diff --git a/docs/design.md b/docs/design.md
index a82bc9716..e8e6e0091 100644
--- a/docs/design.md
+++ b/docs/design.md
@@ -87,6 +87,24 @@ operations. Additionally, you can create a layer with a high degree of
 complexity supporting batch circuit, batch control parameter value, and perform
 batch readout operations. See `tfq.layers.Sample` for an example.
 
+#### Saving and loading models
+
+Saving a complete Keras model with `model.save()` is not supported for models containing TFQ layers, including `tfq.layers.PQC` and `tfq.layers.ControlledPQC`. These layers accept quantum objects, such as `cirq.Circuit`, `cirq.PauliSum`, and `sympy.Symbol`, that are not
+JSON-serializable, and the layers do not implement `get_config()` to serialize their configurations.
+
+Depending on the serialization path, calling `model.save()` may raise a `NotImplementedError` indicating that the layer must override
+`get_config()`, a `TypeError` for non-JSON-serializable `call()`
+arguments such as `sympy.Symbol` or `cirq.PauliSum`, or a `NotEncodableError` when tracing layer `call` defaults for `SavedModel` export. This limitation is not specific to a particular TFQ, TensorFlow, or Keras version.
+
+As an alternative, `model.save_weights()` saves the model's weights without saving the complete model configuration. Recreate the same model architecture before loading the saved weights:
+
+```python
+model.save_weights("my_model.weights.h5")
+
+new_model = build_model()  # Recreate the same model architecture.
+new_model.load_weights("my_model.weights.h5")
+```
+
 ### Differentiators
 
 Unlike many TensorFlow operations, observables in quantum circuits do not have