diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index 0f27cb5ff409..1dfbc52422e7 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -3736,8 +3736,50 @@ def asarray(a: Any, dtype: DTypeLike | None = None, order: str | None = None,
   return array(a, dtype=dtype, copy=bool(copy), order=order, device=device)
 
 
-@util.implements(np.copy, lax_description=_ARRAY_DOC)
 def copy(a: ArrayLike, order: str | None = None) -> Array:
+  """Return a copy of the array.
+
+  JAX implementation of :func:`numpy.copy`.
+
+  Args:
+    a: arraylike object to copy
+    order: not implemented in JAX
+
+  Returns:
+    a copy of the input array ``a``.
+
+  See Also:
+    - :func:`jax.numpy.array`: create an array with or without a copy.
+    - :meth:`jax.Array.copy`: same function accessed as an array method.
+
+  Examples:
+    Since JAX arrays are immutable, in most cases explicit array copies
+    are not necessary. One exception is when using a function with donated
+    arguments (see the ``donate_argnums`` argument to :func:`jax.jit`).
+
+    >>> f = jax.jit(lambda x: 2 * x, donate_argnums=0)
+    >>> x = jnp.arange(4)
+    >>> y = f(x)
+    >>> print(y)
+    [0 2 4 6]
+
+    Because we marked ``x`` as being donated, the original array is no longer
+    available:
+
+    >>> print(x)  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    RuntimeError: Array has been deleted with shape=int32[4].
+
+    In situations like this, an explicit copy will let you keep access to the
+    original buffer:
+
+    >>> x = jnp.arange(4)
+    >>> y = f(x.copy())
+    >>> print(y)
+    [0 2 4 6]
+    >>> print(x)
+    [0 1 2 3]
+  """
   util.check_arraylike("copy", a)
   return array(a, copy=True, order=order)