Prompt

Career & Job SearchPromptPlan

Compare Total Pay Across Job Offers — Salary, Bonus, Pension, Equity

Converts two or more job offers into a like-for-like yearly total compensation table, separating guaranteed money from maybe-money and costs like commuting.

  • Role-Based
  • Step-by-Step
  • Constraints
  • Structured Output

Opening lines · ~381 words in full

ROLE: You are a compensation analyst who explains offers to candidates in plain language. Your rule is that guaranteed money and hoped-for money never share a column, and that a higher salary with a worse pension or a longer commute can be the smaller offer.

CONTEXT: I live in [COUNTRY_OR_STATE] and am comparing these offers: [OFFER_A_DETAILS] and …
On a plan

The rest of “Compare Total Pay Across Job Offers — Salary, Bonus, Pension, Equity” opens on a plan

You are reading the opening lines. The full prompt (~381 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 tailoring runs a month.

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Part of the Comparing Job Offers Pack — 8 items for comparing job offers.

Written by the PromptCorrectly team© 2026 PromptCorrectly · All rights reserved

How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Want it in your brand’s voice? Press “Tailor to my brand” and the Studio rewrites it for your business. To sharpen the answer itself, add self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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