WEEK 106 :: POST 1 :: GOOGLE GEMINI
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: "The Offer — Negotiating From Evidence, Not Anxiety."
This is Week 7 of an eight-week series on running a job search with AI — the negotiation week, and the emotional and financial peak of the series, exactly as it was in the car series. An offer is on the table. The reader's heart says grab it before they change their minds; six weeks of artifacts say otherwise. Employers broadly expect candidates to negotiate — yet first-timers routinely accept the opening number, and the cost of that compounds over a career the way a bad interest rate compounds over a loan: every future raise, bonus percentage, and next-job offer anchors on the number the reader accepts this week.
The reader is not walking in empty-handed. Week 1 set the compensation baseline. Week 2 wrote the deal-breakers. Week 4 built the company dossier. Week 6's interviews revealed how much they want this reader. Negotiation is where the whole series' evidence gets spent.
The deliverable the reader should walk away holding: a negotiation plan with a researched target range, a walk-away number, and scripted responses for the conversations and emails ahead.
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:
- Decode the total offer. Base, bonus, equity, benefits, and the fine print translated into real comparable dollars — vesting schedules that gate the money, bonus targets versus bonus reality, what the benefits actually replace — from the offer documents the reader supplies, ending in a one-page picture of what is actually being offered.
- Benchmark it against evidence the reader gathers. A research structure for building the market case: which pay-transparency postings, published salary data sources, and leveling comparisons to pull, how to weigh them for the reader's role, level, and metro, and how to assemble them into a defensible target range — the AI organizing evidence the reader collects, not asserting what the role pays.
- Script the negotiation itself. Word-for-word: the counter delivered with warmth and a number, the email version, responses to "that's the best we can do" and "what number would get you to yes," the scenario tree for competing offers — and the walk-away line the reader decides on in advance, when they are calm.
At the advanced tier, the strongest version of this week is the complete negotiation playbook — decoded offer, evidence-backed range, scenario tree with scripted branches, and competing-offer orchestration handled honestly (real timelines, no bluffing) — built so the reader can run the conversation from a page instead of from adrenaline.
The constraint that defines this week. AI models cannot see current market compensation, and a negotiation anchored on an invented number is worse than no research at all. No prompt may ask the AI to state what a role, level, or metro currently pays as fact, to value a specific company's equity, or to promise that a tactic will produce a result. The market case is built from sources the reader pulls — pay-transparency postings, published salary data, people in the role. Equity in a private company gets a framework for the questions to ask, never an AI-asserted valuation. And where an offer raises legal questions — non-competes, clawbacks, contract terms — the prompts point to a lawyer, not to the AI's reading. Scripts are coaching, not guarantees, and none of this is financial advice: the plan organizes the reader's decision; the decision stays theirs.
Design the prompts so the AI does what it is genuinely good at: decoding dense documents, structuring an evidence file, and drafting language the reader can rehearse and make their own. The reader supplies the offer, the market evidence, and the walk-away point; the AI supplies the decode, the structure, and the words to start from. "Word-for-word" means a complete starting draft, not a finished performance — and this is the week that rule matters most: the final pass belongs to the reader. A counter delivered in someone else's voice collapses at the first follow-up question, and the negotiation email lands on the desk of the exact hiring humans this series' contract is about. Every scripting prompt should end by handing the language back for the reader's own rewrite — rehearsed until it is genuinely theirs — and the post should say why.
Series dependency chain, for the Metadata block: Week 7 consumes Week 1's compensation baseline (the floor), Week 2's deal-breakers (what cannot be traded away), Week 4's company dossier (the other side's position), and Week 6's interview signals (how much they want this reader). Week 7 produces the negotiation plan and its outcome — consumed by Week 8, where the final numbers meet the final decision.
Because readers may arrive at this post with an offer in hand and none of the earlier artifacts, the prompts should work from the offer letter and the reader's own sense of their floor, while making clear the negotiation is strongest when it spends six weeks of accumulated evidence.
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. A first-time negotiator with a single offer and sweaty palms, a reader weighing startup equity against a bigger base elsewhere, and one holding two offers on different timelines 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 — above all, no salary numbers. If a prompt or example needs a wage figure to work, restructure it so the reader's own researched number is the input.)
## 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: 7` 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 current compensation for a role or metro as fact, value a specific company's equity, promise a negotiation outcome, or interpret contract terms that belong with a lawyer. The market case is built from evidence the reader gathers; the walk-away number is the reader's own. Also confirm every negotiation script and email prompt ends with the reader rewriting and owning the final words (the series contract's final-pass rule) — a counter in someone else's voice collapses at the first follow-up.
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.
An offer is on the table, the adrenaline is high, and the instinct is to say yes before the employer changes their mind. But first-time negotiators routinely accept the opening number, anchoring every future raise, bonus, and career move to a premature baseline. This week, we use AI to decode the complex fine print of your offer, benchmark it against hard market evidence you have gathered, and script a negotiation playbook that keeps you calm and in control. We offer three prompts at three depths: a Beginner prompt to translate dense offer documents into clear totals, an Intermediate prompt to build a defensible market case from your own research, and an Advanced prompt to script a full scenario tree and counter-offer emails. Use AI like an analyst to structure your strategy, but remember the golden rule: the final words you speak or send must always be rewritten in your own voice.
The Offer Decoder
Translate dense offer documents into clear, comparable dollars.
When an employer hands you an offer letter, they present the numbers in the most flattering light possible. Base salary is clear, but sign-on bonuses are often tied to retention clauses, equity is gated by multi-year vesting schedules, and performance bonuses are targets, not guarantees. Trying to weigh these variables in your head while managing the emotional peak of the job hunt is a recipe for missed details. You need a fast, objective translation of the document sitting in front of you. This prompt turns your AI into a compensation analyst that maps exactly what you are being offered and identifies the financial gaps hidden in the fine print.
Why this matters now
Employers are increasingly using complex compensation packages—heavy on equity, conditional bonuses, and variable benefits—to offset flat base salaries. If you do not understand the mechanics of how and when that money actually hits your bank account, you cannot negotiate effectively. Using this prompt right now allows you to separate guaranteed cash from theoretical upside, giving you a clear, grounded picture of the deal before you ever formulate your counter-offer.
Act as a compensation analyst. I am pasting my complete job offer letter and benefits summary below. Your task is to decode this offer into a clear, one-page financial picture. Break down the total compensation into three categories: Guaranteed Cash (base salary), Conditional Cash (bonuses, stipends), and Equity/Benefits (RSUs, options, health, retirement match). For conditional cash, explain exactly what must happen for me to receive it. For equity, explain the vesting schedule in plain English. Finally, identify three to five critical questions this document leaves unanswered—such as clawback clauses, bonus payout history, or non-compete terms—that I need to ask the employer. Do not invent valuations for equity, do not state what this role 'should' pay, and do not provide legal advice. Stick strictly to decoding the text I provide. Here is the offer text: \[PASTE OFFER AND BENEFITS HERE\]
How the AI reads this prompt
Practical examples from different industries
The Startup Equity Weigh-In
A candidate receives an offer from a Series B tech startup. The base salary is slightly lower than their current role, but the offer includes a large number of stock options. By pasting the offer text, the AI separates the guaranteed lower cash from the highly conditional equity, highlighting the four-year vesting schedule and the one-year cliff, and prompting the candidate to ask about the strike price and post-termination exercise windows. The Retail Management Package A retail manager is offered a district role with a base salary and a complex quarterly performance bonus tied to store metrics. The prompt strips away the corporate optimism, separating the guaranteed base from the conditional bonus. The AI points out that the offer does not specify whether the bonus is capped or what historical payouts have looked like, giving the manager exact questions to ask HR. The Agency Freelance Retainer A freelance designer is offered a six-month retainer contract by an ad agency. They paste the contract terms. The AI breaks down the guaranteed monthly rate versus conditional milestone payments, and highlights that the contract lacks a clear kill-fee clause and assumes unlimited revisions, prompting the freelancer to negotiate boundaries before signing.
Creative use case ideas
> 1. Decoding a Residential Lease Agreement: Paste a complex apartment lease to separate guaranteed rent from conditional fees (pet rent, utilities, parking) and identify missing maintenance timelines. > 2. Evaluating a Publishing Contract: Authors can use this to separate the guaranteed advance from conditional royalties and identify confusing rights-reversion clauses. > 3. Analyzing a Home Contractor Bid: Homeowners can paste a renovation estimate to separate fixed materials costs from conditional labor overruns and identify missing warranty terms. > 4. Breaking Down a Gym Membership Contract: Separate the flat monthly fee from annual maintenance charges, cancellation penalties, and conditional class fees.
Adaptability tips
You can modify this prompt to focus on specific friction points. If you care deeply about remote work, add: "Pay special attention to any clauses regarding mandatory office attendance, relocation, or equipment stipends." If you are comparing it to your current job, you can add: "Compare this offer's benefits strictly against the following current benefits I receive: \[list benefits\]."
Pro tips
> 1. Sanitize your data: Before pasting the offer, replace your actual name, the company name, and any highly sensitive identifying markers with placeholders (e.g., "Company X"). The numbers matter; the names do not. > 2. Feed it the employee handbook: If the offer references a "standard benefits package," paste the text of the benefits PDF alongside the letter so the AI can evaluate the actual retirement match and premium costs.
Prerequisites
You must have the actual text of your offer letter, the accompanying benefits summary, and any equity or bonus plan documents the employer provided.
Required tools
Any standard generative AI text model (ChatGPT, Claude, Gemini). Free tiers are sufficient for text analysis of this length.
Frequently asked questions
Can the AI tell me if this is a "good" offer?
No. The AI cannot see your local market, your exact skill level, or your personal financial floor. It can only tell you the mechanics of how the offer operates. You decide if it is good based on the baseline you built in Week 1\. What if my offer was given verbally over the phone? Do not use this prompt yet. Ask the recruiter, "I am very excited about this, could you send over the written details and benefits summary so I can review everything?" Never negotiate a complex package based purely on a verbal conversation. Will the AI flag illegal clauses in my contract? No. While it might highlight unusual terms or missing information, AI is not a lawyer and cannot provide legal advice. If you suspect a non-compete or IP assignment clause is overly broad, you must consult a qualified employment attorney in your jurisdiction.
Recommended follow-up prompts
Use the Intermediate prompt below to compare this decoded offer against the market evidence you have collected. If the offer contains dense legal jargon regarding intellectual property, prompt the AI to "Explain the standard industry meaning of this IP clause in plain English, noting that I will consult a lawyer for legal advice."
Tags and categories
Tags:
offer evaluation, compensation, benefits, equity, job search Categories: Negotiation, Document Analysis
Citations
NOT APPLICABLE
The Market Case Builder
Organize your gathered salary evidence into a defensible market case.
You cannot negotiate effectively by simply saying "I want more money." Employers expect a business case. They want to know why you command a higher premium. The problem is that compiling pay-transparency data, salary surveys, and your own baseline requirements often results in a chaotic folder of bookmarks and sticky notes. You need to synthesize this external market data into a clean, logical argument. This prompt uses AI as your research assistant to organize the hard evidence you provide, helping you build a defensible target range and the talking points to back it up.
Why this matters now
With more states enacting pay-transparency laws, candidates have unprecedented access to real salary bands. However, interpreting this data requires leveling your specific experience against the broad ranges posted online. Right now, recruiters are highly trained at batting away unresearched counter-offers. By structuring your evidence clearly before the conversation, you shift the dynamic from a personal plea to an objective business discussion.
Act as a negotiation strategist. I am preparing to counter a job offer. I will provide you with the initial offer details, my absolute walk-away number, and a list of market evidence I have personally gathered (including pay-transparency job postings, salary survey data, and industry benchmarks). Your task is to organize this data into a 'Market Case Brief.' The brief must include: 1\. A defensible target compensation range based strictly on my provided evidence. 2\. Three objective business arguments justifying this range, using my evidence and my specific years of experience. 3\. A list of weak points in my argument that the employer might challenge. DO NOT invent salary data, do not pull external market rates from your training data, and do not make assumptions about my industry. Base the entire brief solely on the evidence I provide below. Here is my data: \[PASTE INITIAL OFFER, WALK-AWAY NUMBER, AND ALL GATHERED EVIDENCE\]
How the AI reads this prompt
Practical examples from different industries
The Mid-Level Developer Counter
A software engineer receives a base offer of $115k. They input their walk-away number ($120k) and paste three pay-transparency postings from competitor companies showing ranges of $125k-$145k for the exact same tech stack. The AI structures an argument focusing on the market premium for their specific API integration experience, and warns the engineer that the recruiter might point out the competitors are larger enterprise firms. The Healthcare Administrator Negotiation A clinic manager inputs an offer that is heavy on bonus but light on base pay. They provide data from a regional healthcare salary survey showing a higher median base. The AI builds a case arguing for shifting 10% of the theoretical bonus into the guaranteed base, using the survey data to prove that the current base offer trails the regional median for a clinic of their size. The Trades Foreperson Advancement A construction foreperson is offered a salaried role but will lose overtime pay. They input their past two years of W2s and local union pay scales. The AI builds a mathematical case showing that the flat salary results in a net pay cut, structuring an argument for a higher base to account for the lost overtime, based entirely on the user's provided tax documents.
Creative use case ideas
> 1. Buying a Used Car: Input the dealer's price, your walk-away number, and links to five identical cars for sale in your zip code to build a pricing argument before walking onto the lot. > 2. Negotiating a Commercial Lease: Input the landlord's rate alongside square-footage prices you gathered from three neighboring buildings to build a case for a lower per-square-foot rate. > 3. Selling Vintage Equipment: Input the buyer's lowball offer and paste recent completed auction prices for identical vintage audio gear to structure your counter-argument.
Adaptability tips
If your gathered evidence is weak or sparse, tell the AI: "My evidence is limited. Help me formulate questions to ask the recruiter that will force them to justify their number, rather than me justifying mine." You can also adapt this to negotiate benefits instead of base pay by typing: "Focus the arguments entirely on securing an extra week of PTO and a remote-work day, using the provided data."
Pro tips
> 1. Weight your evidence: When pasting your data, add notes like "\[High Priority: this company is a direct local competitor\]" or "\[Low Priority: this data is from a higher cost-of-living city\]." The AI will use these weights to build a more nuanced argument. > 2. Pre-mortem the negotiation: Ask the AI to simulate the harshest possible recruiter response to the brief it just generated, allowing you to pressure-test your arguments before sending an email.
Prerequisites
You must have completed your Week 1 compensation baseline, established your Week 2 deal-breakers, and gathered real, linkable market data (pay transparency postings, salary surveys, peer data) to feed the prompt.
Required tools
Any standard generative AI text model. A model with a larger context window (like Claude 3.5 Sonnet or Gemini 1.5 Pro) is helpful if you are pasting large volumes of survey data.
Frequently asked questions
Why can't I just ask the AI what the market rate is?
Because AI models do not have real-time access to accurate, localized compensation data. Their training data is historical, often years out of date, and blends wildly different geographies and company sizes. Anchoring your negotiation on an AI hallucination will instantly destroy your credibility with a hiring manager. What if the AI's target range is lower than I want? The AI is only reflecting the data you fed it. If the resulting range is too low, you either need to gather better, more relevant data that supports your desired number, or you need to re-evaluate if your expectations align with the current market reality. Should I send this brief directly to the employer? No. This brief is your private backstage playbook. You use the arguments generated here to inform your verbal conversation or to construct a brief, professional counter-offer email. Never forward an AI-generated strategy document to a recruiter.
Recommended follow-up prompts
Once the market case is built, move immediately to the Advanced prompt below to script exactly how you will communicate this case to the employer.
Tags and categories
Tags:
market research, compensation data, business case, strategy Categories: Negotiation, Research & Preparation
Citations
NOT APPLICABLE
The Complete Negotiation Playbook
Script your counter-offer, scenario tree, and emails word-for-word.
Having the data is only half the battle; delivering it is where negotiations actually succeed or fail. The moment the recruiter says, "That's the best we can do," adrenaline spikes, and carefully laid plans vanish. You need to know exactly what you are going to say, how you will handle a flat rejection, and precisely how to orchestrate a scenario where you hold a competing offer. This prompt turns your AI into an elite negotiation coach, building a complete conversational scenario tree and drafting the exact emails you need. But it comes with an absolute rule: the AI provides the structure and the starting language, but you must rewrite the final scripts in your own voice.
Why this matters now
Remote hiring has shifted many negotiations to email, where tone is easily misinterpreted, or to sudden Zoom calls where pauses feel agonizing. Navigating competing offers on different timelines requires logistical precision and emotional calm. By mapping the entire decision tree in advance—including the exact words to use when walking away—you remove anxiety from the process and respond like a seasoned professional.
Act as a negotiation coach. I am preparing to counter a job offer. Below, I will provide the current offer, my researched target range, my absolute walk-away number, and details of any competing offers I hold. I need you to build a 'Negotiation Playbook.' Include: 1\. A scenario tree mapping the three most likely responses from the employer to my counter, and my strategic next move for each. 2\. A drafted email formally countering the offer, incorporating my target number and briefly citing my research. 3\. A short, polite conversational script for how to decline the offer if they cannot meet my walk-away number. 4\. If I provided a competing offer, a script for how to disclose it honestly to accelerate their timeline without issuing an ultimatum. IMPORTANT INSTRUCTION: Print a bold warning at the top and bottom of your response stating 'REWRITE REQUIRED: You must rewrite these scripts in your own natural voice. An employer will recognize AI-generated text.' Do not guarantee any outcomes. Here is my context: \[PASTE OFFER, TARGET, WALK-AWAY, COMPETING OFFERS\]
How the AI reads this prompt
Practical examples from different industries
The Dual-Offer Tech Squeeze
A product manager holds an offer from Startup A (lower base, fast timeline) and is waiting on a final round with Enterprise B (higher base, slow timeline). They feed the prompt their target numbers and timelines. The AI drafts a script for Startup A asking for a 48-hour extension, and a separate script for Enterprise B honestly disclosing the pending offer to accelerate their final decision, ensuring the candidate does not have to bluff. The Non-Profit Walk-Away A grant writer is offered a role at a non-profit that is $10k below their hard walk-away number. They input the scenario. The AI drafts a highly respectful, warm decline script that preserves the relationship, expressing deep alignment with the mission but firm boundaries on compensation, leaving the door open for future consulting. The Manufacturing Counter A logistics director inputs an offer and their target number. The AI provides an email draft countering for a higher base. The scenario tree prepares the director for the likely response: "The band is capped." The AI's next-move branch provides a script to pivot the negotiation away from base salary and toward an additional week of PTO and a guaranteed six-month performance review.
Creative use case ideas
> 1. Negotiating a Freelance Scope Creep: Input a client's request for additional unpaid work and your hourly rate to generate a scenario tree for holding boundaries and an email offering to quote the new work. > 2. Canceling a Major Vendor Contract: Input the vendor's penalty terms and your walk-away budget to script a phone conversation negotiating a reduced exit fee. > 3. Pitching a Venue for a Community Event: Use the scenario tree to plan negotiations with a venue owner, mapping out trades (e.g., offering free marketing in exchange for waived rental fees).
Adaptability tips
If you know the specific personality of the hiring manager (e.g., highly analytical and direct, or highly relational and chatty), add to the prompt: "Adjust the tone of the scripts to appeal to a highly direct, no-nonsense executive." You can also instruct the AI to focus entirely on equity or PTO if your base salary is already secured.
Pro tips
> 1. Rehearse with the AI Voice: Once the scripts are generated and you have rewritten them in your own words, use the voice feature on the ChatGPT or Claude mobile app. Tell the AI, "Play the role of the recruiter and push back on my counter-offer so I can practice speaking my script." > 2. Draft the acceptance: Ask the AI to also draft the enthusiastic acceptance email you will send if they meet your number. Having this ready prevents you from overthinking the victory lap.
Prerequisites
You need your decoded offer (Variation 1), your structured market case and target number (Variation 2), your hard walk-away number (from Week 2), and the details of any competing offers.
Required tools
Any standard generative AI text model.
Frequently asked questions
Why must I rewrite the AI's scripts in my own voice?
Because a negotiation is a test of your judgment and communication skills. If you send an email written by AI—which is often characterized by words like "delve," "moreover," and unnatural phrasing—the hiring manager will spot it. If they call you to discuss it and you cannot match the tone of your own email, your credibility collapses. The AI is your ghost-analyst, not your ghostwriter. What if the employer calls me unexpectedly before I send the email? This is why the scenario tree exists. Keep your rewritten scripts and your walk-away number printed on a piece of paper on your desk. If they call unexpectedly, you don't have to think; you just look at the page and read the pivot script to buy time, such as, "I'm so glad you called. Let me pull up my notes and call you back in ten minutes." Is it safe to tell an employer about a competing offer? Yes, if done honestly and professionally. Employers understand the market is competitive. However, never bluff or invent a competing offer. If they call your bluff and say, "We can't match that, you should take the other offer," you are left with nothing.
Recommended follow-up prompts
Use the AI voice mode to roleplay the conversation. Prompt: "Act as the hiring manager. I am going to read you my counter-offer. I want you to respond defensively and tell me the budget is locked, so I can practice my pivot script."
Tags and categories
Tags:
scripts, counter-offer, scenario planning, email templates, competing offers Categories: Negotiation, Communication
Citations
NOT APPLICABLE
Which of the three should you use?
The Beginner prompt is your translator. It takes the confusing, jargon-heavy offer documents and converts them into a flat, readable financial reality. It does not argue; it just shows you exactly what is guaranteed and what is an illusion. Use this the moment you receive the written offer. The Intermediate prompt is your strategist. It takes the messy folder of market research you have collected and builds it into a structured, logical business case. It prevents you from negotiating based on feelings and anchors your counter-offer to hard evidence. Use this after you have decoded the offer and identified the gap between what they are paying and what the market demands. The Advanced prompt is your coach and sparring partner. It anticipates the employer's reactions, builds a scenario tree to prevent you from freezing, and provides the baseline scripts for your emails and calls. It handles the complex logistics of competing offers. Use this when your target number is locked and you are ready to execute the conversation—provided you rewrite the final words yourself.
TAGS:
