Update how-to-use-pipelines-prompt-flow.md

update with correct notebook URLs. 
For issue #112204

Author: HaoHoo

articles/machine-learning/how-to-use-pipelines-prompt-flow.md

@@ -37,19 +37,19 @@ Azure Machine Learning offers notebook tutorials for several use cases with prom
 
 **QA Data Generation** 
 
-[QA Data Generation](https://github.com/Azure/azureml-insiders/blob/main/previews/retrieval-augmented-generation/examples/notebooks/qa_data_generation.ipynb) can be used to get the best prompt for RAG and to evaluation metrics for RAG. This notebook shows you how to create a QA dataset from your data (Git repo). 
+[QA Data Generation](https://github.com/Azure/azureml-examples/blob/main/sdk/python/generative-ai/rag/notebooks/qa_data_generation.ipynb) can be used to get the best prompt for RAG and to evaluation metrics for RAG. This notebook shows you how to create a QA dataset from your data (Git repo). 
 
 
 **Test Data Generation and Auto Prompt**
 
-[Use vector indexes to build a retrieval augmented generation model](https://github.com/Azure/azureml-insiders/blob/main/previews/retrieval-augmented-generation/examples/notebooks/mlindex_with_testgen_autoprompt.ipynb) and to evaluate prompt flow on a test dataset.
+[Use vector indexes to build a retrieval augmented generation model](https://github.com/Azure/azureml-examples/blob/main/sdk/python/generative-ai/rag/notebooks/mlindex_with_testgen_autoprompt.ipynb) and to evaluate prompt flow on a test dataset.
 
 **Create a FAISS based Vector Index**
 
-[Set up an Azure Machine Learning Pipeline](https://github.com/Azure/azureml-insiders/blob/main/previews/retrieval-augmented-generation/examples/notebooks/faiss/faiss_mlindex_with_langchain.ipynb) to pull a Git Repo, process the data into chunks, embed the chunks and create a langchain compatible FAISS Vector Index. 
+[Set up an Azure Machine Learning Pipeline](https://github.com/Azure/azureml-examples/blob/main/sdk/python/generative-ai/rag/notebooks/faiss/faiss_mlindex_with_langchain.ipynb) to pull a Git Repo, process the data into chunks, embed the chunks and create a langchain compatible FAISS Vector Index. 
 
 ## Next steps
 
 [How to create vector index in Azure Machine Learning prompt flow (preview)](how-to-create-vector-index.md)
 
-[Use Vector Stores](concept-vector-stores.md) with Azure Machine Learning (preview)
+[Use Vector Stores](concept-vector-stores.md) with Azure Machine Learning (preview)