update doc according to comments (#805)

Signed-off-by: Zheng, Zhaoqiong <zhaoqiong.zheng@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ChatQnA/README.md

@@ -8,7 +8,7 @@ ChatQnA architecture shows below:
 
 ![architecture](./assets/img/chatqna_architecture.png)
 
-ChatQnA is implemented on top of [GenAIComps](https://github.com/opea-project/GenAIComps), the ChatQnA Flow Chart shows below:
+The ChatQnA example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
 
 ```mermaid
 ---
@@ -95,8 +95,7 @@ flowchart LR
 
 ```
 
-This ChatQnA use case performs RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2 or Intel Xeon Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Visit [Habana AI products](https://habana.ai/products) for more details.
-
+This ChatQnA use case performs RAG using LangChain, Redis VectorDB and Text Generation Inference on [Intel Gaudi2](https://www.intel.com/content/www/us/en/products/details/processors/ai-accelerators/gaudi-overview.html) or [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon.html).
 In the below, we provide a table that describes for each microservice component in the ChatQnA architecture, the default configuration of the open source project, hardware, port, and endpoint.
 
 <details>

CodeTrans/README.md

@@ -6,7 +6,7 @@ The workflow falls into the following architecture:
 
 ![architecture](./assets/img/code_trans_architecture.png)
 
-This Code Translation use case uses Text Generation Inference on Intel Gaudi2 or Intel Xeon Scalable Processor. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Visit [Habana AI products](https://habana.ai/products) for more details.
+This Code Translation use case demonstrates Text Generation Inference across multiple platforms. Currently, we provide examples for [Intel Gaudi2](https://www.intel.com/content/www/us/en/products/details/processors/ai-accelerators/gaudi-overview.html) and [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon.html), and we invite contributions from other hardware vendors to expand OPEA ecosystem.
 
 ## Deploy Code Translation Service
 

README.md

@@ -7,7 +7,7 @@
 
 ## Introduction
 
-GenAIComps-based Generative AI examples offer streamlined deployment, testing, and scalability. All examples are fully compatible with Docker and Kubernetes, supporting a wide range of hardware platforms such as Gaudi, Xeon, and other hardwares.
+GenAIExamples are designed to give developers an easy entry into generative AI, featuring microservice-based samples that simplify the processes of deploying, testing, and scaling GenAI applications. All examples are fully compatible with Docker and Kubernetes, supporting a wide range of hardware platforms such as Gaudi, Xeon, and NVIDIA GPU, and other hardwares, ensuring flexibility and efficiency for your GenAI adoption.
 
 ## Architecture