diff --git a/lectures/lp_intro.md b/lectures/lp_intro.md
index 0b61a1e5..39c06df0 100644
--- a/lectures/lp_intro.md
+++ b/lectures/lp_intro.md
@@ -487,11 +487,11 @@ The optimal plan tells the  factory to produce $2.5$ units of Product 1 and $5$
 
 We are using the `linprog` function as a *black box*.
 
-Inside it, Python first  transforms the problem into  standard form.
+SciPy accepts inequality constraints in the form $A_{ub} x \leq b_{ub}$, equality constraints in the form $A_{eq} x = b_{eq}$, and variable bounds.
 
-To do that, for each inequality constraint it generates one slack variable.
+In this lecture, `linprog` uses SciPy's default `highs` method, which calls the HiGHS optimization solver.
 
-Here the vector of slack variables is a two-dimensional NumPy array that  equals $b_{ub} - A_{ub}x$.
+The slack value returned by `linprog` is a one-dimensional NumPy array whose entries measure the difference $b_{ub} - A_{ub}x$ for each inequality constraint.
 
 See the [official documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html#scipy.optimize.linprog) for more details.