Reverse order of RRF examples

Author: Mikep86

solutions/search/retrievers-examples.md

@@ -418,18 +418,25 @@ stack: ga 9.1
 
 There's an even simpler way to execute a hybrid search though: We can use the [multi-field query format](elasticsearch://reference/elasticsearch/rest-apis/retrievers.md#multi-field-query-format), which allows us to query multiple fields without explicitly specifying inner retrievers.
 
-The following example uses the multi-field query format to query the `text` and `text_semantic` fields.
+One of the major challenges with hybrid search is normalizing the scores across matches on all field types.
 Scores from [`text`](elasticsearch://reference/elasticsearch/mapping-reference/text.md) and [`semantic_text`](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md) fields don't always fall in the same range, so we need to normalize the ranks across matches on these fields to generate a result set.
 For example, BM25 scores from `text` fields are unbounded, while vector similarity scores from `text_embedding` models are bounded between [0, 1].
 The multi-field query format [handles this normalization for us automatically](elasticsearch://reference/elasticsearch/rest-apis/retrievers.md#multi-field-field-grouping).
 
+The following example uses the multi-field query format to query every field specified in the `index.query.default_field` index setting, which is set to `*` by default.
+This default value will cause the retriever to query every field that either:
+
+- Supports term queries, such as `keyword` and `text` fields
+- Is a `semantic_text` field
+
+In this example, that would translate to the `text`, `text_semantic`, `year`, `topic`, and `timestamp` fields.
+
 ```console
 GET /retrievers_example/_search
 {
     "retriever": {
         "rrf": {
-            "query": "artificial intelligence",
-            "fields": ["text", "text_semantic"]
+            "query": "artificial intelligence"
         }
     }
 }     
@@ -477,26 +484,25 @@ This returns the following response based on the final rrf score for each result
 
 ::::
 
-We don't even need to specify the `fields` parameter when using the multi-field query format.
-If we omit it, the retriever will automatically query fields specified in the `index.query.default_field` index setting, which is set to `*` by default.
-This default value will cause the retriever to query every field that either:
-
-- Supports term queries, such as `keyword` and `text` fields
-- Is a `semantic_text` field
-
-In this example, that would translate to the `text`, `text_semantic`, `year`, `topic`, and `timestamp` fields.
+We can also use the `fields` parameter to explicitly specify the fields to query.
+The following example uses the multi-field query format to query the `text` and `text_semantic` fields.
 
 ```console
 GET /retrievers_example/_search
 {
     "retriever": {
         "rrf": {
-            "query": "artificial intelligence"
+            "query": "artificial intelligence",
+            "fields": ["text", "text_semantic"]
         }
     }
 }     
 ```
 
+::::{note}
+The `fields` parameter also accepts [wildcard field patterns](elasticsearch://reference/elasticsearch/rest-apis/retrievers.md#multi-field-wildcard-field-patterns).
+::::
+
 This returns the following response based on the final rrf score for each result.
 
 ::::{dropdown} Example response
@@ -539,8 +545,6 @@ This returns the following response based on the final rrf score for each result
 
 ::::
 
-See [wildcard field patterns](elasticsearch://reference/elasticsearch/rest-apis/retrievers.md#multi-field-wildcard-field-patterns) for more information about wildcard resolution.
-
 
 ## Example: Linear retriever with the multi-field query format [retrievers-examples-linear-multi-field-query-format]
 ```yaml {applies_to}