diff --git a/docs/en/models/fast-sam.md b/docs/en/models/fast-sam.md
index 902ab8397f..89166563e0 100644
--- a/docs/en/models/fast-sam.md
+++ b/docs/en/models/fast-sam.md
@@ -81,19 +81,19 @@ To perform object detection on an image, use the `predict` method as shown below
         prompt_process = FastSAMPrompt(source, everything_results, device="cpu")
 
         # Everything prompt
-        ann = prompt_process.everything_prompt()
+        results = prompt_process.everything_prompt()
 
         # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
-        ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+        results = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
 
         # Text prompt
-        ann = prompt_process.text_prompt(text="a photo of a dog")
+        results = prompt_process.text_prompt(text="a photo of a dog")
 
         # Point prompt
         # points default [[0,0]] [[x1,y1],[x2,y2]]
         # point_label default [0] [1,0] 0:background, 1:foreground
-        ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
-        prompt_process.plot(annotations=ann, output="./")
+        results = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
+        prompt_process.plot(annotations=results, output="./")
         ```
 
     === "CLI"
@@ -105,6 +105,10 @@ To perform object detection on an image, use the `predict` method as shown below
 
 This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
 
+!!! Note
+
+    All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
+
 ### Val Usage
 
 Validation of the model on a dataset can be done as follows:
diff --git a/docs/en/models/sam.md b/docs/en/models/sam.md
index 629a83ea99..b3301700ba 100644
--- a/docs/en/models/sam.md
+++ b/docs/en/models/sam.md
@@ -56,10 +56,10 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
         model.info()
 
         # Run inference with bboxes prompt
-        model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
+        results = model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
 
         # Run inference with points prompt
-        model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
+        results = model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
         ```
 
 !!! Example "Segment everything"
@@ -128,6 +128,10 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
         results = predictor(source="ultralytics/assets/zidane.jpg", crop_n_layers=1, points_stride=64)
         ```
 
+!!! Note
+
+    All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
+
 - More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
 
 ## SAM comparison vs YOLOv8