+  - LLaMa-specific Custom ops library.
+- Comes with two ABI variants, arm64-v8a and x86\_64.
 
 ## Downloading AAR
 
@@ -28,19 +38,31 @@ We provide a prebuilt Android library (AAR), `executorch.aar` for both generic (
 
 ## Using prebuilt libraries
 
-To add the Java library to your app, simply download the AAR, and add it to your gradle build rule.
+To add the Java library to your app:
+1. Download the AAR.
+2. Add it to your gradle build rule as a file path.
+
+The Java package requires `fbjni` and `soloader`, and currently requires users to explicitly declare the dependency. Therefore, two more `dependencies` in gradle rule is required:
+```
+implementation("com.facebook.soloader:soloader:0.10.5")
+implementation("com.facebook.fbjni:fbjni:0.5.1")
+```
+
+### Example usage
 
-In your app working directory, such as example executorch/examples/demo-apps/android/LlamaDemo,
+In your app working directory, such as executorch/examples/demo-apps/android/LlamaDemo,
 ```
 mkdir -p app/libs
-curl https://ossci-android.s3.amazonaws.com/executorch/release/executorch-241002/executorch.aar -o app/libs/executorch.aar
+curl https://ossci-android.s3.amazonaws.com/executorch/release/v0.5.0-rc3/executorch.aar -o app/libs/executorch.aar
 ```
 
 And include it in gradle:
 ```
 # app/build.grardle.kts
 dependencies {
     implementation(files("libs/executorch.aar"))
+    implementation("com.facebook.soloader:soloader:0.10.5")
+    implementation("com.facebook.fbjni:fbjni:0.5.1")
 }
 ```