diff --git a/examples/examples_test.py b/examples/examples_test.py
index 1162ef058..2b2b56fcf 100644
--- a/examples/examples_test.py
+++ b/examples/examples_test.py
@@ -21,6 +21,7 @@ from absl.testing import parameterized
 
 import numpy as np
 
+from jax import lax
 from jax import test_util as jtu
 from jax import random
 import jax.numpy as jnp
@@ -88,7 +89,7 @@ class ExamplesTest(jtu.JaxTestCase):
     truth = rng.randn(d)
     xs = rng.randn(n, d)
     ys = jnp.dot(xs, truth)
-    kernel = lambda x, y: jnp.dot(x, y)
+    kernel = lambda x, y: jnp.dot(x, y, precision=lax.Precision.HIGH)
     predict = kernel_lsq.train(kernel, xs, ys)
     self.assertAllClose(predict(xs), ys, atol=1e-3, rtol=1e-3,
                         check_dtypes=False)
diff --git a/examples/kernel_lsq.py b/examples/kernel_lsq.py
index 570828b40..5211f4da0 100644
--- a/examples/kernel_lsq.py
+++ b/examples/kernel_lsq.py
@@ -19,7 +19,7 @@ import numpy.random as npr
 
 import jax.numpy as jnp
 from jax.experimental import optimizers
-from jax import grad, jit, make_jaxpr, vmap
+from jax import grad, jit, make_jaxpr, vmap, lax
 
 
 def gram(kernel, xs):
@@ -74,7 +74,7 @@ if __name__ == "__main__":
 
   # linear kernel
 
-  linear_kernel = lambda x, y: jnp.dot(x, y)
+  linear_kernel = lambda x, y: jnp.dot(x, y, precision=lax.Precision.HIGH)
   truth = npr.randn(d)
   xs = npr.randn(n, d)
   ys = jnp.dot(xs, truth)