typo

Author: JamesJBarnett

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

@@ -63,7 +63,7 @@ In Azure AI Search, an index that works best for RAG workloads has these qualiti
 
 - Accommodates the queries you want create. You should have fields for vector and hybrid content, and those fields should be attributed to support specific query behaviors, such as searchable or filterable. You can only query one index at a time (no joins) so your fields collection should define all of your searchable content.
 
-- Your schema should either be flat (no complex types or structures), or you should [format the complext type utput as JSON](search-get-started-rag.md#send-a-complex-rag-query) before sending it to the LLM. This requirement is specific to the RAG pattern in Azure AI Search.
+- Your schema should either be flat (no complex types or structures), or you should [format the complext type output as JSON](search-get-started-rag.md#send-a-complex-rag-query) before sending it to the LLM. This requirement is specific to the RAG pattern in Azure AI Search.
 
 <!-- Although Azure AI Search can't join indexes, you can create indexes that preserve parent-child relationship, and then use sequential queries in your search logic to pull from both (a query on the chunked data index, a lookup on the parent index). 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. -->
 
@@ -209,4 +209,4 @@ Tasks:
 ## Next step
 
 > [!div class="nextstepaction"]
-> [Create an indexing pipeline](tutorial-rag-build-solution-pipeline.md)
+> [Create an indexing pipeline](tutorial-rag-build-solution-pipeline.md)