AI for essay brainstorming and outlining in AI for Everyone - Time & Space Complexity
When using AI to help brainstorm and outline essays, it's important to understand how the time needed grows as the essay length or complexity increases.
We want to know how the AI's work time changes when given more ideas or longer outlines.
Analyze the time complexity of the following AI brainstorming and outlining process.
function generateOutline(ideas) {
let outline = []
for (let idea of ideas) {
let subpoints = brainstormSubpoints(idea)
outline.push({idea: idea, subpoints: subpoints})
}
return outline
}
function brainstormSubpoints(idea) {
// returns a list of subpoints for the idea
return ["subpoint1", "subpoint2", "subpoint3"]
}
This code takes a list of main ideas and creates an outline by generating subpoints for each idea.
Look at what repeats as input grows.
- Primary operation: Looping through each main idea to generate subpoints.
- How many times: Once for each idea in the input list.
As the number of ideas increases, the AI spends more time creating subpoints for each one.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | About 10 times the subpoint generation steps |
| 100 | About 100 times the subpoint generation steps |
| 1000 | About 1000 times the subpoint generation steps |
Pattern observation: The work grows directly with the number of ideas; doubling ideas doubles the work.
Time Complexity: O(n)
This means the time to brainstorm and outline grows in a straight line with the number of ideas.
[X] Wrong: "Adding more ideas won't affect the time much because subpoints are fixed."
[OK] Correct: Even if subpoints per idea are fixed, more ideas mean more loops, so total time still grows with ideas.
Understanding how AI tasks scale with input size helps you explain efficiency clearly, a useful skill in many tech discussions.
What if the number of subpoints generated per idea also grows with the idea's complexity? How would the time complexity change?