Graph systems of linear inequalities and identify the feasible region; apply to simple constraint problems.
A system of inequalities asks for points that satisfy EVERY inequality at once. Graph each one's shaded half-plane the way you did last week, and the system's solution is wherever all the shadings overlap - often a wedge or polygon-shaped patch called the feasible region, because every point in it is a "feasible" (allowed) choice.
This is how real limits get modeled: a budget, a weight limit, a minimum requirement. Each constraint becomes one inequality; the feasible region is every combination that obeys all of them simultaneously. Later math (and real logistics/business decisions) often asks for the BEST point in that region - and that best point almost always sits at a corner where two boundary lines meet, not floating in the middle.
Mastery looks like: They can test whether a point satisfies a system of two inequalities and set up a simple two-constraint system from a word description.
Common stumbles: Checking only one inequality and stopping; assuming "close" to satisfying a constraint (like 24 ≤ 25 needed but getting 24 when 25 was required) still counts.