How Students and Job Seekers Use AI Prompts to Study Smarter and Land Better Opportunities

Help me build an argument outline for a 2,500-word political science essay.

ASSIGNMENT: Analyze the impact of social media platforms on democratic participation in the 2020s. Must reference at least 4 peer-reviewed sources and take a clear argumentative position.

MY THESIS ANGLE: Social media has simultaneously increased political engagement among young voters while degrading the quality of public discourse through algorithmic polarization.

SOURCES I HAVE ALREADY:
- Sunstein (2017) "#Republic: Divided Democracy in the Age of Social Media"
- Bail et al. (2018) "Exposure to opposing views on social media can increase political polarization" (PNAS)
- Boulianne (2020) meta-analysis on social media and political participation

WHAT I NEED:
1. An argument outline with thesis, 3-4 supporting sections, and counterargument
2. For each section: the key claim, which sources support it, and what evidence I still need to find
3. Suggested search terms for finding 1-2 additional peer-reviewed sources that address algorithmic recommendation systems
4. A brief analysis of the strongest counterargument to my thesis and how to address it

FORMAT: Outline format with bullet points. Do not write the essay — I need the structural framework to write it myself.

CITATION STYLE: APA 7th edition

Rewrite these 3 resume bullet points for a specific job application.

TARGET ROLE: Growth Marketing Associate at Notion (Series C, PLG SaaS)
JOB POSTING PRIORITIES: "data-driven experimentation," "cross-functional collaboration," "content-led growth," "self-starter"

MY CURRENT BULLETS:
1. "Managed social media accounts and created content"
2. "Ran email campaigns and tracked results"
3. "Worked with the design team on marketing materials"

MY ACTUAL EXPERIENCE (use this context to make the bullets specific):
- I managed Instagram and LinkedIn for a campus startup incubator (800 followers to 2,400 in 6 months)
- I ran a 4-email welcome sequence for new incubator applicants that had a 34% open rate
- I created briefs for a student designer and we produced 12 carousel posts that drove 40% of our application traffic

INSTRUCTIONS:
- Rewrite each bullet with quantified impact (use the real numbers above)
- Mirror the job posting's language where authentic (do not force-fit buzzwords)
- Use strong action verbs: Grew, Designed, Launched, Optimized, Collaborated
- Keep each bullet under 2 lines
- Maintain first-person implied (no "I" — standard resume format)

Generate a realistic interview practice session for a specific role.

ROLE: Associate Product Manager at Stripe (new grad program)
INTERVIEW STAGE: Second round — product sense interview (45 minutes)

WHAT I KNOW ABOUT STRIPE'S INTERVIEW PROCESS:
- They value structured thinking and clear communication
- Product sense interviews focus on product design and strategy, not coding
- They look for candidates who can break down ambiguous problems

MY BACKGROUND: CS degree, 1 summer internship at a fintech startup (built an internal dashboard), led a campus hackathon (200 participants)

GENERATE:
1. Three product sense questions that Stripe might realistically ask, ranging from tactical ("improve this feature") to strategic ("enter this market")
2. For each question, provide:
   - The question itself
   - What the interviewer is evaluating (the rubric)
   - A framework for structuring my answer (not a scripted answer)
   - One common mistake candidates make on this type of question
   - How I could connect my specific experience (hackathon, internship) to strengthen my answer

3. Two follow-up questions the interviewer might ask based on typical candidate responses

FORMAT: Number each question clearly. Use headers for each sub-section.

Help me prepare for my Machine Learning midterm using active recall techniques.

COURSE: Introduction to Machine Learning (undergraduate CS, 300-level)
TOPICS COVERED: supervised learning (regression, classification), decision trees, random forests, SVMs, bias-variance tradeoff, cross-validation, gradient descent

MY CURRENT UNDERSTANDING: I can explain the concepts at a high level but struggle with the mathematical intuition behind gradient descent and the bias-variance tradeoff. I can implement basic sklearn models but cannot explain why one model outperforms another on a given dataset.

WHAT I NEED:
1. Five conceptual questions that test understanding (not memorization) — the kind my professor would put on the exam
2. For each question: the question, a hint I can use if stuck, and a clear explanation I can check my answer against
3. Three "explain it like I am teaching someone" prompts for the concepts I flagged as weak (gradient descent intuition, bias-variance tradeoff)
4. Two dataset scenarios where I need to recommend which model to use and explain why

STYLE: Socratic — ask me to reason through things rather than just giving me information. Challenge my assumptions.