A greedy algorithm is correct when the problem has the greedy-choice property and optimal substructure. Intuitively, you can prove that making the locally best choice can be exchanged into an optimal solution (exchange argument), so the local choice leads to a global optimum.
Advanced answer
Deep dive
Expanding on the short answer — what usually matters in practice:
Complexity: compare typical operations (average vs worst-case).
Invariants: what must always hold for correctness.
When the choice is wrong: production symptoms (latency, GC, cache misses).
Explain the "why", not just the "what" (intuition + consequences).
Trade-offs: what you gain/lose (time, memory, complexity, risk).
Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "greedy-algorithms:-what-property-makes-a-greedy-"
function explain() {
// Start from the core idea:
// A greedy algorithm is correct when the problem has the greedy-choice property and optimal
}
Common pitfalls
Too generic: no concrete trade-offs or examples.
Mixing average-case and worst-case (e.g., complexity).