diff --git a/assets/mzn/2_knapsack.mzn b/assets/mzn/2_knapsack.mzn
deleted file mode 100644
index 01f4abb..0000000
--- a/assets/mzn/2_knapsack.mzn
+++ /dev/null
@@ -1,15 +0,0 @@
-% Problem parameters
-enum TOYS = {football, tennisball, stuffed_lama, stuffed_elephant};
-array[TOYS] of int: toy_joy = [63, 12, 50, 100];
-array[TOYS] of int: toy_space = [32, 8, 16, 40];
-int: space_left = 64;
-
-% Decision variables
-var set of TOYS: selection;
-var int: total_joy = sum(toy in selection)(toy_joy[toy]);
-
-% Constraints
-constraint sum(toy in selection)(toy_space[toy]) < space_left;
-
-% Goal
-solve maximize total_joy;
diff --git a/assets/mzn/back_knapsack.mzn b/assets/mzn/back_knapsack.mzn
new file mode 100644
index 0000000..235f113
--- /dev/null
+++ b/assets/mzn/back_knapsack.mzn
@@ -0,0 +1,15 @@
+% Problem parameters
+enum TOYS;@\Vlabel{line:back-knap-toys}@
+array[TOYS] of int: toy_joy;@\Vlabel{line:back-knap-joy}@
+array[TOYS] of int: toy_space;@\Vlabel{line:back-knap-space}@
+int: space_left;@\Vlabel{line:back-knap-left}@
+
+% Decision variables
+var set of TOYS: selection;@\Vlabel{line:back-knap-sel}@
+var int: total_joy = sum(toy in selection)(toy_joy[toy]);@\Vlabel{line:back-knap-tj}@
+
+% Constraints
+constraint sum(toy in selection)(toy_space[toy]) < space_left;@\Vlabel{line:back-knap-con}@
+
+% Goal
+solve maximize total_joy;@\Vlabel{line:back-knap-obj}@
diff --git a/chapters/2_background.tex b/chapters/2_background.tex
index 72698f8..0f81ecf 100644
--- a/chapters/2_background.tex
+++ b/chapters/2_background.tex
@@ -40,7 +40,8 @@ the model.
     problem using dynamic programming}
 \end{listing}
 
-\begin{example}
+\begin{example}%
+\label{ex:back-knapsack}
 
   Let us consider the following scenario: Packing for a weekend trip, I have to
   decide which toys to bring for my dog, Audrey. We only have a small amount of
@@ -118,42 +119,51 @@ discrete satisfiability and optimisation problems
 \autocite{nethercote-2007-minizinc}. Its expressive language and extensive
 library of constraints allow users to easily model complex problems.
 
-Let us introduce the language by modelling the well-known \emph{Latin squares}
-problem \autocite{wallis-2011-combinatorics}: Given an integer \(n\), find an
-\(n \times n\) matrix, such that each row and column is a permutation of values
-\(1 \ldots n\). A \minizinc\ model encoding this problem could look as follows:
+Let us introduce the language by modelling the problem from
+\cref{ex:back-knapsack}. A \minizinc\ model encoding this problem is shown in
+\cref{lst:back-mzn-knapsack}.
 
 \begin{listing}
-  \mznfile{assets/mzn/2_knapsack.mzn}
-  \caption{\label{lst:2-mzn-knapsack} A \minizinc\ model describing a 0-1 knapsack
+  \mznfile{assets/mzn/back_knapsack.mzn}
+  \caption{\label{lst:back-mzn-knapsack} A \minizinc\ model describing a 0-1 knapsack
     problem}
 \end{listing}
 
-\begin{mzn}
-int: n;
-array [1..n, 1..n] of var 1..n: x;
+The model starts with the declaration of the \glspl{parameter}.
+\Lref{line:back-knap-toys} declares an enumerated type that represents all
+possible toys, \(T\) in the mathematical model in the example.
+\Lref{line:back-knap-joy,line:back-knap-space} declare arrays mapping from toys
+to integer values, these represent the functional mappings \(joy\) and
+\(space\). Finally, \lref{line:back-knap-left} declares an integer
+\gls{parameter} to represent the car capacity as an equivalent to \(C\).
 
-constraint forall (r in 1..n) (
-  all_different([x[r, c] | c in 1..n])
-);
-constraint forall (c in 1..n) (
-  all_different([x[r, c] | r in 1..n])
-);
-\end{mzn}
+The model then declares its \glspl{variable}. \Lref{line:back-knap-sel} declares
+the main \gls{variable} \mzninline{selection}, which represents the selection of
+toys to be packed. \(S\) in our earlier model. We also declare the variable
+\mzninline{total_joy}, on \lref{line:back-knap-tj}, which is functionally
+defined to be the summation of all the joy for the toy picked in our selection.
 
-The model introduces a \gls{parameter} \mzninline{n}, and a two-dimensional
-array of \glspl{variable} (marked by the \mzninline{var} keyword) \mzninline{x}.
-Each variable in \mzninline{x} is restricted to the set of integers
-\mzninline{1..n}, which is called the variable's \gls{domain}. The constraints
-specify the requirements of the problem: for each row \mzninline{r}, the
-\mzninline{x} variables of all columns must take pairwise different values (and
-the same for each column \mzninline{c}). This is modelled using the
-\mzninline{all_different} function, one of hundreds of pre-defined constraints
-in \minizinc's library.
+Finally, the model contains a constraint, on \lref{line:back-knap-con}, to
+ensure we do not exceed the given capacity and states the goal for the solver:
+to maximise the value of the variable \mzninline{total_joy}.
+
+One might note that, although more textual and explicit, the \minizinc\ model
+definition is very similar to our earlier mathematical definition.
 
 Given ground assignments to input \glspl{parameter}, a \minizinc\ model is
 translated (via a process called \emph{flattening}) into a set of variables and
-primitive constraints. Here is the result of flattening for \mzninline{n=2}:
+primitive constraints.
+
+Given the assignments
+
+\begin{mzn}
+TOYS = {football, tennisball, stuffed_lama, stuffed_elephant};
+toy_joy = [63, 12, 50, 100];
+toy_space = [32, 8, 16, 40];
+space_left = 64;
+\end{mzn}
+
+is the result of flattening for \mzninline{n=2}:
 
 \begin{mzn}
 var 1..2: x_1_1;