diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md
index 02b30c0de..db9b571e6 100644
--- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md
+++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/README.md
@@ -1,8 +1,30 @@
 # RAPIDS Accelerated UDF Examples
+
 This project contains sample implementations of RAPIDS accelerated user-defined functions.
 
+The ideal solution would be to replace the UDF with a series of DataFrame or SQL operations. If that
+is not possible, we also provide
+a [UDF compiler extension](https://nvidia.github.io/spark-rapids/docs/additional-functionality/udf-to-catalyst-expressions.html)
+to translate UDFs to Catalyst expressions. The extension is limited to only support compiling simple
+operations. For complicated cases, you can choose to implement a RAPIDS accelerated UDF.
+
 ## Spark Scala UDF Examples
 
+[URLDecode](src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala)
+is the simplest demo for getting started. From the code you can see there is an original CPU
+implementation provided by the `apply` method. We only need to implement the RapidsUDF interface
+which provides a single method we need to override called
+`evaluateColumnar`. The CPU URLDecode function processes the input row by row, but the GPU
+evaluateColumnar returns a cudf ColumnVector, because the GPU get its speed by performing operations
+on many rows at a time. In the `evaluateColumnar` function, there is a cudf implementation of URL
+decode that we're leveraging, so we don't need to write any native C++ code. This is all done
+through the [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable). The benefit to
+implement via the Java API is ease of development, but the memory model is not friendly for doing
+GPU operations because the JVM makes the assumption that everything we're trying to do is in heap
+memory. We need to free the GPU resources in a timely manner with try-finally blocks. Note that we
+need to implement both CPU and GPU functions so the UDF will still work if a higher-level operation
+involving the RAPIDS accelerated UDF falls back to the CPU.
+
 - [URLDecode](src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala)
   decodes URL-encoded strings using the
   [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
@@ -12,6 +34,23 @@ This project contains sample implementations of RAPIDS accelerated user-defined
 
 ## Spark Java UDF Examples
 
+Below are some examples for implementing RAPIDS accelerated Scala UDF via JNI and native code. If
+there is no existing simple Java API we could leverage, we can write native custom code.
+Take [CosineSimilarity](src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java) as the
+example, the Java class for the UDF is similar as the previous URLDecode/URLEncode demo. We need to
+implement a cosineSimilarity function in C++ code and goes into the native code as quickly as
+possible, because it is easier to write the code safely. In the native code, it `reinterpret_cast`
+the input to a column view, do some sanity checking and convert to list column views, then compute
+the cosine similarity, finally return the unique pointer to a column, release the underlying
+resources. On Java side we are going to wrap it in a column vector and own that resource.
+In `cosine_similarity.cu` we implement the computation as the actual CUDA kernel. In the CUDA kernel
+we can leverage the [Thrust template library](https://docs.nvidia.com/cuda/thrust/index.html) to
+write the standard algorithms for GPU parallelizing code. The benefit of implementing the UDF in
+native code is for maximum control over GPU memory utilization and performance. However the
+trade-off is a more complicated build environment, as we need to build against libcudf with
+significantly longer build times. Implementing a RAPIDS accelerated UDF in native code is a
+significant effort.
+
 - [URLDecode](src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java)
   decodes URL-encoded strings using the
   [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
@@ -24,6 +63,8 @@ This project contains sample implementations of RAPIDS accelerated user-defined
 
 ## Hive UDF Examples
 
+Below are some examples for implementing RAPIDS accelerated Hive UDF via JNI and native code.
+
 - [URLDecode](src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java)
   implements a Hive simple UDF using the
   [Java APIs of RAPIDS cudf](https://docs.rapids.ai/api/cudf-java/stable)
@@ -37,44 +78,48 @@ This project contains sample implementations of RAPIDS accelerated user-defined
   [native code](src/main/cpp/src) to count words in strings
 
 ## Building and run the tests without Native Code Examples
-Some UDF examples use native code in their implementation.
-Building the native code requires a libcudf build environment, so these
-examples do not build by default.
+
+Some UDF examples use native code in their implementation. Building the native code requires a
+libcudf build environment, so these examples do not build by default.
 
 ### Prerequisites
-Download Spark and set SPARK_HOME environment variable.
-Refer to [Prerequisites](../../../docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md#Prerequisites)  
-Install python 3.8+, then install pytest, pyspark, sre_yield, findspark by using pip or conda.
-For example:
+
+Download [Apache Spark](https://spark.apache.org/downloads.html) and set `SPARK_HOME` environment variable.
+Install Python 3.8+, then install `pytest`, `sre_yield` by using pip or conda. For
+example:
+
 ```
+export SPARK_HOME=path-to-spark
 pip install pytest
-pip install pyspark
 pip install sre_yield
-pip install findspark
 ```
 
 Run the following command to build and run tests
+
 ```bash
+cd spark-rapids-examples/examples/UDF-Examples/RAPIDS-accelerated-UDFs
 mvn clean package
 ./run_pyspark_from_build.sh -m "not rapids_udf_example_native"
 ```
 
 ## Building with Native Code Examples and run test cases
-The `udf-native-examples` Maven profile
-can be used to include the native UDF examples in the build, i.e.: specify
- `-Pudf-native-examples` on the `mvn` command-line.
+
+The `udf-native-examples` Maven profile can be used to include the native UDF examples in the build,
+i.e.: specify
+`-Pudf-native-examples` on the `mvn` command-line.
 
 ### Creating a libcudf Build Environment
+
 Building the native code requires a libcudf build environment.  
-The `Dockerfile` in this directory can be used to setup a Docker image that
-provides a libcudf build environment. This repository will either need to be
-cloned or mounted into a container using that Docker image.
-The `Dockerfile` contains build arguments to control the Linux version,
-CUDA version, and other settings. See the top of the `Dockerfile` for details.
+The `Dockerfile` in this directory can be used to setup a Docker image that provides a libcudf build
+environment. This repository will either need to be cloned or mounted into a container using that
+Docker image. The `Dockerfile` contains build arguments to control the Linux version, CUDA version,
+and other settings. See the top of the `Dockerfile` for details.
 
 First install docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
 
 Run the following commands to build and start a docker
+
 ```bash
 cd spark-rapids-examples/examples/UDF-Examples/RAPIDS-accelerated-UDFs
 docker build -t my-local:my-udf-example-ubuntu .
@@ -82,32 +127,42 @@ nvidia-docker run -it my-local:my-udf-example-ubuntu
 ```
 
 ### Build the udf-examples jar
-In the docker, clone the code and compile.
+
+In the Docker container, clone the code and compile.
+
 ```bash
 git clone https://github.com/NVIDIA/spark-rapids-examples.git
 cd spark-rapids-examples/examples/UDF-Examples/RAPIDS-accelerated-UDFs
 mvn clean package -Pudf-native-examples
 ```
-The building will spend some time like 1.5 hours.
-Then the rapids-4-spark-udf-examples*.jar is generated under RAPIDS-accelerated-UDFs/target directory.
+
+The build could take a long time (e.g.: 1.5 hours). Then the rapids-4-spark-udf-examples*.jar is
+generated under RAPIDS-accelerated-UDFs/target directory.
 
 ### Run all the examples including native examples in the docker
-Download Spark and set SPARK_HOME environment variable.
-Refer to [Prerequisites](../../../docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md#Prerequisites)   
-Set SPARK_HOME environment variable. 
+
+See the above [Prerequisites section](#prerequisites)
+
 ```
 export SPARK_HOME=path-to-spark
+pip install pytest
+pip install sre_yield
 ```
-Install python 3.8+, then install pytest, pyspark, sre_yield, findspark by using pip or conda.
-See above Prerequisites section
+
+Run the following command to run tests
+
 ```
 ./run_pyspark_from_build.sh
 ```
 
 ## How to run the Native UDFs on Spark local mode
-First finish the steps in "Building with Native Code Examples and run test cases" section, then do the following in the docker.
+
+First finish the steps in 
+[Building with Native Code Examples and run test cases](#building-with-native-code-examples-and-run-test-cases) section, 
+then do the following inside the Docker container.
 
 ### Get jars from Maven Central
+
 [rapids-4-spark_2.12-23.08.1.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.1/rapids-4-spark_2.12-23.08.1.jar)
 
 ### Launch a local mode Spark
@@ -151,5 +206,3 @@ spark.sql("CREATE TEMPORARY FUNCTION {} AS '{}'".format("wordcount", "com.nvidia
 spark.sql("select wordcount(c1) from tab group by c1").show()
 spark.sql("select wordcount(c1) from tab group by c1").explain()
 ```
-
-Refer to [more Spark modes](../../../docs/get-started/xgboost-examples/on-prem-cluster) to test against more Spark modes.
diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh
index 6cf4f5c70..14512619b 100755
--- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh
+++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh
@@ -49,7 +49,7 @@ else
       "$SCRIPTPATH"
       "$SCRIPTPATH"/src/main/python)
 
-    # --ignore=target is used to exclude the target directory whihch contains unrelated python files.
+    # --ignore=target is used to exclude the target directory which contains unrelated python files.
     TEST_COMMON_OPTS=(-v
           -rfExXs
           "$TEST_ARGS"
diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src/CosineSimilarityJni.cpp b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src/CosineSimilarityJni.cpp
index aa7035857..a707d9a7e 100644
--- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src/CosineSimilarityJni.cpp
+++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/src/CosineSimilarityJni.cpp
@@ -76,7 +76,7 @@ Java_com_nvidia_spark_rapids_udf_java_CosineSimilarity_cosineSimilarity(JNIEnv*
     auto lv2 = cudf::lists_column_view(*v2);
     std::unique_ptr<cudf::column> result = cosine_similarity(lv1, lv2);
 
-    // take ownership of the column and return the column address to Java
+    // take ownership of the column and return the column address to Java and release the underlying resources.
     return reinterpret_cast<jlong>(result.release());
   } catch (std::bad_alloc const& e) {
     auto msg = std::string("Unable to allocate native memory: ") +
diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java
index e85774777..b4df62b4f 100644
--- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java
+++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java
@@ -68,9 +68,12 @@ public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) {
 
     // Load the native code if it has not been already loaded. This is done here
     // rather than in a static code block since the driver may not have the
-    // required CUDA environment.
+    // required CUDA environment. 
     NativeUDFExamplesLoader.ensureLoaded();
-
+    
+    // We need to go into the native code as quickly as possible
+    // because it is easier to write the code safely.
+    // Then wrap returns in a column vector and own that resource.
     return new ColumnVector(cosineSimilarity(args[0].getNativeView(), args[1].getNativeView()));
   }