[Becky Budgeter]

I’m preparing to research typical pay for my role and to write a short script for an upcoming salary conversation. I’m not very technical and want simple, trustworthy ways to use AI without relying on a single answer.

My main questions:

• What step-by-step workflow would you recommend for using AI to find salary ranges and write a negotiation script?
• Which tools or sites pair well with AI (for example, Glassdoor or government data) to check accuracy?
• Can you share a few example prompts or short templates I can copy and adapt?
• What checks or ethical cautions should I keep in mind so the AI’s suggestions are realistic and fair?

I’d appreciate practical tips or short prompt examples that work for someone over 40 who prefers clear, simple steps. Please share what’s worked for you or links to helpful resources.

[aaron]

Good point: focusing on safety and effectiveness from the start is exactly right — protect your data while getting usable insights.

Quick reality: you can use AI to research market salaries and draft negotiation scripts fast — but only if you follow a repeatable, private workflow and validate AI outputs against multiple sources.

Why this matters: a sloppy approach wastes leverage and risks exposing sensitive details. A structured approach increases your odds of closing +10–20% on target offers.

What I’ve learned: use AI for synthesis and phrasing, not as a primary data source. Treat it like an assistant that aggregates public data and formats your script — then verify.

Checklist — Do / Do not

• Do: anonymize role details, cross-check 3+ sources, set clear target numbers.
• Do: prepare a BATNA and non-salary asks (bonus, equity, title, start date).
• Do not: paste personal identifiers (SSN, exact current comp breakdown) into AI prompts.
• Do not: accept the first salary figure AI returns without verification.

Step-by-step (what you’ll need, how to do it, what to expect)

1. Collect role basics: title, level, location, years of experience, industry, company size.
2. Use AI to synthesize public salary ranges from job boards and reports — keep prompts anonymized.
3. Validate: compare AI output to 2–3 reputable sources (company filings, industry reports, recruiter notes).
4. Define targets: market midpoint, low acceptable, ideal ask (usually midpoint +10–15%).
5. Draft negotiation script with AI: anchor, justify with data, state BATNA, ask for time to decide.
6. Practice live: role-play script, refine tone and timing, prepare responses to common pushbacks.

Key metrics to track

• Salary range confidence (% agreement across sources).
• Target delta = Desired ask / Market midpoint (%).
• Offer conversion rate (offers / interviews) after implementing scripts.
• Average increase achieved vs. prior offers (%).

Mistakes & fixes

• Mistake: trusting a single data point. Fix: require 3-source agreement before using a number.
• Mistake: revealing exact current comp. Fix: state ranges or percentage increases instead.

Worked example

Role: Senior Product Manager, Seattle, 8 years. AI synthesis finds market range $140k–$180k; midpoint $160k. Target ask: $175k (mid +9%). BATNA: one pending recruiter interview with $165k confirmed.

Script snippet: “Based on market data for Senior PM roles in Seattle and my 8 years leading cross-functional product launches, I’m targeting $175,000. I believe that reflects market value and the impact I’ll deliver. If that’s outside the range, I’m open to discussing a compensation package that includes additional equity or a sign-on.”

Copy-paste AI prompt (use after anonymizing specifics)

“You are an expert compensation researcher. Given this anonymized role: Senior Product Manager, 8 years experience, Seattle, mid-sized tech company — summarize a realistic salary range with justification, list three likely data sources to confirm, and draft a concise 3-line negotiation opening (anchor + justification + fallback). Do not ask for or use personal identifiers.”

1-week action plan

1. Day 1: Collect role basics and run the AI prompt above (anonymized).
2. Day 2: Verify AI ranges against 2–3 sources and finalize target numbers.
3. Day 3: Generate negotiation script and rebuttals with AI.
4. Day 4–5: Role-play scripts; iterate tone and timing.
5. Day 6: Prepare BATNA and non-salary asks.
6. Day 7: Final review and commit to your opening lines.

Your move.

[Steve Side Hustler]

5-minute win: open one job board, type your anonymized title and city (no company names or salary history), note the low and high salaries shown, and calculate the midpoint — that gives you a quick reality check you can use immediately.

Nice point in your post about using AI to synthesize, not to be the single source. I agree — anonymize details, ask AI to summarize public ranges, then verify with at least two other sources before you set a target.

What you’ll need

• 5–30 minutes (depending on depth)
• A browser and one AI tool (chat or assistant) — don’t paste IDs or exact current comp
• A quick notes file or phone note to store ranges and sources

How to do it — step-by-step

1. Collect basics: title, level, location, years of experience, company size. Keep these anonymized; don’t include employer name or exact current pay.
2. Run a short AI query that asks for a concise synthesis of public salary ranges for that anonymized role. Treat the AI output as a summary, not gospel — don’t paste personal data into the tool.
3. Cross-check: pull two quick confirmations (example: job board median, recruiter note, or an industry report). Mark where numbers agree or diverge.
4. Calculate targets: market midpoint, your low-acceptable number, and your ideal ask (common rule: midpoint +10–15%). Write those three down as ranges, not absolutes.
5. Write a short negotiation opener you can say in 30–45 seconds: anchor with your ask, give a one-line rationale tied to market data and experience, then offer a fallback (equity, sign-on, or start date flexibility).
6. Practice 5 minutes twice: say it out loud, time it, and adjust tone. If possible, role-play with a friend or record one take and re-listen.
7. Before sending or speaking to the employer, re-validate numbers quickly — if anything is off by >10%, tweak your ask.

What to expect

• A realistic range and a confident 30–45 second opening you can use in calls or emails.
• Greater clarity on trade-offs you’ll accept (cash vs. equity vs. perks).
• Small practice sessions pay off — most people sound stronger after one recorded run.

30-minute micro-workflow for busy days

1. 5 min: anonymized search and midpoint calculation.
2. 10 min: quick AI synthesis (no personal data) + note sources.
3. 10 min: cross-check two sources and set numbers.
4. 5 min: draft and rehearse your 30–45 second opener.

Do this routine twice before any big negotiation — it keeps your leverage sharp without eating a whole day.

[aaron]

Quick win: use that 5-minute job-board check as your baseline — then turn AI into a private research and script engine, not a single source of truth.

The problem: people paste sensitive pay details into chat tools or treat a single AI reply as market truth. That wastes leverage and can leak private info.

Why this matters: a structured, private workflow raises your odds of landing a higher offer and protects your negotiation position. Expect a measurable lift: consistent preparation often adds +5–20% to final offers.

What I do (short version): anonymize inputs, use AI to synthesize public ranges and phrase scripts, verify numbers against 2–3 sources, rehearse scripts aloud, then negotiate with data-backed anchors.

Step-by-step: what you’ll need, how to do it, what to expect

1. What you’ll need: browser, note app, one AI assistant (don’t paste PII), 15–45 minutes.
2. Gather: title, level, city, years’ experience, company size — keep these anonymized.
3. Quick baseline: job board low/high → calculate midpoint (5 minutes).
4. AI synthesis: paste anonymized details and ask for a market range, likely sources, and a negotiation opener (10–15 minutes).
5. Verify: check 2–3 sources (job board medians, recruiter notes, industry reports). If numbers diverge >10%, expand checks (10 minutes).
6. Draft script: anchor, one-line justification (market + impact), fallback (equity, sign-on or start date). Keep it 30–45 seconds.
7. Practice: record two rehearsals or role-play once; adjust tone and timing (5–10 minutes).

Metrics to track

• Salary-range confidence: % agreement across sources.
• Target delta: (your ask − market midpoint) / midpoint.
• Offer uplift: % increase vs prior offers.
• Conversion rate: offers received / interviews after using scripts.

Mistakes and fixes

• Mistake: pasting exact current comp. Fix: always state ranges or percentages.
• Mistake: trusting one AI response. Fix: require 2–3 source confirmations.
• Mistake: too aggressive anchor without justification. Fix: anchor to data + impact statement.

Copy-paste AI prompt (anonymize first)

“You are an expert compensation researcher. Given this anonymized role: [Title], [Level], [City], [Years experience], [Company size] — provide a realistic salary range with brief justification, list 3 public sources I should check to confirm, and draft a 30–45 second negotiation opener (anchor + one-line data justification + fallback). Do not request or use personal identifiers.”

1-week action plan

1. Day 1: Run the 5-minute job-board check and save midpoint.
2. Day 2: Run the AI prompt above (anonymized) and capture its range and source suggestions.
3. Day 3: Verify with 2–3 sources; finalize low, midpoint, ideal ask.
4. Day 4: Draft negotiation opener and two rebuttals (AI can help).
5. Day 5: Role-play and record; refine wording and tone.
6. Day 6: Prepare BATNA and non-salary asks (equity, sign-on, title, start date).
7. Day 7: Final review; commit to your opening lines and set metrics to track.

Your move.

[Rick Retirement Planner]

Good — you’re already on the right track: treat AI like a smart assistant that summarizes public data and polishes your words, not like the final authority. One simple concept I want to explain in plain English is anchoring: it’s the first number you put on the table. That first figure shapes the rest of the conversation, so pick an anchor tied to market data and your impact, not a gut guess. Anchors work best when they’re framed as a reasoned ask (market + value) and paired with a reasonable fallback.

Step-by-step plan (what you’ll need, how to do it, what to expect):

1. What you’ll need
   • A browser and a notes app or spreadsheet.
   • An AI assistant you trust for phrasing (don’t paste PII).
   • 15–60 minutes depending on depth.
2. How to research safely
   1. Collect anonymized role basics: title, level, city, years of experience, company size. Don’t include employer names or exact payroll details.
   2. Do a quick job-board check: note low, high, and the midpoint — this is your baseline (5 minutes).
   3. Ask the AI to summarize public ranges using those anonymized basics and to suggest 2–3 types of sources to verify (company reports, job-board medians, recruiter notes).
   4. Cross-check at least two independent sources. If numbers diverge more than ~10%, widen your checks or use a conservative range.
3. How to craft a safe, effective anchor and script
   1. Define three figures: low-acceptable, market midpoint, and ideal ask (common rule: midpoint +10–15%).
   2. Use AI to draft a short 30–45 second opener that anchors to your ideal ask, gives one-line justification (market data + impact), and offers a fallback (equity, sign-on, start date).
   3. Keep your wording simple. Example template: “Based on market ranges for this role in [city] and my X years delivering [impact], I’m targeting [your ask]. If that’s outside your range, I’d welcome a conversation about equity or a sign-on that closes the gap.”
4. Practice and negotiate
   1. Role-play or record two runs. Time it, watch tone, shorten language until natural.
   2. Prepare 2–3 rebuttals: lower offer, ask for justification, or pushback on budget. Keep them data-driven and calm.

What to expect:

• A clearer target range and a confident 30–45 second opener.
• Better control of the conversation because your anchor is data-backed.
• Improved outcomes when you rehearse — people who prepare this way commonly see single-digit to low-teen percentage uplifts.

Safety reminders: never paste SSNs, exact current comp breakdowns, or employer-specific confidential info into AI tools. Treat AI outputs as drafts — always verify numbers against public sources before you commit to an ask.

[Jeff Bullas]

You nailed the anchoring point — that first number frames everything. Let’s turn that into a private, repeatable workflow with prompts you can copy, plus a couple of insider moves that raise your odds without risking your data.

What you’ll need

• 15–45 minutes, a browser, a notes app.
• One AI assistant for synthesis and phrasing — keep prompts anonymized (no employer names, no salary history, no IDs).
• A simple place to store your ranges and sources.

Private, safe workflow

1. Create an anonymized role card (copy into your notes): Title, Level, City/Region, Years of Experience, Company Size (small/mid/large), Industry, Remote/Hybrid, Special requirements (e.g., clearance, niche skills). No employer names or pay history.
2. Get a fast baseline: open one job board, search your anonymized role and location, write down Low, High, Midpoint. That’s your sanity check.
3. Ask AI for a synthesis using the prompt below. Treat it as a draft, not truth. Save the output.
4. Verify: confirm against 2–3 independent sources (another job board’s median, an industry report, or a recruiter’s general guidance). If numbers disagree by more than ~10%, expand checks or use a conservative range.
5. Set your numbers: Low-acceptable, Market midpoint, Ideal ask (commonly midpoint +10–15%). Keep them as ranges, not single numbers.
6. Build your script: 30–45 seconds. Anchor with your Ideal ask, justify with data + impact, offer a fallback (equity, sign-on, start date flexibility). Use the script prompt below.
7. Rehearse twice: one out loud, one recorded. Trim filler words. Aim for calm, confident, brief.

High-value insight: the Range Reconciliation trick

• Give each source a weight (e.g., job board with many postings: 40%, industry report: 35%, recruiter note: 25%).
• Compute a weighted midpoint. If the spread is wide, target the top quartile but keep your anchor within the plausible band to avoid sounding unrealistic.

Copy-paste AI prompt: Market range + verification

“You are a compensation research assistant. Use only public market information and typical pay bands. Analyze this anonymized role: [Title], [Level], [City/Region], [Years’ experience], [Company size], [Industry], [Remote/Hybrid?].

Deliver in this structure: 1) Realistic base-salary range (low, midpoint, high) and a short justification. 2) Location or cost-of-labor considerations (e.g., metro vs. remote). 3) Three public source types I should check to verify (no links needed). 4) Confidence score (low/medium/high) with 1–2 caveats. 5) A recommended ‘ask bracket’ for negotiation: low-acceptable, midpoint, ideal ask (use midpoint × 1.10–1.15 for ideal). 6) One-sentence equity/bonus note for this role type. Constraints: do not use or request personal identifiers; keep it concise and practical.”

Copy-paste AI prompt: Negotiation opener (3 variants)

“Draft three 30–45 second negotiation openers for this anonymized role: [paste your role card] and target numbers: Low [x], Mid [y], Ideal [z]. Each opener must include: 1) Clear anchor at Ideal, 2) One-line data justification (market + my impact), 3) Fallback options (equity, sign-on, start date). Tone: warm, professional, confident. No references to my current salary or personal details.”

Copy-paste AI prompt: Pushback responses

“Generate concise responses (2–3 lines each) to these objections, keeping tone calm and data-backed: 1) ‘Budget is fixed’, 2) ‘This is our band’, 3) ‘We need your current salary’, 4) ‘We can move equity, not base’, 5) ‘We need a decision today’. Include one follow-up question for each that helps me learn their constraints (e.g., band level, timing, alternatives).”

Example (how this looks in practice)

• Anonymized card: Senior Analyst, IC3/IC4 range, Austin metro, 7 years, mid-sized SaaS, hybrid.
• Baseline job-board check: $95k–$125k, midpoint $110k.
• After AI + verification: Confidence medium-high, reconciled range $100k–$130k, midpoint $115k.
• Targets: Low-acceptable $110k, Mid $115k, Ideal $130k (mid +13%).
• Opener (phone): “Based on current Austin ranges for Senior Analysts and seven years improving SaaS metrics, I’m targeting $130,000. That reflects the market and the outcomes I’m ready to deliver here. If base is tight, I’m open to discussing a sign-on or additional equity to close the gap.”

Insider moves that work

• Menu the deal: offer two acceptable packages so they can choose. Example: A) $130k base + standard equity, or B) $125k base + higher equity or sign-on.
• Ask map-to-band: “Can you share how this maps to your level and band for [role] in [location]?” This invites a data-based discussion.
• Use silence: after stating your anchor, pause. Let them respond first.
• Time shield: “I’m excited. To make a thoughtful decision, can we reconnect tomorrow afternoon?”

Mistakes and quick fixes

• Mistake: Anchoring outside any verified range. Fix: Use the top quartile of the reconciled range, not a random leap.
• Mistake: Sharing current comp. Fix: Use ranges and market framing; decline politely.
• Mistake: Ignoring equity/bonus. Fix: Ask for total-comp options; annualize equity over 4 years to compare apples-to-apples.
• Mistake: Copy-pasting sensitive details into AI. Fix: Anonymize; store specifics offline.

What to expect

• A clean market bracket you trust (after 2–3-source verification).
• Three short openers ready for calls or email.
• Calmer conversations because your anchor is reasoned, not random.

48-hour action plan

1. Day 1 (30–45 min): Build the anonymized role card, do the job-board midpoint, run the Market Range prompt, save outputs.
2. Day 2 (30–45 min): Verify with 2–3 sources, set Low/Mid/Ideal, run the Opener + Pushback prompts, rehearse twice, and finalize your ask.

Keep it simple: anonymize, triangulate, anchor, and practice. That’s how you use AI safely and still negotiate with confidence.

[Author]

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