diff --git a/docs/src/understanding_mooncake/algorithmic_differentiation.md b/docs/src/understanding_mooncake/algorithmic_differentiation.md
index 22b3c8911..d752157f6 100644
--- a/docs/src/understanding_mooncake/algorithmic_differentiation.md
+++ b/docs/src/understanding_mooncake/algorithmic_differentiation.md
@@ -410,36 +410,110 @@ This "vector-Jacobian product" expression is commonly used to explain AD, and is
 
 # Directional Derivatives and Gradients
 
-Now we turn to using reverse-mode AD to compute the gradient of a function.
-In short, given a function ``g : \mathcal{X} \to \RR`` with derivative ``D g [x]`` at ``x``, its gradient is equal to ``D g [x]^\ast (1)``.
-We explain why in this section.
+Now we turn to using forwards- and reverse-mode AD to compute the gradient of a function.
 
-The derivative discussed here can be used to compute directional derivatives.
-Consider a function ``f : \mathcal{X} \to \RR`` with Frechet derivative ``D f [x] : \mathcal{X} \to \RR`` at ``x \in \mathcal{X}``.
-Then ``D f[x](\dot{x})`` returns the directional derivative in direction ``\dot{x}``.
+Recall that if ``D f[x] : \mathcal{X} \to \mathbb{R}`` is the Frechet derivative discussed here then ``D f[x](\dot{x})`` is the _directional derivative_ in the ``\dot{x}`` direction.
 
-Gradients are closely related to the adjoint of the derivative.
-Recall that the gradient of ``f`` at ``x`` is defined to be the vector ``\nabla f (x) \in \mathcal{X}`` such that ``\langle \nabla f (x), \dot{x} \rangle`` gives the directional derivative of ``f`` at ``x`` in direction ``\dot{x}``.
-Having noted that ``D f[x](\dot{x})`` is exactly this directional derivative, we can equivalently say that
+The _gradient_ of ``f : \mathcal{X} \to \mathbb{R}`` at ``x`` is defined to be the vector ``\nabla f (x) \in \mathcal{X}`` such that
 ```math
-D f[x](\dot{x}) = \langle \nabla f (x), \dot{x} \rangle .
+\langle \nabla f (x), \dot{x} \rangle = D f[x](\dot{x})
 ```
+for any direction ``\dot{x}``.
+In other words, the vector ``\nabla f`` encodes all the information about the directional derivatives of ``f``, and we use the inner product to retrieve each one.
 
-The role of the adjoint is revealed when we consider ``f := \mathcal{l} \circ g``, where ``g : \mathcal{X} \to \mathcal{Y}``, ``\mathcal{l}(y) := \langle \bar{y}, y \rangle``, and ``\bar{y} \in \mathcal{Y}`` is some fixed vector.
-Noting that ``D \mathcal{l} [y](\dot{y}) = \langle \bar{y}, \dot{y} \rangle``, we apply the chain rule to obtain
+An alternative characterisation is that ``\nabla f(x)`` is the vector pointing in the direction of steepest ascent on ``f`` at ``x``, with magnitude equal to the directional derivative in that steepest direction.
+
+_**Aside: The choice of inner product**_
+
+Notice that the value of the gradient depends on how the inner product on ``\mathcal{X}`` is defined.
+Indeed, different choices of inner product result in different values of ``\nabla f``.
+Adjoints such as ``D f[x]^*`` are also inner product dependent.
+However, the actual derivative ``D f[x]`` is of course invariant -- it makes no reference to the inner product.
+
+In practice, Mooncake uses the Euclidean inner product, extended in the "obvious way" to other composite data types (that is, as if everything is flattened and embedded in ``\mathbb{R}^N``).
+But we endeavour to keep the discussion general in order to make the role of the inner product explicit.
+
+
+
+#### Computing the gradient from forwards-mode
+
+To compute the gradient in forwards-mode, we need to evaluate the forwards pass ``\dim \mathcal{X}`` times.
+We also need to refer to a basis ``\{\mathbf{e}_i\}`` of ``\mathcal{X}`` and its reciprocal basis ``\{\mathbf{e}^i\}`` defined by ``\langle \mathbf{e}_i, \mathbf{e}^j \rangle = \delta_i^j``.
+(For any basis there exists such a reciprocal basis, and they are the same if the basis is orthonormal.)
+
+Equipped with such a pair of bases, we can always decompose a vector ``x = \sum_i x^i \mathbf{e}_i`` into its components ``x^i = \langle x, \mathbf{e}^i \rangle``.
+Therefore, the gradient is given by
 ```math
-\begin{align}
-D f [x] (\dot{x}) &= [(D \mathcal{l} [g(x)]) \circ (D g [x])](\dot{x}) \nonumber \\
-    &= \langle \bar{y}, D g [x] (\dot{x}) \rangle \nonumber \\
-    &= \langle D g [x]^\ast (\bar{y}), \dot{x} \rangle, \nonumber
-\end{align}
+\nabla f(x)
+	= \sum_i \langle \nabla f(x), \mathbf{e}^i \rangle \mathbf{e}_i
+	= \sum_i D f[x](\mathbf{e}^i) \, \mathbf{e}_i
 ```
-from which we conclude that ``D g [x]^\ast (\bar{y})`` is the gradient of the composition ``l \circ g`` at ``x``.
+where the second equality follows from the gradient's implicit definition.
 
-The consequence is that we can always view the computation performed by reverse-mode AD as computing the gradient of the composition of the function in question and an inner product with the argument to the adjoint.
+If the inner product is Euclidean, then ``\mathbf{e}^i = \mathbf{e}_i`` and we can interpret the ``i``th component of ``\nabla f`` as the directional derivative when moving in the ``i``th direction.
 
-The above shows that if ``\mathcal{Y} = \RR`` and ``g`` is the function we wish to compute the gradient of, we can simply set ``\bar{y} = 1`` and compute ``D g [x]^\ast (\bar{y})`` to obtain the gradient of ``g`` at ``x``.
+_**Example**_
 
+Consider again the Julia `function`
+```julia
+f(x::Float64, y::Tuple{Float64, Float64}) = x + y[1] * y[2]
+```
+corresponding to ``f(x, y) = x + y_1 y_2``.
+An orthonormal basis for the function's domain ``\mathbb{R} \times \mathbb{R}^2`` is
+```math
+\mathbf{e}_1 = \mathbf{e}^1 = (1, (0, 0)), \quad
+\mathbf{e}_2 = \mathbf{e}^2 = (0, (1, 0)), \quad
+\mathbf{e}_3 = \mathbf{e}^3 = (0, (0, 1)), \quad
+```
+so the gradient is
+```math
+\begin{align*}
+\nabla f(x, y)
+	&= \sum_i D f[x, y](\mathbf{e}^i) \mathbf{e}_i \\
+	&= \Big(D f[x, y](1, (0, 0)), \big(D f[x, y](0, (1, 0)), D f[x, y](0, (0, 1))\big)\Big) \\
+	&= (1, (y_2, y_1))
+\end{align*}
+```
+referring [above](#AD-of-a-Julia-function:-a-slightly-less-trivial-example) for the form of ``D f[x, y]``.
+
+#### Computing the gradient from reverse-mode
+If we perform a single reverse-pass on a function ``f : \mathcal{X} \to \RR`` to obtain ``D f[x]^\ast``, then the gradient is simply
+```math
+\nabla f (x) = D f[x]^\ast (1) .
+```
+
+To show this, note that ``D f [x] (\dot{x}) = \langle 1, D f[x] (\dot{x}) \rangle = \langle D f[x]^\ast (1), \dot{x} \rangle`` using the definition of the adjoint.
+Then, the definition of the gradient gives
+```math
+\langle \nabla f (x), \dot{x} \rangle = \langle D f[x]^\ast (1), \dot{x} \rangle
+```
+which implies ``\nabla f (x) = D f[x]^\ast (1)`` since ``\dot{x}`` is arbitrary.
+
+_**Example**_
+
+The adjoint derivative of ``f(x, y) = x + y_1 y_2`` (see [above](#AD-of-a-Julia-function:-a-slightly-less-trivial-example)) immediately gives
+```math
+\nabla f(x, y) = D f[x, y]^\ast (1) = (1, (y_2, y_1)) .
+```
+
+_**Aside: Adjoint Derivatives as Gradients**_
+
+It is interesting to note that value of ``D f[x]^\ast (\bar{y})`` returned by performing reverse-mode on a function ``f : \mathcal{X} \to \mathcal{Y}`` can always be viewed as the gradient of another function ``F : \mathcal{X} \to \mathbb{R}``.
+
+Let ``F \coloneqq h_{\bar{y}} \circ f`` where ``h_{\bar{y}}(y) = \langle y, \bar{y}\rangle``.
+One can show ``D h_{\bar{y}}[y]^\ast (1) = \bar{y}``.
+Then, since
+```math
+\begin{align*}
+\langle \nabla F(x), \dot{x} \rangle
+	&= \langle D F[x]^\ast (1), \dot{x} \rangle \\
+	&= \langle D f[x]^\ast (D h_{\bar{y}}[f(x)]^\ast (1)), \dot{x} \rangle \\
+	&= \langle D f[x]^\ast (\bar{y}), \dot{x} \rangle \\
+\end{align*}
+```
+we have that ``\nabla F(x) = D f[x]^\ast (\bar{y})``.
+
+The consequence is that we can always view the computation performed by reverse-mode AD as computing the gradient of the composition of the function in question and an inner product with the argument to the adjoint.