acrolinx fixes

Author: HeidiSteen

articles/search/tutorial-rag-build-solution-index-schema.md

@@ -73,8 +73,8 @@ In Azure AI Search, an index that works best for RAG workloads has these qualiti
 
 Although Azure AI Search can't join indexes, you can create indexes that preserve parent-child relationship, and then use sequential or parallel queries in your search logic to pull from both. This exercise includes templates for parent-child elements in the same index and in separate indexes, where information from the parent index is retrieved using a lookup query.
 
-> [!NOTE]
-> Schema design affects storage and costs. This exercise is focused on schema fundamentals. In the [Minimize storage and costs](tutorial-rag-build-solution-minimize-storage.md) tutorial, you revisit schema design to consider narrow data types, attribution, and vector configurations that offer more efficient.
+<!-- > [!NOTE]
+> Schema design affects storage and costs. This exercise is focused on schema fundamentals. In the [Minimize storage and costs](tutorial-rag-build-solution-minimize-storage.md) tutorial, you revisit schema design to consider narrow data types, attribution, and vector configurations that offer more efficient. -->
 
 ## Create an index for RAG workloads
 
@@ -278,7 +278,7 @@ A minimal index for LLM is designed to store chunks of content. It typically inc
 
    Fields in the child index:
 
-   - id
+   - ID
    - chunk
    - chunkVectcor
    - parent_id

articles/search/tutorial-rag-build-solution-pipeline.md

@@ -46,7 +46,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
 ## Provide the index schema
 
-Open or create a Jupyter notebook (.ipynb) in Visual Studio code to contain the scripts that comprise the pipeline. Initial steps install packages and collect variables for the connections. After you complete the set up steps, you're ready to begin with the components of the indexing pipeline. 
+Open or create a Jupyter notebook (`.ipynb`) in Visual Studio Code to contain the scripts that comprise the pipeline. Initial steps install packages and collect variables for the connections. After you complete the setup steps, you're ready to begin with the components of the indexing pipeline. 
 
 Let's start with the index schema from the [previous tutorial](tutorial-rag-build-solution-index-schema.md). It's organized around vectorized and nonvectorized chunks. It includes a `locations` field that stores AI-generated content created by the skillset.  
 
@@ -309,7 +309,7 @@ www.nasa.gov
 np-2018-05-2546-hQ
 ```
 
-Try a few more queries to get a sense of what the search engine returns directly so that you can compare it with an LLM-enabled response. Re-run the previous script with this query: "how much of the earth is covered in water"?
+Try a few more queries to get a sense of what the search engine returns directly so that you can compare it with an LLM-enabled response. Rerun the previous script with this query: "how much of the earth is covered in water"?
 
 Results from this second query should look similar to the following results, which are lightly edited for concision.