diff --git a/website/docs/docs/build/measures.md b/website/docs/docs/build/measures.md
index 1901c7d05b7..977b630fada 100644
--- a/website/docs/docs/build/measures.md
+++ b/website/docs/docs/build/measures.md
@@ -102,7 +102,7 @@ semantic_models:
     description: A record of every transaction that takes place. Carts are considered  multiple transactions for each SKU.
     model: ref('schema.transactions')
     defaults:
-      agg_time_dimension: metric_time
+      agg_time_dimension: transaction_date
 
 # --- entities ---
     entities:
@@ -167,7 +167,7 @@ semantic_models:
         
 # --- dimensions ---
     dimensions:
-      - name: metric_time
+      - name: transaction_date
         type: time
         expr: date_trunc('day', ts) # expr refers to underlying column ts
         type_params:
@@ -204,7 +204,7 @@ semantic_models:
     description: A subscription table with one row per date for each active user and their subscription plans. 
     model: ref('your_schema.subscription_table')
     defaults:
-      agg_time_dimension: metric_time 
+      agg_time_dimension: subscription_date
 
     entities:
       - name: user_id
@@ -212,7 +212,7 @@ semantic_models:
         primary_entity: subscription_table
 
     dimensions:
-      - name: metric_time
+      - name: subscription_date
         type: time
         expr: date_transaction
         type_params:
diff --git a/website/docs/docs/build/metricflow-commands.md b/website/docs/docs/build/metricflow-commands.md
index d010cd8bb17..466a61cc5bc 100644
--- a/website/docs/docs/build/metricflow-commands.md
+++ b/website/docs/docs/build/metricflow-commands.md
@@ -34,10 +34,22 @@ For dbt Cloud CLI users, MetricFlow commands are embedded in the dbt Cloud CLI,
 
 You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install MetricFlow on Windows or Linux operating systems:
 
+<VersionBlock lastVersion="1.7">
+ 
 1. Create or activate your virtual environment `python -m venv venv`
 2. Run `pip install dbt-metricflow`
   * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `python -m pip install "dbt-metricflow[your_adapter_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `python -m pip install "dbt-metricflow[snowflake]"`
 
+</VersionBlock>
+
+<VersionBlock firstVersion="1.8">
+ 
+1. Create or activate your virtual environment `python -m venv venv`
+2. Run `pip install dbt-metricflow`
+  * You can install MetricFlow using PyPI as an extension of your dbt adapter in the command line. To install the adapter, run `python -m pip install "dbt-metricflow[adapter_package_name]"` and add the adapter name at the end of the command. For example, for a Snowflake adapter run `python -m pip install "dbt-metricflow[dbt-snowflake]"`
+
+</VersionBlock>
+
 **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow.
 
 Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package.
diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index e932fb36f53..9932a35839c 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -463,9 +463,22 @@ For example, if you use a custom calendar in your organization, such as a fiscal
 
 - This is useful for calculating metrics based on a custom calendar, such as fiscal quarters or weeks. 
 - Use the `custom_granularities` key to define a non-standard time period for querying data, such as a `retail_month` or `fiscal_week`, instead of standard options like `day`, `month`, or `year`.
-- Ensure the the `standard_granularity_column` is a date time type.
 - This feature provides more control over how time-based metrics are calculated.
 
+<Expandable alt_header="Data types and time zone considerations">
+ 
+When working with custom calendars in MetricFlow, it's important to ensure:
+
+- Consistent data types &mdash; Both your dimension column and the time spine column should use the same data type to allow accurate comparisons. Functions like `DATE_TRUNC` don't change the data type of the input in some databases (like Snowflake). Using different data types can lead to mismatches and inaccurate results.
+
+  We recommend using `DATETIME` or `TIMESTAMP` data types for your time dimensions and time spine, as they support all granularities. The `DATE` data type may not support smaller granularities like hours or minutes.
+
+- Time zones &mdash; MetricFlow currently doesn't perform any timezone manipulation. When working with timezone-aware data, inconsistent time zones may lead to unexpected results during aggregations and comparisons.
+
+For example, if your time spine column is `TIMESTAMP` type and your dimension column is `DATE` type, comparisons between these columns might not work as intended. To fix this, convert your `DATE` column to `TIMESTAMP`, or make sure both columns are the same data type.
+
+</Expandable>
+
 ### Add custom granularities
 
 To add custom granularities, the Semantic Layer supports custom calendar configurations that allow users to query data using non-standard time periods like `fiscal_year` or `retail_month`. You can define these custom granularities (all lowercased) by modifying your model's YAML configuration like this: