Merge pull request #57 from STRIDES/fix_readme_zy

Updated README with GenAI Tutorial links

tutorials/README.md

@@ -34,9 +34,8 @@ There are a lot of ways to run workflows on AWS. Here we list a few possibilitie
  **Please also note, GPU machines cost more than most CPU machines, so be sure to shut these machines down after use, or apply an EC2 [lifecycle configuration](/docs/auto-shutdown-instance.md). You may also encounter service quotas to protect you from the accidental use of expensive machine types. If that happens, and you still want to use a certain instance type, follow these [instructions](/docs/service_quotas.md).**
 
 ## **Artificial Intelligence** <a name="ai"></a>
-Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Artificial intelligence and machine learning algorithms are being applied to a variety of biomedical research questions, ranging from image classification to genomic variant calling. AWS has a long list of AI/ML tutorials available and we have compiled a list here. Most recent development focuses on generative AI including use cases such as extracting information from text, transforming speech to text, and generating images from text. Sagemaker Studio  allows the user to rapidly create, test, and train generative AI models and has ready to use models all contained with [JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html). These models range from foundation models, fine-tunable models, and task-specific solutions. 
-+ For examples of generative AI, look at [this AWS GitHub repo](https://github.com/aws-samples/amazon-sagemaker-generativeai).
-+ You can also view [our tutorials](https://github.com/STRIDES/NIHCloudLabAWS/tree/main/tutorials/notebooks/GenAI) using several AWS products
+Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Artificial intelligence and machine learning algorithms are being applied to a variety of biomedical research questions, ranging from image classification to genomic variant calling. AWS has a long list of AI/ML tutorials available and we have compiled a list here. Most recent development focuses on generative AI including use cases such as extracting information from text, transforming speech to text, and generating images from text. Sagemaker Studio allows the user to rapidly create, test, and train generative AI models and has ready to use models all contained with [JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html). These models range from foundation models, fine-tunable models, and task-specific solutions. 
++ For examples of generative AI, view our [GenAI tutorials](/tutorials/notebooks/GenAI) that use several AWS products such as [Bedrock](/tutorials/notebooks/GenAI/AWS_Bedrock_Intro.ipynb) and [Jumpstart](/tutorials/notebooks/GenAI/AWS_GenAI_Jumpstart.ipynb) and utilizes other tools like [Langchain](/tutorials/notebooks/GenAI/Pubmed_chatbot.ipynb) and [Huggingface](/tutorials/notebooks/GenAI/AWS_GenAI_Huggingface.ipynb) to deploy, train, prompt, and implement techniques like [Retrieval-Augmented Generation (RAG)](/tutorials/notebooks/GenAI/Pubmed_chatbot.ipynb) to GenAI models. Also take a look at the [AWS GitHub repo](https://github.com/aws-samples/amazon-sagemaker-generativeai) for more Gen AI tutorials.
 + For other AI use cases, we recommend you start with this comprehensive [on-demand workshop](https://catalog.workshops.aws/hcls-aiml/en-US/breast-cancer-classification) on how to use SageMaker Studio for a variety of AI/ML use cases including applying a classifier to RNAseq data, classifying tabular breast cancer data, buiding graph neural nets on HIV data, training a medical imaging model on chest scans, summarize scientific literature using foundation models, MLOps using gene expression data, and finally, performing antibody structure prediction. 
 + AWS has a very general tutorial [here](https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/) on how to build out an AI pipeline on SageMaker.
 + These [general examples](https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_applying_machine_learning) will teach you how to use Sagemaker tools more broadly.

tutorials/notebooks/GenAI/Pubmed_chatbot.ipynb

@@ -734,7 +734,7 @@
    "id": "1abcbd48-bb84-4310-b8eb-ad87850a8649",
    "metadata": {},
    "source": [
-    "Running our script in the terminal will require us to export the following global variables then running our python script."
+    "Running our script in the terminal will require us to export the following global variables then running our python script. Dont forget to run you python script on the terminal use the command `python NAME_OF_YOUR_SCRIPT.py`. For more guidence take a look at our **example inference scripts** for the [PubMed API](/example_scripts/langchain_chat_llama_2_zeroshot.py) and [Kendra](/example_scripts/kendra_chat_llama_2.py)."
    ]
   },
   {