diff --git a/bootcamp/RAG/advanced_rag/vanilla_rag_with_langchain.ipynb b/bootcamp/RAG/advanced_rag/vanilla_rag_with_langchain.ipynb
index bfa894873..804e43f6c 100644
--- a/bootcamp/RAG/advanced_rag/vanilla_rag_with_langchain.ipynb
+++ b/bootcamp/RAG/advanced_rag/vanilla_rag_with_langchain.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using LangChain and Milvus.\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "[LangChain](https://www.langchain.com/) is a framework for developing applications powered by large language models (LLMs). [Milvus](https://milvus.io/) is the world's most advanced open-source vector database, built to power embedding similarity search and AI applications.\n",
     "\n"
diff --git a/bootcamp/tutorials/integration/evaluation_with_ragas.ipynb b/bootcamp/tutorials/integration/evaluation_with_ragas.ipynb
index 3c7845c97..429f85f33 100644
--- a/bootcamp/tutorials/integration/evaluation_with_ragas.ipynb
+++ b/bootcamp/tutorials/integration/evaluation_with_ragas.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "This guide demonstrates how to use Ragas to evaluate a Retrieval-Augmented Generation (RAG) pipeline built upon [Milvus](https://milvus.io/).\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "[Ragas](https://docs.ragas.io/en/latest/index.html#) is a framework that helps you evaluate your RAG pipelines. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in.\n",
     "\n",
diff --git a/bootcamp/tutorials/integration/rag_with_milvus_and_haystack.ipynb b/bootcamp/tutorials/integration/rag_with_milvus_and_haystack.ipynb
index a71565aa1..0bd2ef984 100644
--- a/bootcamp/tutorials/integration/rag_with_milvus_and_haystack.ipynb
+++ b/bootcamp/tutorials/integration/rag_with_milvus_and_haystack.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using Haystack and Milvus.\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "[Haystack](https://haystack.deepset.ai/) is the open source Python framework by deepset for building custom apps with large language models (LLMs). [Milvus](https://milvus.io/) is the world's most advanced open-source vector database, built to power embedding similarity search and AI applications.\n",
     "\n",
diff --git a/bootcamp/tutorials/integration/rag_with_milvus_and_langchain.ipynb b/bootcamp/tutorials/integration/rag_with_milvus_and_langchain.ipynb
index 64e3c1a82..9adc5b621 100644
--- a/bootcamp/tutorials/integration/rag_with_milvus_and_langchain.ipynb
+++ b/bootcamp/tutorials/integration/rag_with_milvus_and_langchain.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using LangChain and Milvus.\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "[LangChain](https://www.langchain.com/) is a framework for developing applications powered by large language models (LLMs). [Milvus](https://milvus.io/) is the world's most advanced open-source vector database, built to power embedding similarity search and AI applications.\n",
     "\n",
diff --git a/bootcamp/tutorials/integration/rag_with_milvus_and_llamaindex.ipynb b/bootcamp/tutorials/integration/rag_with_milvus_and_llamaindex.ipynb
index ba760a89e..db295e340 100644
--- a/bootcamp/tutorials/integration/rag_with_milvus_and_llamaindex.ipynb
+++ b/bootcamp/tutorials/integration/rag_with_milvus_and_llamaindex.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using LlamaIndex and Milvus.\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "[LlamaIndex](https://www.llamaindex.ai/) is a simple, flexible data framework for connecting custom data sources to large language models (LLMs). [Milvus](https://milvus.io/) is the world's most advanced open-source vector database, built to power embedding similarity search and AI applications.\n",
     "\n",
diff --git a/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb b/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb
index 297b964ec..56aa7e6b0 100644
--- a/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb
+++ b/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "In this tutorial, we will show you how to build a RAG(Retrieval-Augmented Generation) pipeline with Milvus.\n",
     "\n",
-    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using a vector similarity search engine like Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
+    "The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.\n",
     "\n",
     "\n",
     "## Preparation\n",