diff --git a/tensorflow_probability/python/experimental/fastgp/fast_gp.py b/tensorflow_probability/python/experimental/fastgp/fast_gp.py
index f382271f6c..bc741b93ca 100644
--- a/tensorflow_probability/python/experimental/fastgp/fast_gp.py
+++ b/tensorflow_probability/python/experimental/fastgp/fast_gp.py
@@ -85,15 +85,11 @@ class GaussianProcess(distribution.Distribution):
   """Fast, JAX-only implementation of a GP distribution class.
 
   See tfd.distributions.GaussianProcess for a description and parameter
-  documentation.  Currently only supports log_prob and posterior_predictive
-  (the only two methods used by smc.py).
+  documentation, but note that not all of that class's methods are supported.
 
   The default parameters are tuned to give a good time / error trade-off
   in the n > 15,000 regime where this class gives a substantial speed-up
-  over tfd.distributions.GaussianProcess.  In particular, it is tuned to
-  give a trade-off in the case where you care about the accuracy of both
-  log_prob and its derivative.  If you care only about log_prob, it is
-  recommended to use log_det_algorithm='slq' with preconditioner_num_iters=1.
+  over tfd.distributions.GaussianProcess.
   """
 
   def __init__(
@@ -311,7 +307,26 @@ def get_preconditioner(cov):
 
   @jax.named_call
   def log_prob(self, value, key, is_missing=None) -> Array:
-    """log P(value | GP)."""
+    """log P(value | GP).
+
+    Args:
+      value: `float` or `double` jax.Array.
+      key: A jax KeyArray.  This method uses stochastic methods to quickly
+        estimate the log probability of `value`, and `key` is needed to
+        generate the stochasticity.  `key` is also used when computing the
+        derivative of this function.  In some circumstances it is acceptable
+        and in fact even necessary to pass the same value of `key` to multiple
+        invocations of log_prob; for example if the log_prob is being
+        optimized by an algorithm that assumes it is deterministic.
+      is_missing: Optional `bool` jax.Array of shape `[..., e]` where `e` is
+        the number of index points in each batch.  Represents a batch of
+        Boolean masks.  When not `None`, the returned log_prob is for the
+        *marginal* distribution in which all dimensions with `is_missing==True`
+        have been marginalized out.
+
+    Returns:
+      A stochastic approximation to log P(value | GP).
+    """
     empty_sample_batch_shape = value.ndim == 1
     if empty_sample_batch_shape:
       value = value[jnp.newaxis]