diff --git a/docs/sagemaker/inference.md b/docs/sagemaker/inference.md
index c7ff4df6d..b5c5f7fc4 100644
--- a/docs/sagemaker/inference.md
+++ b/docs/sagemaker/inference.md
@@ -346,25 +346,50 @@ The `inference.py` file contains your custom inference module, and the `requirem
 Here is an example of a custom inference module with `model_fn`, `input_fn`, `predict_fn`, and `output_fn`:  
 
 ```python
+from sagemaker_huggingface_inference_toolkit import decoder_encoder
+
 def model_fn(model_dir):
-    return "model"
+    # implement custom code to load the model
+    loaded_model = ...
+    
+    return loaded_model 
 
-def input_fn(data, content_type):
-    return "data"
+def input_fn(input_data, content_type):
+    # decode the input data  (e.g. JSON string -> dict)
+    data = decoder_encoder.decode(input_data, content_type)
+    return data
 
 def predict_fn(data, model):
-    return "output"
+    # call your custom model with the data
+    outputs = model(data , ... )
+    return predictions
 
 def output_fn(prediction, accept):
-    return prediction
+    # convert the model output to the desired output format (e.g. dict -> JSON string)
+    response = decoder_encoder.encode(prediction, accept)
+    return response
 ```
 
 Customize your inference module with only `model_fn` and `transform_fn`:   
 
 ```python
+from sagemaker_huggingface_inference_toolkit import decoder_encoder
+
 def model_fn(model_dir):
-    return "loading model"
+    # implement custom code to load the model
+    loaded_model = ...
+    
+    return loaded_model 
 
 def transform_fn(model, input_data, content_type, accept):
-    return f"output"
+     # decode the input data (e.g. JSON string -> dict)
+    data = decoder_encoder.decode(input_data, content_type)
+
+    # call your custom model with the data
+    outputs = model(data , ... ) 
+
+    # convert the model output to the desired output format (e.g. dict -> JSON string)
+    response = decoder_encoder.encode(output, accept)
+
+    return response
 ```