Merge pull request #299 from HeidiSteen/heidist-rag

[azure search] added troubleshooting steps to RAG quickstart

Author: prmerger-automator[bot]

articles/search/search-get-started-rag.md

@@ -6,7 +6,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: quickstart
-ms.date: 08/18/2024
+ms.date: 09/16/2024
 ---
 
 # Quickstart: Generative search (RAG) with grounding data from Azure AI Search
@@ -262,9 +262,11 @@ This section uses Visual Studio Code and Python to call the chat completion APIs
     Several other hotels have views and water features, but do not offer beach access or views of the ocean.
     ```
 
-    If you get an authorization error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
+    If you get a **Forbidden** error message, check Azure AI Search configuration to make sure role-based access is enabled.
 
-    To experiment further, change the query and rerun the last step to better understand how the model works with the grounding data.
+    If you get an **Authorization failed** error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational.
+
+    Otherwise, to experiment further, change the query and rerun the last step to better understand how the model works with the grounding data.
 
     You can also modify the prompt to change the tone or structure of the output.
 

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

@@ -43,7 +43,7 @@ Chunks are the focus of the schema, and each chunk is the defining element of a
 
 ### Enhanced with generated data
 
-In this tutorial, sample data consists of PDFs and content from the [NASA Earth Book](https://www.nasa.gov/ebooks/earth/). This content is descriptive and informative, with numerous references to geographies, countries, and areas across the world. All of the textual content is captured in chunks, but these recurring instances of place names create an opportunity for adding structure to the index. Using skills, it's possible to recognize entities in the text and capture them in an index for use in queries and filters. In this tutorial, we include an [entity recognition skill](cognitive-search-skill-entity-recognition-v3.md) that recognizes and extracts location entities, loading it into a searchable and filterable `locations` field. Adding structured content to your index gives you more options for filtering, improved relevance, and richer answers.
+In this tutorial, sample data consists of PDFs and content from the [NASA Earth Book](https://www.nasa.gov/ebooks/earth/). This content is descriptive and informative, with numerous references to geographies, countries, and areas across the world. All of the textual content is captured in chunks, but these recurring instances of place names create an opportunity for adding structure to the index. Using skills, it's possible to recognize entities in the text and capture them in an index for use in queries and filters. In this tutorial, we include an [entity recognition skill](cognitive-search-skill-entity-recognition-v3.md) that recognizes and extracts location entities, loading it into a searchable and filterable `locations` field. Adding structured content to your index gives you more options for filtering, improved relevance, and more focused answers.
 
 ### Parent-child fields in one or two indexes?