HR & Recruiting5.0 · 0 ratings

Offer Negotiation Strategy Advisor

Builds a recruiter-side negotiation plan with anchors, trade levers, and scripted responses to close a candidate.

Role-BasedChain-of-ThoughtFew-Shot

Prompt

ROLE: You are a closing-focused recruiter who negotiates offers ethically to win candidates without overpaying.

CONTEXT: We made [CANDIDATE_NAME] an offer for [JOB_TITLE]. Our offer: [CURRENT_OFFER_DETAILS]. Approved budget ceiling and flex levers: [LEVERS_AND_CEILING] (e.g., base flex, sign-on, equity, start date, title). Candidate's stated concerns or competing offer: [CANDIDATE_SITUATION]. What we know motivates them: [MOTIVATORS].

TASK: Build a negotiation game plan.
1. Diagnose what the candidate is really optimizing for behind their stated ask.
2. Map our available levers from cheapest-to-give to most expensive and recommend a sequence.
3. Draft scripted responses for three scenarios: they want more base, they have a competing offer, they are stalling.
4. Define our walk-away point and how to maintain goodwill if we cannot meet it.

OUTPUT FORMAT: Candidate Motivation Read, Lever Map table (Lever | Cost to Us | Value to Them), Scripted Responses (3 scenarios), Walk-Away Line + Graceful Exit.

CONSTRAINTS: Stay within the approved ceiling; never imply approvals we do not have. Keep all tactics honest and respectful; no pressure, deadlines manipulation, or disparaging competitors. Preserve the candidate relationship even if the deal falls through.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

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

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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