diff --git a/bootcamp/RAG/zilliz_pipeline_rag.ipynb b/bootcamp/RAG/zilliz_pipeline_rag.ipynb
index 6a6edd19e..92abc8577 100644
--- a/bootcamp/RAG/zilliz_pipeline_rag.ipynb
+++ b/bootcamp/RAG/zilliz_pipeline_rag.ipynb
@@ -11,10 +11,9 @@
    "source": [
     "# Build RAG using Zilliz Cloud Pipelines\n",
     "\n",
-    "[Zilliz Cloud Pipelines](https://docs.zilliz.com/docs/pipelines) is a fully-managed, scalable service for unstructured data indexing and retrieval. With intuitive API of ingestion and search pipelines, developers can easily build AI-powered search and RAG applications. leaving the heavy-lifting parts to\n",
-    "this service, which can transform unstructured data into high quality vector embeddings and retrieve semantically similar content efficiently.\n",
+    "[Zilliz Cloud Pipelines](https://docs.zilliz.com/docs/pipelines) is AI-powered retrieval service. It simplifies the maintenance of information retrieval system by providing ingestion and search pipelines as easy-to-use API service. As an AI application developer, with quality optimization and devops taken care of, you can focus on building AI applications tailored to your specific use case.\n",
     "\n",
-    "In this notebook, we show how to use [Zilliz Cloud Pipelines](https://zilliz.com/zilliz-cloud-pipelines) to build a simple yet scalable [RAG (Retrieval Augmented Generation)](https://research.ibm.com/blog/retrieval-augmented-generation-RAG) application. Retrieval is at the heart of RAG solution, which typically involves maintaining a knowledge base with document parsing and chunking, hosting an embedding model and using vector database. With Zilliz Cloud Pipelines, users no longer need to deal with such complex tech stack. Everything can be done with a simple API call.\n",
+    "In this notebook, we show how to use [Zilliz Cloud Pipelines](https://zilliz.com/zilliz-cloud-pipelines) to build a simple yet scalable [Retrieval Augmented Generation (RAG)](https://zilliz.com/use-cases/llm-retrieval-augmented-generation) application. Retrieval is at the heart of RAG solution, which typically involves maintaining a knowledge base with document parsing and chunking, hosting an embedding model and using vector database as retrieval engine. With Zilliz Cloud Pipelines, you don't need to deal with such a complex tech stack. Everything can be done with an API call.\n",
     "\n",
     "We first create the an Ingestion pipeline for document indexing and a Search pipeline for knowledge retrieval. Then we run Ingestion pipeline by API call to import documents to establish the knowledge base. Finally, we build an RAG application that runs Search pipeline to conduct Retrieval Augmented Generation.\n",
     "\n",
@@ -32,7 +31,7 @@
    "source": [
     "## Setup\n",
     "### Prerequisites\n",
-    "Please make sure you have a Serverless cluster in Zilliz Cloud. If not already, you can [sign up for free](https://cloud.zilliz.com/signup).\n",
+    "Please make sure you have a Serverless cluster in Zilliz Cloud. If not already, you can [sign up for free](https://cloud.zilliz.com/signup?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-21_github-docs_zilliz-pipeline-rag_github).\n",
     "\n",
     "To learn how to create a Serverless cluster and get your CLOUD_REGION, CLUSTER_ID and API_KEY, please refer to this [page](https://docs.zilliz.com/docs/create-cluster) for more details."
    ]