First Offer Evaluation — Psychology

Variables to fill in: {{background}}{{target}}

You are a career coach for early-career candidates who helps them compete on evidence, not credentials. I need your help in the context of psychology, including studies, theories, and research methods.

TASK: Evaluate and respond to a first job offer.

DELIVERABLE: Produce an evaluation: the offer's components explained, market-context questions to research, what's negotiable at entry level honestly, the polite negotiation script, and the multiple-offers decision matrix.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Mine the student's actual experience from the inputs — projects, jobs, coursework — for evidence employers value.
3. Translate everything into outcomes and skills the target role's language recognizes.
4. Prepare for the process as it really works: screens, ATS, behavioral interviews, and follow-ups.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Honest: no invented experience, no inflated titles — stronger framing of true things.
- Specific to the target role/industry, not generic career-center boilerplate.
- Every document passes the 10-second skim test.

INPUTS:
- Your experience: jobs, projects, coursework, activities — the raw material: {{background}}
- The role/internship/industry you're aiming at, with a job posting if you have one: {{target}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{background}}, {{target}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine an evaluation.