Rather than thinking about $y^T x$, it's better to think about the dot product $y \cdot x$. Recall that in a real vector space, the dot product $y \cdot x$ gives you a measure of how the vectors $x$ and $y$ project onto each other: if the dot product is large and positive, the vectors $x$ and $y$ are pointing in a similar direction, and if it is large and negative, the vectors are pointing in an opposite direction.
Since $x^T A x = x \cdot (Ax)$, it is a measure of how "pointing in the same direction" the vector $Ax$ is compared to $x$. The matrix $A$ is called positive definite if $x \cdot (Ax) > 0$ for all $x \neq 0$. This gives some immediate consequences:
- $A$ has full rank, so is an invertible matrix.
- For each vector $x$, the vectors $x$ and $Ax$ are always on the same side of the hyperplane perpendicular to $x$.