clarify metrics ref doc (#51)

* clarify metrics ref doc

* clarify aggreate vs non aggregate

Author: jpetey75

references/metrics.mdx

@@ -7,47 +7,65 @@ A metric is a value that describes or summarizes features from a collection of d
 
 In Lightdash, metrics are used to summarize dimensions or, sometimes, other metrics.
 
+
 ## Adding metrics to your project using the `meta` tag.
 
-### 1. Using the column `meta` tag (Suggested)
+
+### 1. Using the column `meta` tag
 
 To add a metric to Lightdash using the `meta` tag, you define it in your dbt project under the dimension name you're trying to describe/summarize.
 
 ```yaml
 models:
-  - name: my_model
+  - name: orders_model
     columns:
-      - name: user_id # dimension name of your metric
+      - name: user_id # dimension your metric is aggregating
         meta:
           metrics:
-            num_unique_user_ids: # name of your metric
+            distinct_user_ids: # name of your metric
               type: count_distinct # metric type
-            num_user_ids:
-              type: count
+      - name: revenue # dimension your metric is aggregating
+        meta:
+          metrics:
+            sum_revenue: # name of your metric
+              type: sum # metric type
 ```
 
+
+
 Once you've got the hang of what these metrics look like, read more about the [metric types you can use below.](#metric-types)
 
+
 ### 2. Using the model `meta` tag
 
-Sometimes a metric references many columns, in these cases you can define the metric at the model level:
+Sometimes a metric references multiple columns, in these cases you can define the metric at the model level:
 
 ```yaml
-version: 2
-
 models:
-  - name: my_model
+  - name: orders_model
     meta:
       metrics:
-        num_unique_user_ids:
-          type: count_distinct
-          sql: ${TABLE}.user_id
+        revenue_per_user:
+          type: number
+          sql: ${sum_revenue} / ${distinct_user_ids}
+        sum_total_paid:
+          type: sum
+          sql: ${revenue} + ${taxes_paid}
 ```
 
+<Warning>
+  **Non-aggregate metrics** (`number`, `boolean`, etc.) can only reference other metrics in `sql:` since they are inserted directly into the generated SQL query without being wrapped in an aggregate function.
+
+  **Aggregate metrics** (`sum`, `count_distinct`, etc.) can only reference dimensions since they are wrapped in an aggregate function before being added to the generated SQL query. Wrapping an aggreate function in another aggregate function will cause an error.
+</Warning>
+
+Read on to learn more about aggregate vs non-aggregate metrics!
+
 ## Metric Categories
 
 Each metric type falls into one of these categories. The metric categories tell you whether the metric type is an aggregation and what type of fields the metric can reference:
 
+
 ### Aggregate metrics
 
 Aggregate metric types perform (surprise, surprise) aggregations. Sums and averages are examples of aggregate metrics: they are measurements summarizing a collection of data points.