diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index ffe5ed3fc..a6b551508 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -946,6 +946,26 @@ def from_lmm(cls, lmm: LMM | str, result: str | dict, **kwargs: Any) -> Detectio
             ```
 
         !!! example "Gemini 2.0"
+
+            ??? tip "Prompt engineering"
+
+                From Gemini 2.0 onwards, models are further trained to detect objects in
+                an image and get their bounding box coordinates. The coordinates,
+                relative to image dimensions, scale to [0, 1000]. You need to descale
+                these coordinates based on your original image size.
+
+                According to the Gemini API documentation on image prompts, when using
+                a single image with text, the recommended approach is to place the text
+                prompt after the image part in the contents array. This ordering has
+                been shown to produce significantly better results in practice.
+
+                To get the best results from Google Gemini 2.0, use the following prompt.
+
+                ```
+                Detect all the cats and dogs in the image. The box_2d should be
+                [ymin, xmin, ymax, xmax] normalized to 0-1000.
+                ```
+
             ```python
             import supervision as sv
 
@@ -983,6 +1003,11 @@ def from_lmm(cls, lmm: LMM | str, result: str | dict, **kwargs: Any) -> Detectio
                 including small, distant, or partially visible ones, and to return
                 tight bounding boxes.
 
+                According to the Gemini API documentation on image prompts, when using
+                a single image with text, the recommended approach is to place the text
+                prompt after the image part in the contents array. This ordering has
+                been shown to produce significantly better results in practice.
+
                 ```
                 Carefully examine this image and detect ALL visible objects, including
                 small, distant, or partially visible ones.
@@ -1323,6 +1348,26 @@ def from_vlm(cls, vlm: VLM | str, result: str | dict, **kwargs: Any) -> Detectio
             ```
 
         !!! example "Gemini 2.0"
+
+            ??? tip "Prompt engineering"
+
+                From Gemini 2.0 onwards, models are further trained to detect objects in
+                an image and get their bounding box coordinates. The coordinates,
+                relative to image dimensions, scale to [0, 1000]. You need to descale
+                these coordinates based on your original image size.
+
+                According to the Gemini API documentation on image prompts, when using
+                a single image with text, the recommended approach is to place the text
+                prompt after the image part in the contents array. This ordering has
+                been shown to produce significantly better results in practice.
+
+                To get the best results from Google Gemini 2.0, use the following prompt.
+
+                ```
+                Detect all the cats and dogs in the image. The box_2d should be
+                [ymin, xmin, ymax, xmax] normalized to 0-1000.
+                ```
+
             ```python
             import supervision as sv
 
@@ -1360,6 +1405,11 @@ def from_vlm(cls, vlm: VLM | str, result: str | dict, **kwargs: Any) -> Detectio
                 including small, distant, or partially visible ones, and to return
                 tight bounding boxes.
 
+                According to the Gemini API documentation on image prompts, when using
+                a single image with text, the recommended approach is to place the text
+                prompt after the image part in the contents array. This ordering has
+                been shown to produce significantly better results in practice.
+
                 ```
                 Carefully examine this image and detect ALL visible objects, including
                 small, distant, or partially visible ones.