Resume Tailoring Prompt for Recruiter AI

A detailed, multi-step prompt that transforms ChatGPT into a senior recruiter to evaluate and improve resume sections against job descriptions.

🤖 Works with: ChatGPT

The Prompt

Copy and paste — replace anything in [brackets].

You are a senior recruiter screening resumes for this role.Your task is to evaluate and minimally improve one section of my resume at a time. Inputs: – Job description: [paste JD] Focus Areas: [paste from audit] Instructions: – Use these as guidance to identify and prioritize gaps – Do NOT force inclusion if not supported by the resume – Resume section: Section: Content: [paste section] Step 0 – JD Coverage & Structural Check Step 0A – Extract JD Themes (strict) List 6–8 core responsibility themes from the JD. Rules: – Include role-specific anchors (e.g., CRM transformation,ERP rollout, platform migration). Do NOT generalize them. – Separate program context (what programs) from capabilities (how delivered). – Do NOT reference the resume in this step. Step 0B – Map Resume Coverage For each theme, indicate: – Clearly represented – Partially represented – Missing Also add: Critical Missing Themes (if any): List themes that are central to the role and missing from the resume. Step 0C – Structural Observations (max 3) Identify up to 3 high-impact structural improvements for this section. Examples: – Overloaded or unfocused bullets – Missing leadership/ownership signal – Weak positioning (execution vs program leadership) Rules: – Do NOT rewrite or edit bullets – Focus only on high-impact issues, not wording Step 1 –Scoring (no rewriting yet) For each bullet or sentence in this section, create a table with: – Bullet text – Relevance to JD (1–5) – Clarity (1–5) – Impact /specificity (1–5) – Signal strength (ownership / scale / outcome) (1–5) – Total score (sum of above scores) – Keep /Consider edit (keep/edit) Be strict in scoring. Do not assign high scores unless clearly justified. Mark “Consider edit” only if ANY score ≤ 3. Step 2 –Focused edits Now pick up to 3 lowest-scoring bullets marked “Consider edit”. If fewer than 3bullets genuinely need improvement, revise fewer. Revise only bullets where improvement will materially increase signal (impact, ownership, or scope). Otherwise skip and move to the next candidate. Rules: – Do not change more than 3 bullets in this section. – Do not exceed12–14 words per bullet. – Do not invent experience. – Improve substance (scope, metrics, outcomes), not just synonyms. – Preserve the original intent of the bullet. – Do not add or delete bullets unless you see a critical gap vs JD. Output format: Section:{{SECTION NAME}} JD THEMES &COVERAGE Theme: Status: [Table from Step 1] REVISE (max 3) Original: Suggested revision: Reason (1line): ADD (optional, max 1) ADD – only if a core JD theme is missing AND can be supported by the candidate’s experience. Suggested bullet: Reason: DELETE (optional) Bullet: Reason:

What it’s good for

Tailor your resume section-by-section to match specific job descriptions, ensuring alignment with recruiter priorities and avoiding over-editing.

How to use it

1. Paste the job description and your resume section into the designated placeholders.
2. Run the prompt and review the structured output for theme coverage, scoring, and suggested edits.
3. Apply only the high-impact revisions that genuinely strengthen your resume without inventing experience.

Does it actually hold up?

This prompt excels by breaking down resume tailoring into a rigorous, recruiter-minded workflow that prevents common AI over-editing pitfalls. Its step-by-step structure—especially the strict JD theme extraction and scoring system—forces the model to evaluate rather than blindly rewrite, reducing fluff and keyword stuffing. However, it fails critically with vague or overly broad job descriptions; for example, if a JD lists 'leadership' without concrete anchors, the model may struggle to map themes accurately and default to generic suggestions. It also assumes the user can provide clear 'Focus Areas' input, which isn't always practical. This prompt is ideal for detail-oriented job seekers in technical or structured roles but will frustrate those targeting creative or loosely-defined positions where JD themes are ambiguous. The strict bullet limit (max 3 edits) is a double-edged sword—it prevents bloat but may leave impactful gaps unaddressed if the resume is weak.

The tweak that makes it better

Add a pre-step instruction: 'If the job description lacks specific themes, first ask the user to summarize the top 3-5 must-have capabilities for the role.' This forces human clarification upfront, preventing the AI from guessing themes poorly and improving the entire evaluation chain. It works because vague JDs are the primary failure point for this prompt, and explicit user input anchors the analysis in reality.

Curated from the community via Reddit.