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-# Bedrock Mistral prompting examples 🪨🌊
+# Bedrock Mistral prompting examples
 
 ## Getting Started 🚀
 
@@ -8,7 +8,7 @@ The notebooks are executed from SageMaker studio with Data Science 3.0 image. 
 
 ## Introduction 🌍
 
-Welcome to the Bedrock Mistral prompting examples repository! This collection aims to provide you with a wide range of prompting examples and techniques to enhance your experience with the Mistral language model. Whether you're a beginner or an experienced user, these examples will help you unlock the full potential of this powerful tool. 💡
+Welcome to the Bedrock Mistral prompting examples repository! This collection aims to provide you with a wide range of prompting examples and techniques to enhance your experience with the Mistral language model. Whether you're a beginner or an experienced user, these examples will help you in your generative AI journey. 💡
 
 ## Resources 📚
 
diff --git a/docs/notebooks.md b/docs/notebooks.md
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 - **Summarizing long documents with LangChain**: 📚 This Python notebook explores summarizing long documents using the Mistral Large language model on Amazon Bedrock with the LangChain library. It covers three main summarization techniques: Stuff (passing the entire document to the model in a single call), Map Reduce (a scalable technique, splitting the document into chunks, summarizing each chunk in parallel, and combining these summaries), and Refine (an iterative approach, generating an initial summary and refining it with additional context from subsequent chunks). The notebook includes detailed code examples for each technique using LangChain's utilities and chains. It demonstrates loading PDF documents, splitting them into text chunks, and customizing prompt templates. Additionally, it showcases the Mistral model's multilingual capabilities by generating a summary in French.
 
 - **Multi-chain Routing in LangChain**: 🔀 This notebook demonstrates the use of multi-chain routing in LangChain, a Python library for building applications with large language models (LLMs) and integrating different Mistral AI models from Amazon Bedrock (Mistral Large, Mistral 7B, and Mixtral 8X7B). The notebook explores a use case in the Financial Services Industry (FSI), where a user can query information about their investments, retrieve financial reports, and search for relevant news articles using a single pipeline.
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