diff --git a/README.org b/README.org
index 2a7d506..dd45173 100644
--- a/README.org
+++ b/README.org
@@ -1,9 +1,10 @@
-* Quack-TD
+#+TITLE: Quack-TD
 
 Quack-TD is a backgammon playing algorithm based upon neural networks trained
-through TD(\lambda)-learning.
+through TD(\lambda)-learning. The algorithm is implemented using Python 3 and
+Tensorflow.
 
-** Usage
+* Usage
 
 The main executable is =main.py=. Various command-line options and switches can be used to
 execute different stages and modify the behaviour of the program. All
@@ -11,22 +12,59 @@ command-line options and switches are listed by running =main.py= with the argum
 =--help=. The three central switches are listed below:
 
 - =--train=: Trains the neural network for a set amount of episodes (full games
-  of backgammon) set by =--episodes= (defaults to 1,000).
+  of backgammon) set by =--episodes= (defaults to 1,000). Summary results of the
+  games played during the training session are written to =models/$MODEL/logs/eval.log=
 
 - =--eval=: Evaluates the nerual network using the methods specified by
   =--eval-methods= for a the amount of episodes set by =--episodes= (defaults to
-  1,000).
+  1,000). Results are written to =models/$MODEL/logs/eval.log=.
 
 - =--play=: Allows the user to interactively play a game of backgammon against
   the algorithm.
 
-** Model storage format
+** Evaluation methods
+
+Currently, only a single evaluation method is implemented:
+
+- =random=: Evaluates by playing against a player that makes random moves drawn
+  from the set of legal moves. Should be used with high episode counts to lower
+  variance. *TODO*: Doesn't even work currently
+
+** Examples
+
+The following examples describe commmon operations.
+
+*** Train default model
+
+=python3 --train=
+
+*** Train model named =quack=
+
+=python3 --train --model=quack=
+
+*** Train default model in sessions of 10,000 episodes
+
+=python3 --train --episodes=10000=
+
+*** Train model =quack= and evaluate after each training session
+
+=python3 --train --eval-after-train --model=quack=
+
+*** Evaluate model named =quack= using default evaluation method (currently =random=)
+
+=python3 --eval --model-name=quack=
+
+*** Evaluate default model using evaluation methods =random= and =foovaluation=
+
+=python3 --eval --eval-methods random foovaluation=
+
+* Model storage format
 
 Models are stored in the directory =models=. If no model is specfied with the
 =--model= option, the model is stored in the =models/default=
 directory. Otherwise, the model is stored in =models/$MODEL=.
 
-*** Files
+** Files
 
 Along with the Tensorflow checkpoint files in the directory, the following files
 are stored:
@@ -41,42 +79,42 @@ are stored:
   =model.episodes= will be updated every time the model is saved to disk. The
   format of this file is specified in [[Log format]].
 
-*** Log format
+** Log format
 
 The evaluation and training log files (=logs/eval.log= and =logs/train.log=
 respectively) are CSV-foramtted files with structure as described below. Both
 files have semicolon-separated columns (=;=) and newline-separated rows (=\n=).
 
-**** Evaluation log (=eval.log=)
+*** Evaluation log (=eval.log=)
 
 Columns are written in the following order:
 
-- =time=: Unix time (Epoch time) timestamp in local time (TODO: should be UTC
+- =time=: Unix time (Epoch time) timestamp in local time (*TODO*: should be UTC
   instead?) describing when the evaluation was finished.
 - =method=: Short string describing the method used for evaluation.
 - =trained_eps=: Amount of episodes trained with the model before evaluation
 - =count=: Amount of episodes used for evaluation
 - =sum=: Sum of outcomes of the games played during evaluation. Outcomes are
   integers in the range of -2 to 2. A sum of 0 indicates that the evaluated
-  algorithm scored neutrally. (TODO: Is this true?)
+  algorithm scored neutrally. (*TODO*: Is this true?)
 - =mean=: Mean of outcomes of the games played during evaluation. Outcomes are
   integers in the range of -2 to 2. A mean of 0 indicates that the evaluated
-  algorithm scored neutrally. (TODO: Is this true?)
+  algorithm scored neutrally. (*TODO*: Is this true?)
 
-TODO: Add example of log row
+*TODO*: Add example of log row
 
-**** Training log (=train.log=)
+*** Training log (=train.log=)
 
 Columns are written in the following order:
 
-- =time=: Unix time (Epoch time) timestamp in local time (TODO: should be UTC
+- =time=: Unix time (Epoch time) timestamp in local time (*TODO*: should be UTC
   instead?) describing when the training session was finished.
 - =trained_eps=: Amount of episodes trained with the model /after/ the training
   session
 - =count=: Amount of episodes used for training
 - =sum=: Sum of outcomes of the games played during training. Outcomes are
   integers in the range of -2 to 2. A sum of 0 indicates that the evaluated
-  algorithm scored neutrally. (TODO: Is this true?)
+  algorithm scored neutrally. (*TODO*: Is this true?)
 - =mean=: Mean of outcomes of the games played during training. Outcomes are
   integers in the range of -2 to 2. A mean of 0 indicates that the evaluated
-  algorithm scored neutrally. (TODO: Is this true?)
+  algorithm scored neutrally. (*TODO*: Is this true?)