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+# Programmer's Guide
+
+The documents in this unit dive into the details of writing TensorFlow
+code.  This section begins with the following guides, each of which
+explain a particular aspect of TensorFlow:
+
+  * @{$variables$Variables: Creation, Initialization, Saving, and Loading},
+    which details the mechanics of TensorFlow Variables.
+  * @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
+    rank (the number of dimensions), shape (the size of each dimension),
+    and datatypes.
+  * @{$variable_scope$Sharing Variables}, which explains how to share and
+    manage large sets of variables when building complex models.
+  * @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
+    rich queuing system.
+  * @{$reading_data$Reading Data}, which documents three different mechanisms
+    for getting data into a TensorFlow program.
+
+The following guide is helpful when training a complex model over multiple
+days:
+
+  * @{$supervisor$Supervisor: Training Helper for Days-Long Trainings}, which
+    explains how to gracefully handle system crashes during a lengthy training
+    session.
+
+TensorFlow provides a debugger named `tfdbg`, which is documented in the
+following two guides:
+
+  * @{$debugger$TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial: MNIST},
+    which walks you through the use of `tfdbg` within an application written
+    in the low-level TensorFlow API.
+  * @{$tfdbg-tflearn$How to Use TensorFlow Debugger (tfdbg) with tf.contrib.learn},
+    which demonstrates how to use `tfdbg` within the Estimators API.
+
+A `MetaGraph` consists of both a computational graph and its associated
+metadata.  A `MetaGraph` contains the information required to continue
+training, perform evaluation, or run inference on a previously
+trained graph.  The following guide details `MetaGraph` objects:
+
+  * @{$meta_graph$Exporting and Importing a MetaGraph}.
+
+To learn about the TensorFlow versioning scheme, consult the following two
+guides:
+
+  * @{$version_semantics$TensorFlow Version Semantics}, which explains
+    TensorFlow's versioning nomenclature and compatibility rules.
+  * @{$data_versions$TensorFlow Data Versioning: GraphDefs and Checkpoints},
+    which explains how TensorFlow adds versioning information to computational
+    graphs and checkpoints in order to support compatibility across versions.
+
+We conclude this section with a FAQ about TensorFlow programming:
+
+  * @{$faq$Frequently Asked Questions}
diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/programmers_guide/leftnav_files
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--- a/tensorflow/docs_src/programmers_guide/leftnav_files
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-reading_data.md
-threading_and_queues.md
+index.md
+variables.md
+dims_types.md
 variable_scope.md
-version_semantics.md
-data_versions.md
+threading_and_queues.md
+reading_data.md
 supervisor.md
 debugger.md
 tfdbg-tflearn.md
 meta_graph.md
+version_semantics.md
+data_versions.md
 faq.md
-dims_types.md
-variables.md