diff --git a/docs/core_use_cases/analytics.md b/docs/core_use_cases/analytics.md
index 886b75618d..71b5530c03 100644
--- a/docs/core_use_cases/analytics.md
+++ b/docs/core_use_cases/analytics.md
@@ -48,7 +48,7 @@ of the map. In this case, we normalize the `people_vaccinated` by the
 `population` count of each country:
 
 ```{code-cell} ipython3
-@task(disable_deck=False)
+@task(enable_deck=True)
 def plot(df: pd.DataFrame):
     """Render a Choropleth map."""
     df["text"] = df["location"] + "<br>" + "Last updated on: " + df["date"]
diff --git a/docs/core_use_cases/machine_learning.md b/docs/core_use_cases/machine_learning.md
index 489b8b05f9..6368b0aa54 100644
--- a/docs/core_use_cases/machine_learning.md
+++ b/docs/core_use_cases/machine_learning.md
@@ -112,7 +112,7 @@ There are many ways to extend your workloads:
     {ref}`Kubeflow Pytorch<kf-pytorch-op>` and {doc}`more <_tags/DistributedComputing>` to do distributed training.
 * - **🔎 Experiment Tracking**
   - Auto-capture training logs with the {py:func}`~flytekitplugins.mlflow.mlflow_autolog`
-    decorator, which can be viewed as Flyte Decks with `@task(disable_decks=False)`.
+    decorator, which can be viewed as Flyte Decks with `@task(enable_deck=True)`.
 * - **⏩ Inference Acceleration**
   - Serialize your models in ONNX format using the {ref}`ONNX plugin <onnx>`, which
     supports ScikitLearn, TensorFlow, and PyTorch.
diff --git a/docs/flyte_fundamentals/visualizing_task_input_and_output.md b/docs/flyte_fundamentals/visualizing_task_input_and_output.md
index 487d1627c9..0390d6cf44 100644
--- a/docs/flyte_fundamentals/visualizing_task_input_and_output.md
+++ b/docs/flyte_fundamentals/visualizing_task_input_and_output.md
@@ -22,14 +22,14 @@ how to generate an HTML report from some Python object.
 
 ## Enabling Flyte decks
 
-To enable Flyte decks, simply set `disable_deck=False` in the `@task` decorator:
+To enable Flyte decks, simply set `enable_deck=True` in the `@task` decorator:
 
 ```{code-cell} ipython3
 import pandas as pd
 from flytekit import task, workflow
 
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 def iris_data() -> pd.DataFrame:
     ...
 ```
@@ -51,7 +51,7 @@ from typing import Optional
 from flytekit import task, workflow
 
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 def iris_data(
     sample_frac: Optional[float] = None,
     random_state: Optional[int] = None,
@@ -168,7 +168,7 @@ function. In the following example, we extend the `iris_data` task with:
 import flytekit
 from flytekitplugins.deck.renderer import MarkdownRenderer, BoxRenderer
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 def iris_data(
     sample_frac: Optional[float] = None,
     random_state: Optional[int] = None,
@@ -220,7 +220,7 @@ except ImportError:
     from typing_extensions import Annotated
 
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 def iris_data(
     sample_frac: Optional[float] = None,
     random_state: Optional[int] = None,