Merge pull request #262713 from laujan/patch-2

Update concept-retrieval-augumented-generation.md

Author: prmerger-automator[bot]

articles/ai-services/document-intelligence/concept-retrieval-augumented-generation.md

@@ -21,7 +21,7 @@ monikerRange: '>=doc-intel-3.1.0'
 
 Retrieval-Augmented Generation (RAG) is a design pattern that combines a pretrained Large Language Model (LLM) like ChatGPT with an external data retrieval system to generate an enhanced response incorporating new data outside of the original training data. Adding an information retrieval system to your applications enables you to chat with your documents, generate captivating content, and access the power of Azure OpenAI models for your data. You also have more control over the data used by the LLM as it formulates a response.
 
-The Document Intelligence [Layout model](concept-layout.md) is an advanced machine-learning based document analysis API. The Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on the provided building blocks.
+The Document Intelligence [Layout model](concept-layout.md) is an advanced machine-learning based document analysis API. The Layout model offers a comprehensive solution for advanced content extraction and document structure analysis capabilities. With the Layout model, you can easily extract text and structural elements to divide large bodies of text into smaller, meaningful chunks based on semantic content rather than arbitrary splits. The extracted information can be conveniently outputted to Markdown format, enabling you to define your semantic chunking strategy based on provided building blocks.
 
 :::image type="content" source="media/rag/azure-rag-processing.png" alt-text="Screenshot depicting semantic chunking with RAG using Azure AI Document Intelligence.":::
 
@@ -31,9 +31,9 @@ Long sentences are challenging for natural language processing (NLP) application
 
 Text data chunking strategies play a key role in optimizing the RAG response and performance. Fixed-sized and semantic are two distinct chunking methods:
 
-* **Fixed-sized chunking**. Most chunking strategies used in RAG today are based on fix-sized text segments known as chunks. Fixed-sized chunking is quick, easy, and effective with text that doesn't have a strong semantic structure such as logs and data. However it isn't recommended for text that requires semantic understanding and precise context. The fixed-size nature of the window can result in severing words, sentences, or paragraphs impeding comprehension and disrupt the flow of information and understanding.
+* **Fixed-sized chunking**. Most chunking strategies used in RAG today are based on fix-sized text segments known as chunks. Fixed-sized chunking is quick, easy, and effective with text that doesn't have a strong semantic structure such as logs and data. However it isn't recommended for text that requires semantic understanding and precise context. The fixed-size nature of the window can result in severing words, sentences, or paragraphs impeding comprehension and disrupting the flow of information and understanding.
 
-* **Semantic chunking**. This method divides the text into chunks based on semantic understanding. Division boundaries are focused on sentence subject and use significant computational algorithmically complex resources. However, it has the distinct advantage of maintaining semantic consistency within each chunk.  It's useful for text summarization, sentiment analysis, and document classification tasks.
+* **Semantic chunking**. This method divides the text into chunks based on semantic understanding. Division boundaries are focused on sentence subject and use significant computational algorithmically-complex resources. However, it has the distinct advantage of maintaining semantic consistency within each chunk.  It's useful for text summarization, sentiment analysis, and document classification tasks.
 
 ## Semantic chunking with Document Intelligence Layout model
 
@@ -43,11 +43,11 @@ Markdown is a structured and formatted markup language and a popular input for e
 
 * **Simplified processing**. You can parse different document types, such as digital and scanned PDFs, images, office files (docx, xlsx, pptx), and HTML, with just a single API call.
 
-* **Scalability and AI quality**. The Layout model is highly scalable in Optical Character Recognition (OCR), table extraction, and [document structure analysis](concept-layout.md#document-layout-analysis). It supports [309 printed and 12 handwritten languages](language-support-ocr.md#model-id-prebuilt-layout) further ensuring high-quality results driven by AI capabilities.
+* **Scalability and AI quality**. The Layout model is highly scalable in Optical Character Recognition (OCR), table extraction, and [document structure analysis](concept-layout.md#document-layout-analysis). It supports [309 printed and 12 handwritten languages](language-support-ocr.md#model-id-prebuilt-layout), further ensuring high-quality results driven by AI capabilities.
 
 * **Large learning model (LLM) compatibility**. The Layout model Markdown formatted output is LLM friendly and facilitates seamless integration into your workflows. You can turn any table in a document into Markdown format and avoid extensive effort parsing the documents for greater LLM understanding.
 
-**Text image processed with Document Intelligence Studio and output to markdown using Layout model**
+**Text image processed with Document Intelligence Studio and output to MarkDown using Layout model**
 
   :::image type="content" source="media/rag/markdown-text-output.png" alt-text="Screenshot of newspaper article processed by Layout model and outputted to Markdown.":::
 
@@ -109,7 +109,7 @@ You can follow the [Document Intelligence Studio quickstart](quickstarts/try-doc
 
 * [Azure OpenAI on your data](../openai/concepts/use-your-data.md) enables you to run supported chat on your documents.  Azure OpenAI on your data applies the Document Intelligence Layout model to extract and parse document data by chunking long text based on tables and paragraphs. You can also customize your chunking strategy using [Azure OpenAI sample scripts](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts) located in our GitHub repo.
 
-* Azure AI Document Intelligence is now integrated with [LangChain](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence) as one of its document loaders. You can use it to easily load the data and output to Markdown format. This [notebook](https://github.com/microsoft/Form-Recognizer-Toolkit/blob/main/SampleCode/Python/sample_rag_langchain.ipynb) shows a simple demo for RAG pattern with Azure AI Document Intelligence as document loader and Azure Search as retriever in LangChain.
+* Azure AI Document Intelligence is now integrated with [LangChain](https://python.langchain.com/docs/integrations/document_loaders/azure_document_intelligence) as one of its document loaders. You can use it to easily load the data and output to Markdown format. For more information, see our [sample code ](https://github.com/microsoft/Form-Recognizer-Toolkit/blob/main/SampleCode/Python/sample_rag_langchain.ipynb) that shows a simple demo for RAG pattern with Azure AI Document Intelligence as document loader and Azure Search as retriever in LangChain.
 
 * The chat with your data solution accelerator [code sample](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) demonstrates an end-to-end baseline RAG pattern sample. It uses Azure AI Search as a retriever and Azure AI Document Intelligence for document loading and semantic chunking.