Operations

Extract Resume Information with AI

Candidate name, current role, years, skills, education — résumés into consistent screening records, with inference kept on a short leash.

Open in Extraction Prompt Generator

Overview

Résumés bury the same facts in a thousand layouts. Screening at scale needs the same fields out of every CV: candidate_name, current_role, years_experience, skills as a deduplicated list, education, location. The catch is inference — "is the top role the current one?" — so this setup runs conservative: the model may take one short step from the text (the role dated "2024–present" is current) but never a chain of assumptions. Missing values come back empty rather than null, keeping the record human-readable in a screening sheet.

How to use this resource

Generate and feed one résumé per call

One CV per extraction keeps the example shape honest and the fields unambiguous.
Check the reading guidance

"Most-recent-first is common but not guaranteed — check the dates." That line saves the current_role field.
Keep conservative inference

Years of experience may be summed from dates (one short step); seniority may not be invented from job titles.

Why This Works

Fixed fields make a thousand layouts comparable in one sheet
The skills list rule (one entry per item, deduplicated) ends comma-blob prose
Conservative policy permits the date-math inference screening needs while blocking speculation

Best for

High-volume screening where layout chaos kills comparisons
Recruiters who need records, not summaries
CVs where the current role isn't obvious from position alone

Not for

Scoring or ranking candidates — that's judgment, not extraction
Deciding fit against a job description — pair with a Role Prompt for evaluation

Use cases

Building a screening sheet where every candidate has the same columns
Getting skills as a clean, deduplicated list instead of prose
Computing years of experience as a digit, not "over a decade"

FAQ

How does this resume prompt pick the current role when the newest job isn't listed first?

The reading guidance line — "Most-recent-first ordering is common but not guaranteed — check the dates" — tells the model to date-check before setting current_role, rather than trusting position on the page. So a role dated "2024–present" wins over one printed higher up. The AMBIGUITY POLICY caps this at one short step from the text, never a chain of assumptions.

Why do missing fields come back as empty strings instead of null in the JSON output?

The MISSING DATA rule deliberately returns an empty string "" for any field not found, never a guessed value, so the record stays human-readable in a screening sheet. That keeps every candidate row with the same columns — candidate_name, current_role, years_experience, skills, education, location — even when a CV omits location or education. Null is never emitted.

Can I make this prompt also rank or score candidates against a job description?

Scoring and fit judgment are out of scope — notFor names it directly, and the EXTRACTION RULES say "Do not summarize or interpret the document." The extraction-prompt-generator produces the extraction prompt you run in your own assistant; for evaluation, pair the resulting records with a Role Prompt. NewPrompt does not run the extraction or judge fit — the recruiter owns that call.

Customize This Resource

Opens this setup in Extraction Prompt Generator. Generate to get the full extraction prompt — then adjust the fields, missing-data behavior, and ambiguity policy.

Open in Extraction Prompt Generator

Prompt Template

Copy it as-is, or use Open in Extraction Prompt Generator to load it pre-filled and customize it with your own context.

TASK
Extract a candidate snapshot from the résumé in the input for our screening pipeline.

SOURCE
The input is a résumé / CV.
Reading guidance: Most-recent-first ordering is common but not guaranteed — check the dates before deciding what is current.

FIELDS TO EXTRACT
- candidate_name (required): Candidate's full name
- current_role (required): Most recent job title
- years_experience (optional): Total years of professional experience
- skills (required): Technical and domain skills mentioned
- education (optional): Highest or most recent degree
- location (optional): City or stated work location

EXTRACTION RULES
- candidate_name: The person's full name as written — do not expand initials or invent missing parts.
- current_role: Exactly as written in the source.
- years_experience: Extract as a number of years — a digit, not words.
- skills: Extract every distinct item as a separate list entry; deduplicate near-identical ones.
- location: The complete value as written, as a single string.
- Extract values from the source text only — do not use outside knowledge to fill fields.
- Do not summarize or interpret the document — return only the fields listed above.

MISSING DATA
- If a field is not found in the source, include it with an empty string "".
- Never invent or guess a value for a missing field.

AMBIGUITY POLICY
- Prefer explicitly stated information; infer a value only when the text strongly implies it.
- Keep any inference minimal — one short step from the text, never a chain of assumptions.

OUTPUT FORMAT
Return the extracted data as a single valid JSON object using the field names below.

EXAMPLE OF A VALID EXTRACTION
{
  "candidate_name": "Jane Smith",
  "current_role": "Senior Data Analyst",
  "years_experience": 6,
  "skills": [
    "Python",
    "SQL"
  ],
  "education": "B.Sc. Computer Science",
  "location": "Berlin, Germany"
}

OUTPUT RULES
- Return only the JSON object — no text before or after it.
- Do not wrap the output in markdown code fences.
- Use the field names exactly as defined — no renaming, no extra fields.

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Tip: Save time by exploring related resources and tools that integrate with this resource.