The Career Signal Check: Is It the Job, or Is It This Month?
WEEK 100 :: POST 2 :: CHATGPT
Directions Given To The A.I. This Week+
Instructions Given to each A.I. — Please provide 3 prompt variations that share this objective:
Each A.I. also received two static attachments: the blog post template (structure) and the authoring instructions (voice and standards). The text below is the week-specific assignment as sent — reflowed for the web; wording unchanged.
I'd like you to write this week's Ketelsen.ai post. Two files are attached: the blog post template (the structure to follow) and the authoring instructions (context, voice, and standards). Please read both before you begin, then produce the complete post in a single response.
This week's theme: "Should I Even Be Job Searching Right Now?"
This is Week 1 of a new eight-week series on running a job search with AI — the follow-up to "AI at the Dealership," and it shares that series' backbone: a high-stakes, emotionally loaded decision where the other side of the table (employers, recruiters, applicant tracking systems) holds most of the cards. Week 1 starts where honest job searches start: before the résumé. Most people begin applying before they have defined what is actually broken about their current situation, and impulsive moves are how people land in roles they regret within months. The reader's job this week is to decide — stay, grow in place, or go — with evidence instead of a mood.
One thing this week must do that no later week has to: speak to all three readers who arrive at a job-search series. The active-but-employed reader wondering if the grass is greener, the reader who has just been laid off and has no stay option, and the career pivoter for whom "go" means a different field entirely. And name the elephant with care: for some laid-off readers, AI itself is part of the story — restructurings framed around AI efficiency are part of this market. A series about using AI must not be breezy about that; one honest, warm sentence acknowledging it (no layoff statistics — the constraint below already forbids them) buys more trust than a page of enthusiasm, and the laid-off reader should feel seen, not lectured.
Week 1 also opens the series, so its Lead carries the series' editorial frame. The Lead should say plainly what this series is and is not: AI as the reader's private analyst, coach, and thinking partner — never their ghostwriter — because hiring decisions are made by humans who are rightly wary of machine-written material, and employers themselves face legal and compliance limits on AI in hiring. The two lines the series stands on — "Use AI like an analyst, not a ghostwriter" and "AI behind the scenes. You on the page." — belong in or near this Lead, worked into the post's own voice rather than dropped in as slogans.
The prompts should handle the three audiences through a "MY SITUATION" context block the reader fills in — the same pattern the car series used — so one prompt serves all three without pretending they are the same person.
The deliverable the reader should walk away holding: a written stay-or-go decision, a personal compensation baseline (what they earn now, fully loaded), and a timeline — the three artifacts every later week in this series builds on.
THE SERIES CONTRACT — identical every week; it binds every prompt you design. This series' tagline is its editorial contract: "Use AI like an analyst, not a ghostwriter." It is written for a reader in a market unsettled by AI itself — some readers are searching precisely because AI eliminated their last role. Write with that reader at the table: no AI-efficiency cheerleading, no automation jokes, no promises that AI will "do it for you" anywhere a human hiring decision is involved. And hold one line in every prompt: the AI is the reader's private analyst, coach, and sparring partner — it structures, researches, rehearses, and questions. It does not ghostwrite. Anything a hiring human will read or hear — résumé lines, cover letters, outreach messages, interview answers, negotiation emails, resignation letters — must end in the reader's own words and be true. Prompts should drive toward drafts the reader rewrites and owns, and should say so explicitly. Employers increasingly restrict how AI may be used in their own hiring decisions for legal and compliance reasons, and recruiters increasingly recognize — and discard — material that reads machine-written. A prompt that makes a reader look AI-generated hurts them twice. Posts that ignore this contract should expect to lose the week. Two practical notes. First: a standing “About this series” notice covering these same points is added to every published post automatically at publication — acknowledge the frame in your own voice where your week's prompt calls for it, but do not write a formal disclaimer block of your own, and do not open every post with the same acknowledgment paragraph: outside the weeks whose prompts explicitly carry the series frame, this contract lives in your tone and your prompt design. Second, the framing is POSITIVE: used this way — analyst backstage, reader on the page — AI is an advantage no hiring human will ever hold against your reader. Write like that is true, because it is.
The three prompts should help a reader:
- Run an honest career audit. A satisfaction diagnostic that separates "bad month" from "bad fit" — role, manager, growth, compensation, energy — and names what specifically is broken, so the reader is diagnosing before prescribing.
- Inventory their skills and market position. What the reader actually does all day, translated into the language the market hires for, with an honest read on which skills are appreciating and which are aging — the AI structuring the inventory from the reader's own history, not asserting market statistics.
- Build the full stay-vs-go decision framework. The "budget week" of the series: current compensation fully decoded (base, bonus, benefits, the things that quietly vanish on exit), savings runway if the search goes long, the benefits cliff of leaving mid-year, weighed inside a decision framework the reader owns — ending in a written decision and timeline.
At the advanced tier, the strongest version of this week is a decision memo the reader writes to themselves — situation, evidence, options scored, decision, revisit date — produced by a prompt that makes the AI a structured interviewer and analyst rather than an oracle. A decision the reader can re-read in six months beats a vibe either way.
A hard constraint, stated up front for the series. AI models cannot see live labor-market data, current layoff patterns, or real salary postings, and this series' citation standards treat unverified statistics as defects. No prompt may ask the AI to state current hiring trends, layoff figures, salary levels, or "shift shock" regret statistics as fact. Where market reality matters, the prompt should have the reader supply what they know or point them to named sources to check — pay-transparency postings, official labor statistics, their own industry contacts — not have the AI assert numbers. The same applies to the money: the runway analysis works on numbers the reader supplies, and nothing in these prompts is financial advice — the framework organizes the reader's own decision, it does not hand down a verdict.
Design the prompts so the AI does what it is genuinely good at: structured interviewing, translating a work history into market language, organizing a messy emotional decision into evidence and options. The reader supplies their situation and their numbers; the AI supplies structure, candor, and sequence. Posts whose prompts have the AI invent market statistics or deliver quit-your-job verdicts should expect to be marked down on Practical Utility and Content Accuracy.
Series dependency chain, for the Metadata block: Week 1 consumes nothing — it is the series opener. Week 1 produces the written stay-or-go decision, the compensation baseline, and the search timeline — consumed by Week 2 (defining the target), Week 7 (the baseline anchors the negotiation), and Week 8 (the decision criteria return in the final offer matrix).
Because readers may arrive at this post from anywhere, the prompts should work for someone starting cold — no prior artifacts exist yet in this series, so this is the one week with no catching-up to do, and the post can say so as an invitation.
Three difficulty tiers as always — Beginner, Intermediate, Advanced — each a genuinely different approach to the same problem, not the same prompt at three lengths.
On examples: this is a career topic that touches every industry. The template lists tech startup / retail / freelance as suggested industry examples — those are marked MAY, and adapting them is expected here. An employed product manager wondering whether restlessness is a signal, a laid-off retail manager with no stay option, and a freelance designer considering a return to full-time are the right kinds of contexts. Choosing them over the suggested business examples is correct behaviour and will not be scored against you.
A note on supplied figures. Anything marked `[SUPPLIED — use as given]` above came from Ketelsen.ai's own research brief. Use it freely — you are not fabricating by repeating it, and you will not be marked down for leaving it uncited. Do not attach an invented source to it. (No supplied figures this week — the outline's hook statistics are directional and unverified, so none are being handed to you. If you find yourself reaching for a regret percentage or a layoff figure, that is the signal to restructure the sentence so it does not need one.)
## BEFORE YOU SUBMIT — STRUCTURAL CHECK
(This block is identical every week. It exists because these specific items are the ones posts drop, and a dropped structural item costs compliance points for something that takes one minute to add.)
Your post is parsed by a script before any human reads it. Confirm all seven:
1. ☐ Response begins with `PLATFORM: <your name>` and `WEEK: 1` 2. ☐ `## Lead` present once, at the very top, before Variation 1 3. ☐ `## In one line` present in all three variations 4. ☐ `## What this prompt gives you` present in all three variations 5. ☐ `## The Prompt` present in all three variations, with the prompt in double quotes beneath it 6. ☐ `## Introductory Hook` and `## Current Use` present in all three variations (three of each — not one) 7. ☐ Every template heading written as `##`, none bolded instead; prompt breakdown is running text split on ` : `, with no `###` headings inside it
A complete post has 57 `##` headings. If your count is well short, a section is missing or was bolded instead of hashed.
One extra check this week: confirm no prompt asks the AI to state a current hiring trend, layoff figure, salary level, or regret statistic as fact, and none delivers a stay-or-quit verdict. The decision framework organizes the reader's own evidence; market numbers come from the reader or from named sources they check themselves.
About this series. AI is reshaping work — for some readers, it's part of why you're searching at all. We don't pretend otherwise. And the hiring world is wary of AI-written material: many employers restrict how AI may be used in their own hiring decisions, and recruiters increasingly recognize — and discard — machine-written applications. So this series teaches a different approach: use AI as your private research analyst, interview coach, and thinking partner, while every word an employer sees or hears from you stays genuinely, verifiably yours. AI behind the scenes. You on the page.
A job search should begin with a diagnosis, not a résumé. This opening week gives you three ways to decide whether to stay, grow where you are, re-enter after a layoff, or move toward a different field: a fast career signal check, a structured evidence-gathering sprint, and a full stay-or-go decision memo. Throughout this series, use AI like an analyst, not a ghostwriter: AI behind the scenes, you on the page.
The Career Signal Check
Separate a bad stretch from a genuinely bad fit.
A difficult week can make any job look like the wrong job. So can a difficult manager, a stalled promotion, a disappointing review, or the quiet realization that the work no longer fits who you are becoming. The danger is treating every painful signal as proof that you should leave—or dismissing every signal as ordinary frustration. This beginner prompt slows the decision down just enough to identify what is actually broken, what might be repairable, and what still needs evidence. It works whether you are employed, recently laid off, or considering a career pivot.
Why this matters now
Before you update a résumé or start applying, you need a working theory about the problem you are trying to solve. Otherwise, you can spend months chasing jobs that reproduce the same manager problems, energy drain, compensation mismatch, or lack of growth under a different company name. For readers whose roles ended in restructurings connected to AI, this decision may carry grief as well as urgency; the prompt makes room for that without turning the experience into a productivity lesson. Its purpose is not to tell you what to do. It is to help you see your own evidence clearly enough to choose a responsible next step.
You are my private career reflection coach and decision-structuring assistant. Your role is to help me think clearly, not to decide for me and not to write job-search materials in my voice.
Begin by asking me the questions below one at a time. Do not analyze until I have answered all of them. Ask a brief follow-up whenever my answer is vague, contradictory, or based mostly on a recent emotional event.
MY SITUATION
Employment status: [employed and considering a search / recently laid off / returning after a break / considering a career pivot]
Current or most recent role:
Industry or field:
How long I have been in this situation:
What made me question my current direction:
What I hope would be different:
Immediate financial or personal constraints:
Questions to ask me:
1. What parts of the work give me energy, and what parts consistently drain it?
2. Is the main problem the work itself, my manager, the organization, growth, compensation, workload, stability, or something outside work?
3. How long has the problem been present, and what evidence shows it is persistent rather than temporary?
4. What have I already tried to improve the situation?
5. Which parts could realistically improve without changing employers or fields?
6. What would I lose by leaving, including pay, bonus, benefits, retirement contributions, paid time off, flexibility, relationships, seniority, vesting, or stability?
7. What would I risk by staying unchanged for another year?
8. Which skills do I use regularly, and which skills do I want to use more?
9. What constraints affect my timeline, such as savings, health coverage, caregiving, location, immigration status, or recovery from a layoff?
10. What decision would I make if I had to choose today, and what fear is influencing that answer?
After the interview, organize my answers into four categories:
- Temporary pressure
- Repairable mismatch
- Structural mismatch
- Unknown or needs evidence
Then give me:
1. A concise diagnosis of what appears to be broken, based only on what I told you.
2. A stay, grow-in-place, or go leaning. Present it as a provisional interpretation, not a verdict.
3. The three strongest pieces of evidence supporting that leaning.
4. The three biggest uncertainties that could change it.
5. A simple compensation snapshot listing the categories I should total before making a move. Use only numbers I provide. Do not estimate salary, benefits, hiring conditions, or market demand.
6. A 30-day plan containing no more than five actions. Include at least one action that tests whether my current situation can improve and at least one action that gathers outside evidence.
7. A fill-in-the-blank decision statement I must complete in my own words:
For the next [time period], I will [stay, grow in place, search, recover, or pivot] because [evidence]. Before I reconsider, I will gather [missing evidence] and protect [important constraint].
Do not write a résumé, cover letter, outreach message, interview answer, resignation letter, or any other material a hiring human would read. You may help me analyze or outline those later, but the final language must be mine and must be true.
Do not claim current hiring trends, layoff figures, salary levels, or skill demand as facts. When outside information is needed, tell me what to verify through sources such as pay-transparency job postings, official labor statistics, benefits documents, or people working in the field.
How the AI reads this prompt
Practical examples from different industries
An employed product manager at a midsize software company has felt restless for six months. She enters that her manager is supportive, compensation is acceptable, and the work is stable, but she is no longer learning and spends most of her time coordinating rather than building. The prompt separates a structural role-design problem from a company problem. Her 30-day plan includes requesting ownership of a neglected product area, reviewing ten relevant job descriptions for recurring responsibilities, and speaking with two product leaders outside her company. The value is not an immediate leave decision; it is a test of whether growth can happen where she already is.
A retail operations manager was laid off after a regional restructuring. He has no stay option, but he still needs to decide whether to seek a similar role immediately, use a bridge job, or move toward logistics and supply-chain operations. The prompt marks staying as not applicable and focuses on energy, transferable skills, cash constraints, benefits timing, and the cost of a longer pivot. His output may lean toward a two-track plan: pursue adjacent operations roles now while testing a supply-chain transition through targeted conversations and coursework. The prompt gives him structure without pretending that job loss was a voluntary career experiment.
A freelance designer is considering returning to full-time employment after several years of independent work. She enjoys creative control but dislikes unpredictable revenue, sales work, and the isolation of working alone. The diagnostic reveals that she does not want to leave design; she wants to change the operating model around it. Her compensation snapshot includes not only desired salary, but also health insurance, paid time off, retirement contributions, software expenses, unpaid administrative time, and the value of schedule control. That broader comparison helps her evaluate employment without treating salary as the whole decision.
Creative use case ideas
- Use the prompt after receiving a promotion that looks good on paper but changes the work in an unwanted direction. - Run it before enrolling in an expensive degree, boot camp, or certification intended to support a career pivot. - Adapt it for a military-to-civilian transition where job titles, benefits, and professional identity may all change at once. - Use it with a partner or family member to separate shared financial constraints from personal career preferences. - Revisit it after a leave of absence, caregiving period, health event, or major life change that alters what sustainable work looks like.
Adaptability tips
For a faster version, reduce the interview to questions 1, 2, 3, 6, 7, and 9. This produces a useful first pass when the reader is overwhelmed or newly laid off.
For a deeper version, ask the AI to label every conclusion as direct evidence, interpretation, assumption, or missing information. That addition makes it easier to see where emotion or guesswork has entered the analysis.
Readers considering a career pivot can replace grow in place with test an adjacent field and ask for separate evidence plans for the current field, the adjacent field, and the more ambitious pivot. Employed readers can ask for a low-visibility version of the 30-day plan that protects confidentiality.
The compensation snapshot can also be adapted into a household decision. Add monthly essential spending, partner income, dependent-care costs, insurance deadlines, and a minimum acceptable cash buffer, while keeping all figures reader-supplied.
Pro tips
- Answer the interview in two sessions: one immediately, then one after forty-eight hours. Ask the AI to compare what changed. Stable conclusions are usually more useful than the strongest emotion from a single day. - Add a confidence score from 1 to 5 beside every conclusion, along with the evidence required to raise the score. - Ask the AI to identify the one assumption that, if false, would most change the decision. That reveals where a small amount of research may prevent a large mistake. - Save the final output with a revisit date. A provisional decision becomes more trustworthy when you know exactly when you will examine it again.
Prerequisites
You need fifteen to thirty minutes, a willingness to answer candidly, and basic information about your current or most recent work situation. Have recent compensation and benefits documents nearby if possible, but exact figures are not required for the first pass. Recently laid-off readers may also want their separation paperwork, health-insurance deadlines, and current monthly spending available. No prior artifact from this series is required.
Required tools
Any general-purpose conversational AI that can conduct a multi-turn interview is sufficient. A free tier can handle the prompt, although a model with a larger context window may be more useful if the reader provides lengthy career history. A calculator or spreadsheet is optional for totaling compensation. No browsing capability is required because the prompt does not ask the AI to supply live market facts.
Frequently asked questions
What if I already know I want to leave?
Use the prompt anyway, but change the goal from deciding whether to leave to defining what must be different next time. People often know that the current situation is unsustainable before they understand which features caused the problem. The diagnostic can prevent a repeat by identifying the manager style, work pattern, compensation issue, ethical concern, instability, or energy drain that should become a future screening criterion. You are not asking the AI for permission to leave; you are extracting lessons from the evidence.
What if I was laid off and staying is not an option?
Mark staying as not applicable and treat the decision as one among re-entering the same field, moving to an adjacent role, using a bridge strategy, or pursuing a larger pivot. The prompt should not make you defend a choice you did not control. It should help you separate immediate financial needs from longer-term career direction and build a timeline that respects both. It is also reasonable to include recovery, grief, or reduced decision capacity as current constraints rather than pretending every week must be optimized.
Can the AI calculate what my current job is really worth?
It can organize and total figures you provide, but it should not invent missing values. Start with base pay, expected bonus, employer retirement contributions, health-insurance costs, paid time off, equity, allowances, professional development, commuting costs, schedule flexibility, and benefits that may disappear when you leave. Some items have a clear dollar value; others should remain labeled as qualitative. The goal is a personal baseline, not a universal market estimate.
What if the result changes depending on my mood?
That is useful information. Run the prompt twice on different days and ask the AI to identify which answers remained stable and which changed. Stable patterns deserve more weight, while highly variable answers may indicate temporary pressure, incomplete evidence, or a need for rest before making a major decision. A decision process should be robust enough to survive more than one difficult afternoon.
Recommended follow-up prompts
1. Ask AI to turn the compensation categories from this prompt into a blank worksheet that uses only your figures and clearly separates annual, monthly, one-time, and non-cash value.
2. Ask AI to design a fourteen-day evidence plan for testing the three biggest uncertainties in your career diagnosis without drafting any messages in your voice.
3. Continue to Week 2 of the AI on the Job Hunt series to define the target role using the decision, constraints, and timeline produced here.
Tags and categories
Tags:
career audit, job-search decision, stay or go, career pivot, layoff recovery, compensation baseline, AI career coach
Categories:
Career Planning, Practical AI Prompts
Citations
NOT APPLICABLE
The Career Evidence Sprint
Test your career assumptions before turning them into applications.
A career decision can feel thoroughly researched when it is really built from three inputs: one difficult experience, a few job listings, and what someone said at dinner. The intermediate approach treats your assumptions as hypotheses to test. Instead of asking AI whether you should search, you ask it to help you gather better evidence from your own experience, internal possibilities, public job descriptions, compensation documents, and conversations with people who understand the work. The result is a short research sprint that ends with a decision based on observations you can inspect—not a recommendation generated from thin context.
Why this matters now
This prompt is useful when you are not ready for a full decision memo but do not trust a quick gut check. It gives employed readers a discreet way to test whether growth is possible internally, gives laid-off readers a structured way to compare re-entry and pivot options, and gives career changers a way to investigate a field before spending heavily on training. The AI does not provide labor-market conclusions. It helps you design the research, capture the evidence, and distinguish what you verified from what you merely assumed.
Act as a career research facilitator. Help me test whether I should stay, grow in place, begin an external search, re-enter my prior field, or explore a career pivot.
You are not an oracle, recruiter, financial adviser, or ghostwriter. Do not decide for me. Do not write anything in my voice that a manager, recruiter, employer, networking contact, or interviewer would receive. You may help me prepare questions and analyze my findings, but I must rewrite and own all outward-facing language.
Use only information I provide. Do not state current salary levels, hiring trends, layoff patterns, skill demand, or job-search statistics as facts. When market evidence matters, tell me how to verify it through named source types such as pay-transparency postings, official labor statistics, company career pages, benefits documents, professional associations, or conversations with people doing the work.
MY SITUATION
Employment status:
Current or most recent role:
Field or industry:
Decision I am considering:
Deadline or urgency:
Financial constraints:
Geographic or scheduling constraints:
What I currently believe is wrong:
What I hope another role or field would improve:
Evidence I already have:
First, convert my situation into three to five testable hypotheses. Use this format:
Hypothesis:
Why I currently believe it:
Evidence that would support it:
Evidence that would weaken it:
Fastest ethical way to test it:
Then interview me about five areas:
1. Role and task fit
2. Manager and organizational fit
3. Growth and skill development
4. Compensation and benefits
5. Energy, health, values, and life constraints
After the interview, build a fourteen-day Career Evidence Sprint with no more than eight actions. Choose from the action types below only when appropriate:
- Review my calendar, work log, or completed projects for recurring energy gains and drains.
- Compare my actual weekly tasks with the tasks described in roles I am considering.
- Review internal roles, stretch assignments, or development options.
- Examine public job descriptions and pay-transparency postings that I personally supply.
- Inventory my full compensation using my documents and numbers.
- Conduct informational conversations using questions I rewrite in my own words.
- Test a target skill through a small project, class sample, volunteer task, or portfolio exercise.
- Identify benefit deadlines, vesting dates, bonus timing, or other exit costs from my documents.
- Calculate my financial runway using only my supplied income, savings, and expense figures.
- Compare an immediate-search path with a slower exploration or pivot path.
Create an evidence log with these columns:
Date | Question tested | Source | Observation | Direct fact or interpretation | Confidence | Effect on decision
Create a compensation baseline with these sections:
- Base pay or current income
- Bonus or variable pay
- Retirement contributions
- Health, dental, and other insurance
- Paid time off and leave
- Equity or vesting
- Allowances, education, and professional expenses
- Commuting or work-location costs
- Flexibility and schedule value
- Benefits or protections that may end on exit
- One-time transition costs
- Unknown values to verify
Do not fill missing amounts with estimates.
At the end of the sprint, help me produce:
1. A one-paragraph problem statement based on verified evidence.
2. A table of hypotheses marked supported, weakened, unresolved, or disproved.
3. A comparison of my realistic options.
4. A provisional stay, grow, search, re-enter, or pivot direction.
5. A minimum acceptable compensation and conditions checklist based only on my baseline and priorities.
6. A ninety-day timeline with decision gates at Day 14, Day 30, Day 60, and Day 90.
7. A short decision record containing:
- What I decided
- What evidence mattered most
- What I am still uncertain about
- What would cause me to revise the decision
- The date I will review it again
End by asking me to rewrite the problem statement and final decision in my own words. Do not treat the process as complete until I confirm that the language is accurate and genuinely mine.
How the AI reads this prompt
Practical examples from different industries
A hospital department supervisor is considering leaving because of schedule instability, administrative load, and limited advancement. Her first hypothesis is that the profession itself no longer fits; a competing hypothesis is that her current department and shift structure are the real problem. The sprint compares her actual weekly tasks with internal administrative roles and external healthcare-operations descriptions she selects. She also reviews pension, leave, insurance, and seniority implications before treating a higher salary elsewhere as an automatic improvement. At Day 14, she may decide to pursue internal operations roles first while gathering evidence about external options.
A laid-off retail manager believes his experience is too industry-specific to transfer. The AI converts that belief into testable hypotheses about workforce scheduling, inventory control, vendor coordination, loss prevention, coaching, and multi-site operations. He supplies several logistics and customer-operations job descriptions, then records where his experience matches, partially matches, or lacks evidence. A short volunteer project helps him test whether he enjoys process-oriented work outside a store environment. The sprint may weaken the belief that he must start over, while still identifying terminology and technical gaps he needs to address himself.
A freelance web designer is deciding whether to continue independently or join an in-house creative team. Her sprint logs two weeks of paid design time, unpaid sales work, revisions, administration, isolation, and schedule flexibility. She calculates current income alongside software, insurance, unpaid leave, retirement funding, and business-development time. She also reviews several employer postings she provides to compare actual task mix rather than job titles alone. The result may show that she wants employment stability but still needs meaningful autonomy, making team structure and creative ownership explicit decision criteria.
Creative use case ideas
- Compare an internal promotion with an external move instead of assuming promotion is automatically the safer choice. - Test whether dissatisfaction comes from a profession, a specialty, a work setting, or a single organization. - Evaluate a return to work after caregiving by testing schedule, commute, benefits, and skill-refresh assumptions before applying. - Use the evidence log to compare graduate school, certification, apprenticeship, and direct job-search paths. - Adapt the sprint for a creative collaboration, volunteer leadership role, or long-term personal project where the real question is whether to recommit, redesign, or stop.
Adaptability tips
For an employed reader who needs discretion, remove any action involving internal conversations and replace it with document review, private work-history analysis, and public information the reader gathers independently. The sprint should never require exposing a search prematurely.
For a laid-off reader with immediate financial pressure, divide the sprint into two tracks: near-term income and long-term direction. The first track can evaluate realistic bridge roles and application priorities; the second can test the pivot without making the entire household wait for certainty.
For a senior leader, add organizational scope, decision authority, political environment, travel, executive exposure, and reputational risk to the interview. For an early-career reader, emphasize supervision quality, skill accumulation, portfolio evidence, and access to meaningful work.
For a career pivoter, ask the AI to distinguish transferable capability from unfamiliar vocabulary. Then require a small real-world test of the target work before recommending a large investment in education.
Pro tips
- Use separate evidence labels for documents, direct observation, conversation, and inference. A conclusion supported by several independent source types is usually more robust than one supported by repeated versions of the same claim. - Add a red-team pass on Day 14: ask the AI to make the strongest evidence-based case against your current leaning. - Preserve the original evidence log before asking for a summary. That gives you a record you can revisit if the summary overemphasizes one theme. - For every action, include the decision it is intended to inform. If an activity will not change a decision, remove it from the sprint.
Prerequisites
Set aside enough time to complete several small actions across fourteen days. Gather a recent résumé or work-history document for reference, a calendar or task log, compensation and benefits documents, and any job descriptions you want to examine. Recently laid-off readers should also have separation terms, insurance deadlines, unemployment information, and a realistic monthly-expense figure. Do not provide confidential employer information, protected personal data, or materials you are not authorized to use.
Required tools
A conversational AI with enough context capacity to retain the interview, hypotheses, evidence log, and decision record is recommended. A spreadsheet is useful for the evidence log and compensation baseline, although a plain document also works. Access to documents and postings selected by the reader is helpful. Browsing is optional because the AI should analyze sources the reader verifies rather than make unsupported claims from memory.
Frequently asked questions
Do I need to complete every action in fourteen days?
No. The sprint should contain the smallest set of actions capable of changing the decision. If one conversation, one document review, and one task comparison resolve the central uncertainty, stop gathering evidence and move to the decision gate. The point of the time box is to prevent unstructured rumination, not to reward activity for its own sake. Record any skipped action and why it no longer matters.
How should I use public job descriptions without treating them as perfect evidence?
Treat each description as evidence about one employer’s stated needs, not proof of the entire market. Compare several descriptions you personally select and note recurring tasks, terminology, requirements, and ambiguities. Separate what the listing explicitly says from what you infer about the role. A posting can help generate questions and hypotheses, but it cannot tell you whether the actual job, manager, or culture will match the advertisement.
Can AI help me prepare for informational conversations?
It can help you identify the questions that would test a hypothesis, but the final wording should be rewritten in your own voice. Keep the conversation honest and avoid pretending to seek advice when your real purpose is to ask for a referral. Afterward, give the AI your notes and ask it to distinguish direct statements, your interpretation, and unanswered questions. Do not upload private messages or sensitive details without permission.
What if the sprint produces mixed evidence?
Mixed evidence is normal and often more credible than a perfectly clean answer. Identify which criteria are non-negotiable, which are preferences, and which uncertainties can only be resolved after taking a reversible step. You may choose a staged decision, such as exploring externally while testing an internal change. The final record should say what remains uncertain rather than forcing false certainty.
Recommended follow-up prompts
1. Ask AI to create a blank evidence log and compensation worksheet from the structures in this prompt, with no sample market figures.
2. Ask AI to red-team your provisional decision using only the evidence log, clearly distinguishing facts, interpretations, and unsupported assumptions.
3. Use the Advanced variation below to convert the sprint results into a weighted decision memo with a revisit date.
Tags and categories
Tags:
career research, evidence sprint, job-search planning, career pivot, compensation analysis, skills inventory, decision framework
Categories:
Career Planning, AI Workflows
Citations
NOT APPLICABLE
The Stay-or-Go Decision Memo
Turn career uncertainty into an auditable decision you can revisit.
The hardest career decisions are rarely missing information altogether. They contain too much information of uneven quality: compensation, identity, fatigue, ambition, health coverage, vesting, family needs, market uncertainty, loyalty, fear, and the suspicion that the next role could solve one problem while creating three others. The advanced prompt treats the decision as a memo you write to your future self. AI conducts the interview, organizes evidence, tests assumptions, calculates only from supplied numbers, and challenges the leading option. You remain the decision-maker, the source of truth, and the author of the final conclusion.
Why this matters now
This approach is for decisions with meaningful financial, personal, or professional consequences. It accommodates an employed reader deciding among staying, negotiating change, and searching; a laid-off reader comparing re-entry, bridge, and pivot strategies; and a career changer weighing the cost and reversibility of a transition. The memo is designed to survive the emotional swing of a good day or bad day. Six months later, the reader should be able to see what was known, what was assumed, why the choice was made, and what conditions were supposed to trigger reconsideration.
Act as a rigorous career decision analyst, structured interviewer, and respectful devil’s advocate. Help me build a decision memo about my next career move.
The memo is mine. You do not have authority to tell me to quit, stay, accept, reject, or change careers. You must distinguish facts I provide, calculations from my figures, interpretations, assumptions, and unknowns.
You are not my ghostwriter. Do not produce final résumé language, cover letters, networking messages, interview answers, negotiation emails, resignation letters, or statements that another person will receive as though I wrote them. You may create analytical scaffolds, question lists, and rough internal outlines. Any outward-facing language must be rewritten, verified, and owned by me.
Do not state current hiring trends, layoff figures, salary levels, skill demand, or career-regret statistics as facts. Do not assume access to live labor-market information. When outside evidence is required, specify the source type I should check and leave the field unresolved until I provide verified information.
MY SITUATION
Employment status:
Current or most recent role:
Industry or field:
Decision deadline:
Primary reason this decision exists:
Options I am currently considering:
Option that feels emotionally safest:
Option that feels most attractive:
Immediate financial constraints:
Health, caregiving, location, immigration, or family constraints:
Information I do not want shared or processed:
PHASE 1 — DEFINE THE DECISION
Interview me until you can write a neutral decision question in one sentence. Do not allow the question to assume that leaving, staying, or pivoting is already correct.
Identify the realistic option set. Consider these only when relevant:
- Stay as-is for a defined period
- Stay and seek a specific internal change
- Search externally in the same field
- Search externally in an adjacent field
- Pursue a career pivot
- Use a bridge role or temporary income strategy
- Pause for recovery, caregiving, health, or skill-building
- Combine two options in a staged plan
For a laid-off person, mark stay options not applicable rather than forcing them into the analysis.
PHASE 2 — BUILD THE EVIDENCE BASE
Interview me about:
- Work content and task fit
- Manager, team, and organizational conditions
- Growth and skill development
- Compensation and benefits
- Stability and risk
- Energy, health, values, and sustainability
- Identity, status, and emotional attachment
- Household and life constraints
- Reversibility and opportunity cost
- Evidence from prior attempts to improve the situation
Create an Evidence Register:
ID | Claim | Source | Fact / calculation / interpretation / assumption / unknown | Confidence 1-5 | Decision relevance | Verification needed
Challenge any claim that uses words such as always, never, everyone, no opportunity, guaranteed, impossible, or obviously.
PHASE 3 — CREATE MY FULL COMPENSATION BASELINE
Use only my documents and numbers. Do not estimate missing values.
Organize:
1. Base pay or average current income
2. Bonus, commission, overtime, or variable pay
3. Retirement contributions or pension value
4. Health, dental, vision, disability, and life insurance
5. Paid time off, holidays, leave, and sabbatical value
6. Equity, vesting, deferred compensation, or profit sharing
7. Education, equipment, memberships, allowances, and reimbursements
8. Commuting, parking, travel, meals, wardrobe, and work-location costs
9. Schedule flexibility, remote-work value, and predictability
10. Severance, notice, unemployment, or transition protections
11. Benefits, vesting, bonus, deductible, or leave consequences tied to exit timing
12. One-time costs of searching, relocating, retraining, licensing, or changing fields
13. Unknown values that require documents or professional advice
Show calculations transparently. Keep qualitative benefits separate from dollar totals unless I assign a value.
Create three figures:
- Current annual cash compensation
- Current annual employer-paid or non-cash value that I can substantiate
- Personal replacement threshold, which is my chosen minimum rather than a market claim
PHASE 4 — TEST FINANCIAL RESILIENCE
Using only figures I provide, calculate:
- Essential monthly spending
- Search or transition budget
- Liquid savings available for the transition
- Income expected during the transition
- Estimated runway under my own scenarios
- Dates when insurance, vesting, bonus, leave, or other benefits may change
Present at least three user-defined scenarios, such as conservative, expected, and stressed. State that the results are planning calculations, not financial advice. Identify which figures require verification by a benefits, tax, legal, immigration, or financial professional.
PHASE 5 — WEIGHT THE DECISION
Ask me to allocate 100 points across the criteria that matter to me. Offer a starting list but require me to approve or modify it:
- Financial security
- Compensation
- Work content
- Growth
- Manager and team
- Stability
- Flexibility
- Health and energy
- Values and ethics
- Location
- Family impact
- Identity or purpose
- Reversibility
- Long-term option value
Define a 1-5 scoring scale in concrete language. Score each option only from supplied evidence. Show the reason for every score and mark weakly supported scores.
Run sensitivity tests:
- Which option wins if my three largest weights each change by plus or minus 25 percent?
- Which single uncertain score most affects the result?
- Which option performs best under the stressed financial scenario?
- Which option is easiest to reverse?
- Which option preserves the most future choices?
Do not hide disagreement between the weighted result and my emotional preference. Explain the disagreement without treating either as automatically correct.
PHASE 6 — RED-TEAM THE LEADING OPTION
For the leading option:
1. Write the strongest evidence-based case against it.
2. Identify the assumptions that must be true for it to work.
3. Conduct a pre-mortem: imagine it failed twelve months from now and list plausible causes.
4. Identify early warning signs.
5. Identify safeguards or experiments that reduce risk.
6. State what evidence would cause a different option to become preferable.
Repeat a shorter red-team pass for the second-place option.
PHASE 7 — BUILD THE TIMELINE
Create a ninety-day action plan with decision gates. Adapt it to my employment status and urgency.
Include:
- Immediate protections in the next 72 hours
- Evidence to gather in the first 14 days
- Compensation and benefits facts to verify
- Internal tests or conversations, when appropriate
- External research I will conduct
- Skill or role experiments
- Application or networking activity only after the target is defined
- Day 14 decision gate
- Day 30 decision gate
- Day 60 decision gate
- Day 90 decision gate
- A stop rule for unproductive activity
- A revisit date for the full decision
PHASE 8 — PRODUCE THE DECISION MEMO
Draft an internal memo with these sections:
Decision question
Situation
Constraints
Evidence
Compensation baseline
Options considered
Criteria and weights
Option scores
Sensitivity analysis
Risks and pre-mortem
Decision
Ninety-day plan
Unknowns and verification needs
Revisit date
In the Decision section, do not supply the final conclusion for me. Instead provide a structured blank I must complete:
I choose to [decision] for the period ending [date].
The evidence that matters most is [evidence].
The costs and risks I accept are [costs and risks].
Before acting, I must verify [unknowns].
I will reconsider if [triggers].
My first three actions are [actions].
After I complete it, audit my wording for contradictions, unsupported claims, or commitments that do not match the evidence. Do not rewrite it into a more polished voice unless I ask for an internal clarity edit. End by reminding me that any language shared with another person must be rewritten in my own words and checked for truth.
How the AI reads this prompt
Practical examples from different industries
A cybersecurity incident-response manager is considering an external move after repeated on-call strain and limited advancement. His current compensation includes salary, bonus, retirement match, health coverage, paid certification costs, remote flexibility, and substantial paid leave. The decision memo separates the value of leaving the current on-call structure from the risks of losing flexibility and vesting. A weighted model initially favors an external search, but sensitivity analysis shows that the result depends heavily on an unverified assumption about schedule quality elsewhere. His first action becomes evidence gathering, not resignation.
A laid-off manufacturing operations director is choosing among an immediate search for similar leadership roles, a lower-level bridge position, and a pivot toward supply-chain consulting. The memo marks stay as not applicable, calculates runway from figures he provides, and models conservative and stressed timelines. The bridge option scores lower on status and compensation but higher on speed, reversibility, and household stability. A staged decision may emerge: pursue director-level roles for a defined period while preparing a bridge path with a clear trigger date. The analysis respects urgency without allowing urgency to erase longer-term choices.
A public-school teacher is considering leaving education for instructional design. Her advanced memo distinguishes dissatisfaction with workload and institutional constraints from continued enjoyment of teaching, curriculum design, and learner support. She supplies her salary, pension contributions, insurance, leave, contract dates, and training costs. A small portfolio experiment and conversations with practitioners provide evidence about the target work, while the pre-mortem tests the risk that she is attracted to an imagined version of the field. The final decision may be a one-year staged pivot rather than an immediate exit.
Creative use case ideas
- Evaluate whether to accept a counteroffer after resigning, using the original reasons for leaving as evidence rather than letting the new salary dominate. - Compare a prestigious role with a less visible role that offers better health, flexibility, or future option value. - Use the memo before becoming a full-time artist, writer, caregiver, student, or nonprofit volunteer, where the financial and identity tradeoffs extend beyond employment. - Revisit a previous career decision and compare what actually happened with the original assumptions, improving future judgment. - Adapt the framework to a major non-career choice such as relocation, graduate school, or a long-term creative commitment.
Adaptability tips
Readers who dislike numerical scoring can retain the evidence register, option set, red-team pass, and decision memo while replacing weighted scores with qualitative ratings. The method should support judgment, not create an illusion that arithmetic can make the decision automatically.
For decisions involving immigration, employment law, taxes, pensions, disability benefits, severance, or health coverage, add a verification gate that requires guidance from an appropriate qualified professional. The AI can organize questions and documents but should not resolve regulated or individualized issues.
For couples or households, have each decision-maker weight the criteria independently before comparing results. Differences in weights often reveal the real disagreement more clearly than debating a job title or salary figure.
For a rapid version, complete Phases 1, 2, 3, 6, and 8 first. Add the full financial scenarios and weighted scoring only if the decision remains uncertain or the consequences justify the extra work.
Pro tips
- Freeze the Evidence Register before scoring. Otherwise, the reader or AI may reinterpret evidence after seeing which option is winning. - Score uncertainty separately from attractiveness. A promising but poorly understood option should not automatically outrank a known option because its disadvantages are still invisible. - Run the process with names removed from the options—for example, Option A, B, and C—then reveal them after initial scoring. This can reduce status and identity bias. - Schedule the revisit date before acting. A decision without a review point tends to become permanent through inertia, even when its original assumptions expire.
Prerequisites
Prepare a private workspace and enough uninterrupted time for a multi-stage analysis. Gather compensation and benefits documents, recent work-history information, household spending figures, relevant deadlines, and any external evidence you have personally verified. Decide what confidential or sensitive information should not be entered into an AI system. Complex financial, legal, immigration, tax, pension, disability, or benefits questions may require qualified professional review.
Required tools
A capable conversational AI with a large context window is recommended because the workflow contains an interview, evidence register, calculations, scoring, sensitivity analysis, red-team review, and final memo. A spreadsheet is strongly recommended for calculations and scenario testing. A private document repository may help preserve evidence and versions. Use a calculator for arithmetic verification, and consult appropriate professionals when the decision involves regulated or individualized advice.
Frequently asked questions
Does the highest weighted score determine the decision?
No. The score is a tool for exposing tradeoffs, not a machine-issued verdict. A narrow win may disappear under sensitivity testing, and a strong emotional objection may reveal a missing criterion or unverified risk. The reader should examine why the option scored highest, how robust the result is, and whether the model reflects what actually matters. The final decision remains a judgment supported by the analysis.
How accurate is the financial runway calculation?
It is only as accurate as the figures and assumptions you provide. Use transparent scenarios rather than a single precise forecast, and keep one-time expenses, variable income, taxes, insurance changes, and emergency reserves visible. The model should show every calculation so you can verify it independently. Treat the result as a planning aid and seek qualified advice where appropriate.
What if the emotional preference and analytical result disagree?
Do not force them to agree. First check whether the weighting system omitted identity, safety, health, meaning, family impact, or another important criterion. Then test whether the emotional preference depends on an unsupported fear or idealized future. The disagreement may be the most important finding in the memo because it shows where more reflection or evidence is needed.
How often should I revisit the memo?
Use the date specified in the decision and revisit sooner if a trigger occurs. Triggers might include a manager change, layoff notice, health issue, expiring benefit, new internal opportunity, unexpected offer, household change, or evidence that invalidates a key assumption. A memo is not valuable because it freezes a decision forever. It is valuable because it records why the decision made sense at a particular time.
Should I upload confidential employer documents to the AI?
Not automatically. Review your employer’s policies, the AI service’s data controls, and the sensitivity of the information before entering anything. You can often extract only the necessary figures or create a redacted summary instead of uploading a complete document. Never include protected, proprietary, personal, customer, patient, or security-sensitive information that you are not authorized to process.
Recommended follow-up prompts
1. Ask AI to create a blank, auditable compensation-baseline spreadsheet structure from Phase 3, with formulas but no assumed values.
2. Ask AI to run a separate red-team review of the completed decision memo, using only the Evidence Register and explicitly flagging every unsupported conclusion.
3. Continue to Week 2 with the written decision, personal compensation baseline, and timeline so the next prompt can define a target role that actually solves the diagnosed problem.
Tags and categories
Tags:
decision memo, stay or go, career strategy, compensation baseline, financial runway, weighted decision, career pivot, risk analysis
Categories:
Career Strategy, Advanced AI Workflows
Citations
NOT APPLICABLE
Which of the three should you use?
The Beginner variation is the right starting point when the reader needs clarity more than machinery. It distinguishes temporary pressure, repairable mismatch, structural mismatch, and missing evidence, then produces a limited 30-day plan. It is especially useful for someone who has not yet organized the problem or who is too overwhelmed for a multi-stage analysis. Its weakness is deliberate: it produces a directional leaning, not a deeply tested decision.
The Intermediate variation is for readers who distrust both impulse and endless reflection. It converts beliefs into hypotheses, builds a fourteen-day research sprint, preserves evidence in a log, and creates decision gates. Choose it when the main obstacle is not lack of self-awareness but lack of verified information. It is also the strongest choice for testing a career pivot before committing to expensive training or a long search.
The Advanced variation is appropriate when the decision has substantial financial, benefits, family, health, identity, or long-term consequences. It combines an evidence register, full compensation baseline, runway scenarios, weighted criteria, sensitivity analysis, red-team testing, and a ninety-day plan. The extra structure is useful only when the stakes justify it. All three variations follow the same contract: the reader supplies the truth and makes the decision; AI supplies structure, questions, calculations from supplied figures, and disciplined challenge.
TAGS:
