diff --git a/privacy/dp_query/dp_query.py b/privacy/dp_query/dp_query.py
index ce0304e..5123520 100644
--- a/privacy/dp_query/dp_query.py
+++ b/privacy/dp_query/dp_query.py
@@ -13,6 +13,33 @@
 # limitations under the License.
 
 """An interface for differentially private query mechanisms.
+
+The DPQuery class abstracts the differential privacy mechanism needed by DP-SGD.
+
+The nomenclature is not specific to machine learning, but rather comes from
+the differential privacy literature. Therefore, instead of talking about
+examples, minibatches, and gradients, the code talks about records, samples and
+queries. For more detail, please see the paper here:
+https://arxiv.org/pdf/1812.06210.pdf
+
+A common usage paradigm for this class is centralized DP-SGD training on a
+fixed set of training examples, which we call "standard DP-SGD training."
+In such training, SGD applies as usual by computing gradient updates from a set
+of training examples that form a minibatch. However, each minibatch is broken
+up into disjoint "microbatches."  The gradient of each microbatch is computed
+and clipped to a maximum norm, with the "records" for all such clipped gradients
+forming a "sample" that constitutes the entire minibatch. Subsequently, that
+sample can be "queried" to get an averaged, noised gradient update that can be
+applied to model parameters.
+
+In order to prevent inaccurate accounting of privacy parameters, the only
+means of inspecting the gradients and updates of SGD training is via the use
+of the below interfaces, and through the accumulation and querying of a
+"sample state" abstraction. Thus, accessing data is indirect on purpose.
+
+The DPQuery class also allows the use of a global state that may change between
+samples. In the common situation where the privacy mechanism remains unchanged
+throughout the entire training process, the global state is usually None.
 """
 
 from __future__ import absolute_import
@@ -62,12 +89,18 @@ class DPQuery(object):
     """Accumulates a single record into the sample state.
 
     Args:
-      params: The parameters for the sample.
-      sample_state: The current sample state.
-      record: The record to accumulate.
+      params: The parameters for the sample. In standard DP-SGD training,
+        the clipping norm for the sample's microbatch gradients (i.e.,
+        a maximum norm magnitude to which each gradient is clipped)
+      sample_state: The current sample state. In standard DP-SGD training,
+        the accumulated sum of previous clipped microbatch gradients.
+      record: The record to accumulate. In standard DP-SGD training,
+        the gradient computed for the examples in one microbatch, which
+        may be the gradient for just one example (for size 1 microbatches).
 
     Returns:
-      The updated sample state.
+      The updated sample state. In standard DP-SGD training, the set of
+      previous mcrobatch gradients with the addition of the record argument.
     """
     pass
 
@@ -77,10 +110,16 @@ class DPQuery(object):
 
     Args:
       sample_state: The sample state after all records have been accumulated.
-      global_state: The global state.
+        In standard DP-SGD training, the accumulated sum of clipped microbatch
+        gradients (in the special case of microbatches of size 1, the clipped
+        per-example gradients).
+      global_state: The global state, storing long-term privacy bookkeeping.
 
     Returns:
       A tuple (result, new_global_state) where "result" is the result of the
-      query and "new_global_state" is the updated global state.
+      query and "new_global_state" is the updated global state. In standard
+      DP-SGD training, the result is a gradient update comprising a noised
+      average of the clipped gradients in the sample state---with the noise and
+      averaging performed in a manner that guarantees differential privacy.
     """
     pass