diff --git a/docs/user_guide/advanced_composition/map_tasks.md b/docs/user_guide/advanced_composition/map_tasks.md
index c57f37b95b..26330a8cd5 100644
--- a/docs/user_guide/advanced_composition/map_tasks.md
+++ b/docs/user_guide/advanced_composition/map_tasks.md
@@ -67,10 +67,14 @@ If the input size exceeds the concurrency value, multiple batches will run seria
 
 ```python
 @workflow
-def map_workflow_with_additional_params(data: list[int] = [10, 12, 11, 10, 13, 12, 100, 11, 12, 10]) -> list[bool]:
+def map_workflow_with_additional_params(data: list[int] = [10, 12, 11, 10, 13, 12, 100, 11, 12, 10]) -> list[typing.Optional[bool]]:
     return map_task(detect_anomalies, concurrency=1, min_success_ratio=0.75)(data_point=data)
 ```
 
+:::{note}
+Notice the return type of the list has been set to `Optional` when a `min_success_ratio` is added. This is due to the fact we are now tolerating failures, meaning the expected return type from the mapped task may in fact not get returned.
+:::
+
 A map task internally uses a compression algorithm (bitsets) to handle every Flyte workflow node’s metadata,
 which would have otherwise been in the order of 100s of bytes.
 
@@ -162,30 +166,14 @@ pyflyte run --remote \
 
 ## ArrayNode
 
-ArrayNode map tasks serve as a seamless substitution for regular map tasks, differing solely in the submodule
-utilized to import the `map_task` function. Specifically, you will need to import `map_task` from the experimental module as illustrated below:
-
-```python
-from flytekit import task, workflow
-from flytekit.experimental import map_task
-
-@task
-def t(a: int) -> int:
-    ...
-
-@workflow
-def array_node_wf(xs: list[int]) -> list[int]:
-    return map_task(t)(a=xs)
-```
-
-Flyte introduces map task to enable parallelization of homogeneous operations,
+Flyte originally introduced map tasks to enable parallelization of homogeneous operations,
 offering efficient evaluation and a user-friendly API. Because it’s implemented as a backend plugin,
 its evaluation is independent of core Flyte logic, which generates subtask executions that lack full Flyte functionality.
-ArrayNode tackles this issue by offering robust support for subtask executions.
+ArrayNode tackled this issue by offering robust support for subtask executions.
 It also extends mapping capabilities across all plugins and Flyte node types.
-This enhancement will be a part of our move from the experimental phase to general availability.
+Starting with `flytekit` version 1.12.0, ArrayNode is the default `map_task` importable via `from flytekit import map_task`.
 
-In contrast to map tasks, an ArrayNode provides the following enhancements:
+In contrast to the original map tasks, an ArrayNode provides the following enhancements:
 
 - **Wider mapping support**. ArrayNode extends mapping capabilities beyond Kubernetes tasks, encompassing tasks such as Python tasks, container tasks and pod tasks.
 - **Cache management**. It supports both cache serialization and cache overwriting for subtask executions.