Skip to content

Workflow · Productivity

Build an AI Resume Optimizer

A tool that takes a resume and a job description, scores the match, and rewrites the resume tailored to the role.

Beginner1 hourStack: Claude or GPT-5, Next.js or Streamlit
ResumeCareerLLMProductivity
Edited by The AIKnowHub team · Editorial team

Problem

Every job application benefits from a tailored resume. Tailoring 20 resumes by hand is awful. Most candidates settle for one generic version and lose to candidates who customize.

Final output

A web app where users paste a resume + job description and get: a match score (0–100), per-requirement gap analysis, a rewritten resume with diff view, and 1-click copy of the new version. Optional PDF export.

Architecture

Two text inputs (resume, job description)
  → LLM #1: extract job requirements (must-have, nice-to-have, vocab)
  → LLM #2: score resume against requirements (0–10 per item)
  → LLM #3: rewrite resume to mirror vocabulary, ATS-friendly
  → UI: diff view + copy/export
  → (Optional) save versions per role

Step-by-step

  1. 01

    Build the input UI

    Two large text areas plus optional file upload for PDFs. Use Next.js, Streamlit, or any framework you like.

  2. 02

    Extract job requirements

    LLM call: from the JD, get must-have skills, nice-to-have skills, top outcomes, and key vocabulary. Return structured JSON.

  3. 03

    Score the match

    LLM call: compare resume against each requirement. Score 0–10 with justification. Return overall match score.

  4. 04

    Rewrite the resume

    LLM call: tailor the resume to the role. Mirror vocabulary. Do not invent experience. Keep it 1 page when rendered.

  5. 05

    Diff view + export

    Show original vs rewritten side-by-side. Add one-click copy of the new version. Optional: PDF export with template.

What you'll build

Paste a resume + a job description; get back: a match score, the gaps, and a rewritten resume optimized for the role (and for ATS).

Why it works

A surprising amount of "resume optimization" is mechanical:

  • Match the job's vocabulary (so ATS filters pass).
  • Reorder bullets so most-relevant accomplishments come first.
  • Tighten verbs (replace "was responsible for" with "led", "shipped", "reduced").
  • Quantify outcomes where the source allows.

A good LLM does all of this in one shot if you prompt it well.

Step-by-step

The prompts and structure above give you the full pipeline. Build it in Next.js, Streamlit, or a quick Python CLI — any frontend works.

UX details that matter

  • Save the original — never overwrite.
  • Show what was added vs reordered vs removed.
  • One-click copy the rewritten Markdown.
  • Export to PDF with a clean template (let users tweak fonts/colors).

Ethical line

Make it crystal clear: the tool does not fabricate experience. If the user's resume lacks a required skill, the output should say so, not invent it. Reviewers can smell lies and ATS doesn't reward them either.

Extending

  • Cover letter generator using the same inputs.
  • Interview question predictor (which questions will this resume likely surface?).
  • Batch mode: apply one resume to 20 jobs and rank them.

Prompt examples

Copy any of these, replace the placeholders, run.

Requirement extraction

Extract from this job description:
- "musts": 5 must-have skills
- "nice": 5 nice-to-have skills
- "outcomes": top 3 outcomes the hire is meant to drive
- "vocab": 10 key terms (titles, tools, methodologies) the resume should mirror

Return JSON only.

JD:
"""
{{job_description}}
"""

Match scoring

Given the candidate resume and the extracted requirements below, score each requirement 0–10 based on evidence in the resume.

For each item return:
{ "requirement": "...", "score": 0-10, "evidence": "quote or 'not present'", "justification": "one line" }

Also return:
- "overall_score": 0–100
- "biggest_gaps": [strings]
- "biggest_strengths": [strings]

Resume:
{{resume}}

Requirements:
{{requirements_json}}

Resume rewrite

Rewrite this resume to maximize fit for the role described by the requirements below.

Rules:
- Do NOT invent experience or skills the candidate doesn't have.
- Mirror the JD's vocabulary where the candidate's existing experience supports it.
- Lead each bullet with an action verb + outcome + (where possible) a metric.
- Keep it 1 page when rendered. Cut weaker bullets first.
- Output clean Markdown.

If there's a real gap the resume can't fill, do NOT paper over it. Leave the original wording.

Resume:
{{resume}}

Requirements:
{{requirements_json}}

Cost estimate

Line itemApprox. cost
Requirement extraction (Haiku/Mini)$0.005
Match scoring (Sonnet/GPT)$0.03
Rewrite (Sonnet/GPT)$0.05
Total per run (approx.)~$0.08

Costs depend on model choice, content length, and how aggressively you cache.

Optimizations

  • Use a cheap model (Haiku, GPT mini) for extraction — quality difference is small for structured extraction.
  • Cache requirement extraction per JD — many users paste the same JD against multiple resumes.
  • Add a 'compare against my last 3 resumes' feature — picks the best base resume per role.
  • Use prompt caching for the system prompt across all 3 calls.

Common mistakes

  • Letting the LLM invent skills — explicitly forbid fabrication in the prompt.
  • Outputting a 2-page resume when the user wanted 1 — set a hard length cap in the prompt.
  • Skipping the diff view — users don't trust opaque rewrites; show them what changed.
  • Optimizing only for keyword matching — ATS filters care about keywords, but humans read the resume too.
  • Forgetting to save the original — never overwrite the input.

Frequently asked questions

Tailoring a resume to a role is normal and expected. The line is fabricating experience. The tool's prompt explicitly forbids invention — that's the honest version.

Related on AIKnowHub