Verified against ChatGPT · 2026-08-13
Compare two job offers on the factors that will actually matter in year two
Builds a structured offer comparison that weighs total compensation, growth trajectory, and quality-of-life factors against what you said matters most, instead of anchoring on whichever number is biggest in year one.
The prompt
Ready to copy — highlighted parts are example details you can swap.
I have two job offers and need a real comparison — not just base salary side by side, since I know that's not actually the full picture and I don't want to make this decision on the one number that's easiest to compare. OFFER A Base $145k, 10% target bonus (historically paid ~80%), no equity, hybrid 3 days/week, established company, standard promotion cycle every 2 years. OFFER B Base $130k, no bonus, 0.15% equity over 4-year vest (Series B, last valuation $400M), fully remote, faster-moving team, no formal promotion cycle yet. WHAT ACTUALLY MATTERS MOST TO ME RIGHT NOW In order: remote flexibility, long-term earning potential, day-to-day work variety. Least important: brand name recognition. PHASE 1 — NORMALIZE THE COMPENSATION Calculate a genuinely comparable total-compensation figure for each offer, accounting for base, bonus target (state clearly if it's not guaranteed), equity (state the assumptions you're making about vesting and valuation, and flag that startup equity is a real-terms unknown, not a number to treat as cash-equivalent), and any other named compensation component. Show the math, not just the final number, so I can sanity-check your assumptions. PHASE 2 — SCORE THE NON-COMP FACTORS Against the priority factors I listed, score each offer honestly — if one offer is clearly stronger on a factor I said matters most, say so directly rather than finding a way to make both offers sound equally good out of diplomacy. PHASE 3 — PROJECT TWO YEARS OUT, NOT JUST YEAR ONE For each offer, note anything in the details that would change the picture by year two — a defined promotion path, a compensation structure that's front-loaded or back-loaded, a role with limited growth ceiling regardless of the starting numbers. A decision based only on year-one numbers can look different once a longer trajectory is considered. OUTPUT FORMAT 1. A compensation comparison table with the math shown. 2. A priority-factor scoring table (factor, Offer A, Offer B, which is stronger). 3. A short year-two outlook paragraph per offer. 4. A direct recommendation given everything above, stated as a recommendation with reasoning, not a noncommittal "it depends."
Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
Requiring the compensation math to be shown rather than just the final normalized number matters because equity valuations and bonus targets involve real assumptions — a startup equity grant's actual value depends entirely on an exit or valuation event that may never happen, and collapsing that into a single comparable dollar figure without showing the assumption baked in would let the user anchor on a false-precision number that treats speculative equity the same as guaranteed cash, which is a genuinely common and costly mistake in offer comparisons. Instructing the model to score priority factors honestly rather than diplomatically split the difference addresses a specific failure mode in AI-assisted decision support: asked to compare two things a user is emotionally invested in, models often default to a balanced, both-sides framing that avoids taking a clear position, which feels safe but is actually unhelpful when one offer is genuinely and clearly stronger on the exact factor the user said matters most — false balance here just pushes the hard decision back onto the user without giving them anything new. The explicit two-year projection phase exists because job offer comparisons evaluated only on year-one numbers systematically undervalue offers with back-loaded structures (a slower-vesting equity grant, a defined promotion path that kicks in at year two) relative to offers that look stronger purely because they're front-loaded, and a comparison that stops at year one is implicitly comparing the two offers at different points in their actual value curves rather than on equal footing. Ending with a direct recommendation rather than a noncommittal "it depends" matters because the entire point of running this comparison was to get help making the decision — a summary that restates both offers' tradeoffs without taking a position given the user's own stated priorities fails at the one thing the exercise was for.
What you get back
Compensation comparison: Offer A guaranteed cash ~$261k/yr (145k base + 80% of 10% target bonus, historically reliable). Offer B guaranteed cash $130k/yr; the 0.15% equity grant is a real-terms unknown tied to a future exit and should not be treated as equivalent to cash — at the last valuation it implies a theoretical ~$600k over 4 years, but Series B equity commonly returns far less or nothing. Recommendation: Given that remote flexibility ranked as your top priority and Offer B is fully remote versus Offer A's hybrid requirement, and your stated tolerance for equity risk...
Verified against
ChatGPT GPT-5.1 · 2026-08-13
Changelog
- 2026-08-13 — Initial publish, verified against ChatGPT GPT-5.1.
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