diff --git a/tests/unit/acquisition/function/test_continuous_thompson_sampling.py b/tests/unit/acquisition/function/test_continuous_thompson_sampling.py
index 8110e33103..53cbeb7db0 100644
--- a/tests/unit/acquisition/function/test_continuous_thompson_sampling.py
+++ b/tests/unit/acquisition/function/test_continuous_thompson_sampling.py
@@ -42,9 +42,7 @@ class DumbTrajectorySampler(RandomFourierFeatureTrajectorySampler):
     """A RandomFourierFeatureTrajectorySampler that doesn't update trajectories in place."""
 
     def update_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunction:
-        tf.debugging.Assert(
-            isinstance(trajectory, feature_decomposition_trajectory), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(trajectory, feature_decomposition_trajectory), [])
         return self.get_trajectory()
 
 
diff --git a/trieste/acquisition/function/entropy.py b/trieste/acquisition/function/entropy.py
index d53cb6a61b..e291e8d461 100644
--- a/trieste/acquisition/function/entropy.py
+++ b/trieste/acquisition/function/entropy.py
@@ -128,7 +128,7 @@ def prepare_acquisition_function(
             :exc:`~tf.errors.InvalidArgumentError` if used with a batch size greater than one.
         :raise tf.errors.InvalidArgumentError: If ``dataset`` is empty.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -150,10 +150,10 @@ def update_acquisition_function(
         :param model: The model.
         :param dataset: The data from the observer.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(isinstance(function, min_value_entropy_search), [tf.constant([])])
+        tf.debugging.Assert(isinstance(function, min_value_entropy_search), [])
 
         query_points = self._search_space.sample(num_samples=self._grid_size)
         tf.debugging.assert_same_float_dtype([dataset.query_points, query_points])
@@ -334,7 +334,7 @@ def prepare_acquisition_function(
                 f"covariance_between_points and get_observation_noise; received {model!r}"
             )
 
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -363,10 +363,10 @@ def update_acquisition_function(
             for the current step. Defaults to ``True``.
         :return: The updated acquisition function.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(self._quality_term is not None, [tf.constant([])])
+        tf.debugging.Assert(self._quality_term is not None, [])
 
         if new_optimization_step:
             self._update_quality_term(dataset, model)
diff --git a/trieste/acquisition/function/function.py b/trieste/acquisition/function/function.py
index e917b84e09..81d801f5cd 100644
--- a/trieste/acquisition/function/function.py
+++ b/trieste/acquisition/function/function.py
@@ -86,7 +86,7 @@ def update_acquisition_function(
         tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(isinstance(function, probability_below_threshold), [tf.constant([])])
+        tf.debugging.Assert(isinstance(function, probability_below_threshold), [])
         mean, _ = model.predict(dataset.query_points)
         eta = tf.reduce_min(mean, axis=0)[0]
         function.update(eta)  # type: ignore
@@ -127,7 +127,7 @@ def prepare_acquisition_function(
             greater than one.
         :raise tf.errors.InvalidArgumentError: If ``dataset`` is empty.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -161,10 +161,10 @@ def update_acquisition_function(
         :param model: The model.
         :param dataset: The data from the observer.  Must be populated.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(isinstance(function, expected_improvement), [tf.constant([])])
+        tf.debugging.Assert(isinstance(function, expected_improvement), [])
 
         # Check feasibility against any explicit constraints in the search space.
         if self._search_space is not None and self._search_space.has_constraints:
@@ -251,7 +251,7 @@ def prepare_acquisition_function(
                 f"AugmentedExpectedImprovement only works with models that support "
                 f"get_observation_noise; received {model!r}"
             )
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
         mean, _ = model.predict(dataset.query_points)
@@ -269,10 +269,10 @@ def update_acquisition_function(
         :param model: The model.
         :param dataset: The data from the observer. Must be populated.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(isinstance(function, augmented_expected_improvement), [tf.constant([])])
+        tf.debugging.Assert(isinstance(function, augmented_expected_improvement), [])
         mean, _ = model.predict(dataset.query_points)
         eta = tf.reduce_min(mean, axis=0)
         function.update(eta)  # type: ignore
@@ -669,7 +669,7 @@ def prepare_acquisition_function(
         :raise KeyError: If `objective_tag` is not found in ``datasets`` and ``models``.
         :raise tf.errors.InvalidArgumentError: If the objective data is empty.
         """
-        tf.debugging.Assert(datasets is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets is not None, [])
         datasets = cast(Mapping[Tag, Dataset], datasets)
 
         objective_model = models[self._objective_tag]
@@ -719,7 +719,7 @@ def update_acquisition_function(
         :param models: The models for each tag.
         :param datasets: The data from the observer.
         """
-        tf.debugging.Assert(datasets is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets is not None, [])
         datasets = cast(Mapping[Tag, Dataset], datasets)
 
         objective_model = models[self._objective_tag]
@@ -730,7 +730,7 @@ def update_acquisition_function(
             message="Expected improvement is defined with respect to existing points in the"
             " objective data, but the objective data is empty.",
         )
-        tf.debugging.Assert(self._constraint_fn is not None, [tf.constant([])])
+        tf.debugging.Assert(self._constraint_fn is not None, [])
 
         constraint_fn = cast(AcquisitionFunction, self._constraint_fn)
         self._constraint_builder.update_acquisition_function(
@@ -777,9 +777,7 @@ def _update_expected_improvement_fn(
         if self._expected_improvement_fn is None:
             self._expected_improvement_fn = expected_improvement(objective_model, eta)
         else:
-            tf.debugging.Assert(
-                isinstance(self._expected_improvement_fn, expected_improvement), [tf.constant([])]
-            )
+            tf.debugging.Assert(isinstance(self._expected_improvement_fn, expected_improvement), [])
             self._expected_improvement_fn.update(eta)  # type: ignore
 
 
@@ -830,7 +828,7 @@ def prepare_acquisition_function(
 
         sampler = model.reparam_sampler(self._sample_size)
 
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -858,12 +856,10 @@ def update_acquisition_function(
         :param model: The model. Must have output dimension [1]. Unused here.
         :param dataset: The data from the observer. Cannot be empty
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(
-            isinstance(function, monte_carlo_expected_improvement), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(function, monte_carlo_expected_improvement), [])
         sampler = function._sampler  # type: ignore
         sampler.reset_sampler()
         samples_at_query_points = sampler.sample(
@@ -974,7 +970,7 @@ def prepare_acquisition_function(
 
         sampler = model.reparam_sampler(self._sample_size)
 
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -1002,12 +998,10 @@ def update_acquisition_function(
         :param model: The model. Must have output dimension [1]. Unused here
         :param dataset: The data from the observer. Cannot be empty.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(
-            isinstance(function, monte_carlo_augmented_expected_improvement), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(function, monte_carlo_augmented_expected_improvement), [])
         sampler = function._sampler  # type: ignore
         sampler.reset_sampler()
         samples_at_query_points = sampler.sample(
@@ -1111,7 +1105,7 @@ def prepare_acquisition_function(
         :raise ValueError (or InvalidArgumentError): If ``dataset`` is not populated, or ``model``
             does not have an event shape of [1].
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -1135,12 +1129,10 @@ def update_acquisition_function(
         :param model: The model. Must have event shape [1].
         :param dataset: The data from the observer. Must be populated.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(
-            isinstance(function, batch_monte_carlo_expected_improvement), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(function, batch_monte_carlo_expected_improvement), [])
         mean, _ = model.predict(dataset.query_points)
         eta = tf.reduce_min(mean, axis=0)
         function.update(eta)  # type: ignore
@@ -1848,9 +1840,7 @@ def update_acquisition_function(
         :param model: The model.
         :param dataset: Unused.
         """
-        tf.debugging.Assert(
-            isinstance(function, multiple_optimism_lower_confidence_bound), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(function, multiple_optimism_lower_confidence_bound), [])
         return function  # nothing to update
 
 
diff --git a/trieste/acquisition/function/greedy_batch.py b/trieste/acquisition/function/greedy_batch.py
index 3de6050c85..2134f7587a 100644
--- a/trieste/acquisition/function/greedy_batch.py
+++ b/trieste/acquisition/function/greedy_batch.py
@@ -135,7 +135,7 @@ def prepare_acquisition_function(
         :return: The (log) expected improvement penalized with respect to the pending points.
         :raise tf.errors.InvalidArgumentError: If the ``dataset`` is empty.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
 
@@ -164,10 +164,10 @@ def update_acquisition_function(
             for the current step. Defaults to ``True``.
         :return: The updated acquisition function.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(self._base_acquisition_function is not None, [tf.constant([])])
+        tf.debugging.Assert(self._base_acquisition_function is not None, [])
 
         if new_optimization_step:
             self._update_base_acquisition_function(dataset, model)
@@ -447,7 +447,7 @@ def __init__(
             See class docs for more details.
         :raise tf.errors.InvalidArgumentError: If ``fantasize_method`` is not "KB" or "sample".
         """
-        tf.debugging.Assert(fantasize_method in ["KB", "sample"], [tf.constant([])])
+        tf.debugging.Assert(fantasize_method in ["KB", "sample"], [])
 
         if base_acquisition_function_builder is None:
             base_acquisition_function_builder = ExpectedImprovement()
diff --git a/trieste/acquisition/function/multi_objective.py b/trieste/acquisition/function/multi_objective.py
index d72c00eae1..fd5d106ebd 100644
--- a/trieste/acquisition/function/multi_objective.py
+++ b/trieste/acquisition/function/multi_objective.py
@@ -91,7 +91,7 @@ def prepare_acquisition_function(
         :param dataset: The data from the observer. Must be populated.
         :return: The expected hypervolume improvement acquisition function.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
         mean, _ = model.predict(dataset.query_points)
@@ -121,10 +121,10 @@ def update_acquisition_function(
         :param model: The model.
         :param dataset: The data from the observer. Must be populated.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
-        tf.debugging.Assert(isinstance(function, expected_hv_improvement), [tf.constant([])])
+        tf.debugging.Assert(isinstance(function, expected_hv_improvement), [])
         mean, _ = model.predict(dataset.query_points)
 
         if callable(self._ref_point_spec):
@@ -319,7 +319,7 @@ def prepare_acquisition_function(
         :param dataset: The data from the observer. Must be populated.
         :return: The batch expected hypervolume improvement acquisition function.
         """
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
         dataset = cast(Dataset, dataset)
         tf.debugging.assert_positive(len(dataset), message="Dataset must be populated.")
         mean, _ = model.predict(dataset.query_points)
@@ -564,9 +564,9 @@ def prepare_acquisition_function(
         :return: The HIPPO acquisition function.
         :raise tf.errors.InvalidArgumentError: If the ``dataset`` is empty.
         """
-        tf.debugging.Assert(datasets is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets is not None, [])
         datasets = cast(Mapping[Tag, Dataset], datasets)
-        tf.debugging.Assert(datasets[self._objective_tag] is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets[self._objective_tag] is not None, [])
         tf.debugging.assert_positive(
             len(datasets[self._objective_tag]),
             message=f"{self._objective_tag} dataset must be populated.",
@@ -599,14 +599,14 @@ def update_acquisition_function(
             for the current step. Defaults to ``True``.
         :return: The updated acquisition function.
         """
-        tf.debugging.Assert(datasets is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets is not None, [])
         datasets = cast(Mapping[Tag, Dataset], datasets)
-        tf.debugging.Assert(datasets[self._objective_tag] is not None, [tf.constant([])])
+        tf.debugging.Assert(datasets[self._objective_tag] is not None, [])
         tf.debugging.assert_positive(
             len(datasets[self._objective_tag]),
             message=f"{self._objective_tag} dataset must be populated.",
         )
-        tf.debugging.Assert(self._base_acquisition_function is not None, [tf.constant([])])
+        tf.debugging.Assert(self._base_acquisition_function is not None, [])
 
         if new_optimization_step:
             self._update_base_acquisition_function(models, datasets)
@@ -689,9 +689,7 @@ def __init__(self, model: ProbabilisticModel, pending_points: TensorType):
         :return: The penalization function. This function will raise
             :exc:`ValueError` or :exc:`~tf.errors.InvalidArgumentError` if used with a batch size
             greater than one."""
-        tf.debugging.Assert(
-            pending_points is not None and len(pending_points) != 0, [tf.constant([])]
-        )
+        tf.debugging.Assert(pending_points is not None and len(pending_points) != 0, [])
 
         self._model = model
         self._pending_points = tf.Variable(pending_points, shape=[None, *pending_points.shape[1:]])
@@ -701,9 +699,7 @@ def __init__(self, model: ProbabilisticModel, pending_points: TensorType):
 
     def update(self, pending_points: TensorType) -> None:
         """Update the penalizer with new pending points."""
-        tf.debugging.Assert(
-            pending_points is not None and len(pending_points) != 0, [tf.constant([])]
-        )
+        tf.debugging.Assert(pending_points is not None and len(pending_points) != 0, [])
 
         self._pending_points.assign(pending_points)
         pending_means, pending_vars = self._model.predict(self._pending_points)
diff --git a/trieste/acquisition/optimizer.py b/trieste/acquisition/optimizer.py
index fdd8c01fa8..d9cc67682c 100644
--- a/trieste/acquisition/optimizer.py
+++ b/trieste/acquisition/optimizer.py
@@ -760,7 +760,7 @@ def get_bounds_of_box_relaxation_around_point(
     :param current_point: The point at which to make the continuous relaxation.
     :return: Bounds for the Scipy optimizer.
     """
-    tf.debugging.Assert(isinstance(space, TaggedProductSearchSpace), [tf.constant([])])
+    tf.debugging.Assert(isinstance(space, TaggedProductSearchSpace), [])
 
     space_with_fixed_discrete = space
     for tag in space.subspace_tags:
diff --git a/trieste/acquisition/rule.py b/trieste/acquisition/rule.py
index d4ff018074..515957666d 100644
--- a/trieste/acquisition/rule.py
+++ b/trieste/acquisition/rule.py
@@ -636,7 +636,7 @@ def acquire(
         def state_func(
             state: AsynchronousRuleState | None,
         ) -> tuple[AsynchronousRuleState | None, TensorType]:
-            tf.debugging.Assert(self._acquisition_function is not None, [tf.constant([])])
+            tf.debugging.Assert(self._acquisition_function is not None, [])
 
             if state is None:
                 state = AsynchronousRuleState(None)
diff --git a/trieste/models/gpflow/inducing_point_selectors.py b/trieste/models/gpflow/inducing_point_selectors.py
index b5acb70137..f0a123234a 100644
--- a/trieste/models/gpflow/inducing_point_selectors.py
+++ b/trieste/models/gpflow/inducing_point_selectors.py
@@ -74,7 +74,7 @@ def calculate_inducing_points(
         :return: The new updated inducing points.
         :raise NotImplementedError: If model has more than one set of inducing variables.
         """
-        tf.debugging.Assert(current_inducing_points is not None, [tf.constant([])])
+        tf.debugging.Assert(current_inducing_points is not None, [])
 
         if isinstance(current_inducing_points, list):
             raise NotImplementedError(
@@ -165,7 +165,7 @@ def _recalculate_inducing_points(
         :raise tf.errors.InvalidArgumentError: If ``dataset`` is empty.
         """
 
-        tf.debugging.Assert(len(dataset.query_points) is not None, [tf.constant([])])
+        tf.debugging.Assert(len(dataset.query_points) is not None, [])
 
         N = tf.shape(dataset.query_points)[0]  # training data size
         shuffled_query_points = tf.random.shuffle(dataset.query_points)  # [N, d]
@@ -208,7 +208,7 @@ def _recalculate_inducing_points(
         :raise tf.errors.InvalidArgumentError: If ``dataset`` is empty.
         """
 
-        tf.debugging.Assert(dataset is not None, [tf.constant([])])
+        tf.debugging.Assert(dataset is not None, [])
 
         query_points = dataset.query_points  # [N, d]
         N = tf.shape(query_points)[0]
diff --git a/trieste/models/gpflow/sampler.py b/trieste/models/gpflow/sampler.py
index d1aa8f1ce7..fc0324edd5 100644
--- a/trieste/models/gpflow/sampler.py
+++ b/trieste/models/gpflow/sampler.py
@@ -414,9 +414,7 @@ def update_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunctio
         :param trajectory: The trajectory function to be resampled.
         :return: The new resampled trajectory function.
         """
-        tf.debugging.Assert(
-            isinstance(trajectory, feature_decomposition_trajectory), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(trajectory, feature_decomposition_trajectory), [])
 
         self._feature_functions.resample()  # resample Fourier feature decomposition
         weight_sampler = self._prepare_weight_sampler()  # recalculate weight distribution
@@ -433,9 +431,7 @@ def resample_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunct
         :param trajectory: The trajectory function to be resampled.
         :return: The new resampled trajectory function.
         """
-        tf.debugging.Assert(
-            isinstance(trajectory, feature_decomposition_trajectory), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(trajectory, feature_decomposition_trajectory), [])
         cast(feature_decomposition_trajectory, trajectory).resample()
         return trajectory  # return trajectory with resampled weights
 
diff --git a/trieste/models/gpflux/sampler.py b/trieste/models/gpflux/sampler.py
index 4aef9937a5..fae6cc9d3a 100644
--- a/trieste/models/gpflux/sampler.py
+++ b/trieste/models/gpflux/sampler.py
@@ -187,9 +187,7 @@ def update_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunctio
             :class:`dgp_feature_decomposition_trajectory`
         """
 
-        tf.debugging.Assert(
-            isinstance(trajectory, dgp_feature_decomposition_trajectory), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(trajectory, dgp_feature_decomposition_trajectory), [])
 
         cast(dgp_feature_decomposition_trajectory, trajectory).update()
         return trajectory
@@ -204,9 +202,7 @@ def resample_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunct
         :raise InvalidArgumentError: If ``trajectory`` is not a
             :class:`dgp_feature_decomposition_trajectory`
         """
-        tf.debugging.Assert(
-            isinstance(trajectory, dgp_feature_decomposition_trajectory), [tf.constant([])]
-        )
+        tf.debugging.Assert(isinstance(trajectory, dgp_feature_decomposition_trajectory), [])
         cast(dgp_feature_decomposition_trajectory, trajectory).resample()
         return trajectory
 
diff --git a/trieste/models/keras/sampler.py b/trieste/models/keras/sampler.py
index f43f8fdba8..24ddc01259 100644
--- a/trieste/models/keras/sampler.py
+++ b/trieste/models/keras/sampler.py
@@ -89,7 +89,7 @@ def update_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunctio
         :param trajectory: The trajectory function to be resampled.
         :return: The new trajectory function updated for a new model
         """
-        tf.debugging.Assert(isinstance(trajectory, deep_ensemble_trajectory), [tf.constant([])])
+        tf.debugging.Assert(isinstance(trajectory, deep_ensemble_trajectory), [])
         trajectory.resample()  # type: ignore
         return trajectory
 
@@ -101,7 +101,7 @@ def resample_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunct
         :param trajectory: The trajectory function to be resampled.
         :return: The new resampled trajectory function.
         """
-        tf.debugging.Assert(isinstance(trajectory, deep_ensemble_trajectory), [tf.constant([])])
+        tf.debugging.Assert(isinstance(trajectory, deep_ensemble_trajectory), [])
         trajectory.resample()  # type: ignore
         return trajectory
 
diff --git a/trieste/space.py b/trieste/space.py
index 0de9214b55..04b95fad14 100644
--- a/trieste/space.py
+++ b/trieste/space.py
@@ -656,12 +656,20 @@ def one_hot_encoder(self) -> EncoderFunction:
 
         def binary_encoder(x: TensorType) -> TensorType:
             # no need to one-hot encode binary categories (but we should still validate)
-            tf.debugging.Assert(tf.reduce_all((x == 0) | (x == 1)), [tf.constant([])])
+            tf.debugging.Assert(
+                tf.reduce_all((x == 0) | (x == 1)),
+                ["Invalid binary values for one-hot encoding:", x],
+            )
             return x
 
         def encoder(x: TensorType) -> TensorType:
             flat_x, unflatten = flatten_leading_dims(x)
-            tf.debugging.assert_equal(flat_x.shape[-1], len(self.tags))
+            tf.debugging.assert_equal(
+                flat_x.shape[-1],
+                len(self.tags),
+                message="Invalid input for one-hot encoding: "
+                f"expected {len(self.tags)} tags, got {flat_x.shape[-1]}",
+            )
             columns = tf.split(flat_x, flat_x.shape[-1], axis=1)
             encoders = [
                 (