Picked up orphaned feedback on closed PR

Author: HeidiSteen

articles/search/search-faceted-navigation-examples.md

@@ -16,6 +16,8 @@ ms.date: 03/31/2025
 
 This section extends [faceted navigation configuration](search-faceted-navigation.md) with examples that demonstrate basic usage and other scenarios.
 
+Facetable fields are defined in an index, but facet parameters and expressions are defined in query requests. If you have an index with facetable fields, you can try new features like [facet hierarchs](#facet-hierarchy-example) and [aggregations](#facet-aggregation-example) on existing indexes.
+
 ## Facet parameters and syntax
 
 Depending on the API, a facet query is usually an array of facet expressions that are applied to search results. Each facet expression contains a facetable field name, optionally followed by a comma-separated list of name-value pairs.

articles/search/search-faceted-navigation.md

@@ -121,7 +121,7 @@ Facets can be calculated over single-value fields and collections. Fields that w
 * Low cardinality (a few distinct values that repeat throughout documents in your search corpus).
 * Short descriptive values (one or two words) that render nicely in a navigation tree.
 
-The values within a field, and not the field name itself, produce the facets in a faceted navigation structure. If the facet is a string field named *Color*, facets are blue, green, and any other value for that field. As a best practice, review field values to ensure there are no typos, nulls, or casing differences. Consider [assigning a normalizer](search-normalizers.md) to a "filterable" and "facetable" field to smooth out minor variations in the text.
+The values within a field, and not the field name itself, produce the facets in a faceted navigation structure. If the facet is a string field named *Color*, facets are blue, green, and any other value for that field. As a best practice, review field values to ensure there are no typos, nulls, or casing differences. Consider [assigning a normalizer](search-normalizers.md) to a filterable and facetable field to smooth out minor variations in the text. For example, "Canada", "CANADA", and "canada" would all be normalized to one bucket.
 
 You can't set facets on existing fields, on vector fields, or fields of type `Edm.GeographyPoint` or `Collection(Edm.GeographyPoint)`.
 

articles/search/vector-search-how-to-chunk-documents.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/11/2025
+ms.date: 03/31/2025
 ---
 
 # Chunk large documents for vector search solutions in Azure AI Search
@@ -20,7 +20,9 @@ We recommend [integrated vectorization](vector-search-integrated-vectorization.m
 
 ## Common chunking techniques
 
-Chunking is only required if the source documents are too large for the maximum input size imposed by models. Here are some common chunking techniques, associated with built-in features if you use [indexers](search-indexer-overview.md) and [skills](cognitive-search-working-with-skillsets.md).
+Chunking is only required if the source documents are too large for the maximum input size imposed by models, but it's also beneficial if content is poorly represented as a single vector. Consider a wiki page that covers a lot of varied sub-topics. The entire page might be small enough to meet model input requirements, but you might get better results if you chunk at a finer grain.
+
+Here are some common chunking techniques, associated with built-in features if you use [indexers](search-indexer-overview.md) and [skills](cognitive-search-working-with-skillsets.md).
 
 | Approach | Usage | Built-in functionality |
 |----------|-------|-----------------|