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HR03·Candidate Shortlist

Screening & Candidate Shortlisting

Extract evidence from candidate materials, generate summary cards, and produce a criteria-linked shortlist with risk flags.

Before you start

What you’ll need

  • Finalized must-have criteria from the job description workflow
  • Access to candidate application materials in a shareable format
  • Candidate Resumes
  • Screening Notes

Who’s involved

  • RecruiterScreens candidates, verifies extracted evidence, and manages the shortlisting workflow.
  • Hiring ManagerValidates the final shortlist against role requirements and approves candidates for next steps.

Safe use

  • Data HandlingThis workflow processes candidate personally identifiable information (names, contact details, employment history). Do not paste candidate data into public or unapproved GenAI tools.
  • VerificationGenAI may fabricate qualifications or misattribute experience across candidates. Verify every evidence citation against the original application before sharing the shortlist.

Execution steps

Hybrid

Inputs

  • Confirm the must-have criteria are finalized and approved
  • Verify all candidate resumes are collected and legible
  • Check that screening notes cover initial recruiter observations

Data Handling: Do not include candidate contact details, salary expectations, or identification numbers in the prompt — use candidate identifiers only.

Prompt

Extract criterion-linked evidence from candidate materials

CONTEXT
You will be provided with the following source documents:
1. Must-Have Criteria
2. Candidate Resumes
3. Screening Notes

TASK
For each candidate, extract specific, verbatim quotes or concrete facts from their application that relate to each must-have criterion. Produce an Evidence Extraction Table mapping every candidate to every criterion.

OUTPUT FORMAT
Use a markdown table with the following columns:
- **Candidate** — candidate identifier
- **Criterion** — the must-have criterion being assessed
- **Evidence** — verbatim quote or specific fact from the application
- **Source** — which document the evidence comes from (resume, cover letter, screening notes)
- **Strength** — [Strong / Partial / No Evidence]

Include one row per candidate-criterion pair. If no evidence exists for a criterion, enter "No evidence found" in the Evidence column and "No Evidence" in the Strength column.

CONSTRAINTS
Do not infer or assume qualifications not explicitly stated in the source materials. Do not paraphrase — use verbatim quotes where possible. Do not include personally identifiable contact details in the output.

Outputs

  • Evidence Extraction Table

Verification: Verify that every evidence citation maps to an actual passage in the candidate’s application — GenAI may fabricate quotes.

Hybrid

Inputs

Prompt

Flag misattributed or fabricated evidence entries

CONTEXT
You will be provided with an Evidence Extraction Table and the original candidate resumes it was derived from.

TASK
Compare each evidence entry in the table against the original source document. Flag any entry where the quoted evidence cannot be found in the source, is materially paraphrased, or is attributed to the wrong candidate.

OUTPUT FORMAT
Return a markdown table with columns:
- **Candidate** — candidate identifier
- **Criterion** — the criterion in question
- **Status** — [Confirmed / Corrected / Removed]
- **Note** — explanation of any correction or removal

CONSTRAINTS
Do not add new evidence that was not in the original extraction. Only confirm, correct, or remove existing entries.

Outputs

  • Verified Evidence Table
  • Confirm every Strong-rated entry has a traceable verbatim quote
  • Verify no evidence is attributed to the wrong candidate
GenAI

Inputs

  • Verified Evidence Table

Prompt

Produce evidence-backed candidate summary cards

CONTEXT
You will be provided with a Verified Evidence Table that maps each candidate’s application evidence to the must-have criteria for the role.

TASK
For each candidate, generate a summary card that consolidates the evidence into a concise profile. Each card should state the candidate’s overall strength against the criteria, highlight the strongest evidence, and note any criteria with weak or missing evidence.

OUTPUT FORMAT
For each candidate, use this structure:

### [Candidate Identifier]
- **Overall Fit**: [Strong Fit / Moderate Fit / Weak Fit]
- **Strongest Evidence**: 2–3 bullet points citing the most compelling criterion-evidence pairs
- **Gaps or Weak Areas**: 1–2 bullet points noting criteria with Partial or No Evidence ratings
- **Screening Note**: One sentence summarizing the recruiter’s overall impression

EXAMPLE
### Candidate A
- **Overall Fit**: Strong Fit
- **Strongest Evidence**:
  - Technical Skills: "Led migration of three legacy systems to cloud infrastructure" (Resume)
  - Experience: "8 years in enterprise platform engineering" (Resume)
- **Gaps or Weak Areas**:
  - Core Competencies: No evidence of stakeholder management experience
- **Screening Note**: Strong technical profile with a gap in stakeholder-facing experience.

CONSTRAINTS
Do not introduce qualifications or evidence not present in the Verified Evidence Table. Do not rank or recommend candidates — summary cards are descriptive only.

Outputs

Verification: Verify that overall fit ratings are consistent with the actual evidence counts — GenAI may over-rate candidates with sparse evidence.

GenAI

Inputs

  • Verified Evidence Table

Prompt

Consolidate screening risks and evidence gaps

CONTEXT
You will be provided with a Verified Evidence Table showing each candidate’s evidence against the must-have criteria.

TASK
Produce a Risk & Gaps Log that identifies every instance where a candidate has Partial or No Evidence against a must-have criterion. Group findings by criterion so reviewers can see which requirements are hardest to fill across the candidate pool.

OUTPUT FORMAT
Use a markdown structure with two sections:

**By Criterion**
For each criterion with gaps, list affected candidates:
- **[Criterion Name]**: [Candidate A — No Evidence], [Candidate B — Partial]

**By Candidate**
For each candidate with gaps, list the criteria:
- **[Candidate Identifier]**: [Criterion 1 — Partial], [Criterion 2 — No Evidence]

CONSTRAINTS
Do not include candidates or criteria with Strong evidence ratings — this log covers risks only. Do not recommend actions or mitigations.
GenAI

Inputs

  • Verified Evidence Table
  • Candidate Summary Cards
  • Risk & Gaps Log

Prompt

Rank candidates into evidence-based shortlist buckets

CONTEXT
You will be provided with a Verified Evidence Table, Candidate Summary Cards, and a Risk & Gaps Log for a set of screened candidates.

TASK
Assign each candidate to one of three shortlist buckets — Advance, Hold, or Decline — based solely on the strength of evidence against the must-have criteria. Produce a Draft Shortlist Table with a brief rationale for each placement.

OUTPUT FORMAT
Use a markdown table with columns:
- **Candidate** — candidate identifier
- **Bucket** — [Advance / Hold / Decline]
- **Strong Criteria Count** — number of criteria rated Strong
- **Gap Count** — number of criteria rated Partial or No Evidence
- **Rationale** — one sentence explaining the bucket placement, citing the key evidence or gap

Sort the table: Advance candidates first, then Hold, then Decline.

CONSTRAINTS
Do not use subjective impressions — every bucket placement must trace to evidence in the Verified Evidence Table. Do not introduce information beyond the provided inputs.

Outputs

  • Draft Shortlist Table

Verification: Verify that bucket placements are consistent with the evidence counts — GenAI may assign Advance to candidates with significant gaps.

Hybrid

Inputs

  • Draft Shortlist Table
  • Candidate Summary Cards
  • Risk & Gaps Log

Prompt

Incorporate reviewer feedback into final shortlist

CONTEXT
You will be provided with a Draft Shortlist Table and specific feedback from the hiring manager on bucket adjustments.

TASK
Update the shortlist table based on the feedback to produce the final Shortlist Table (Buckets/Ranked). Adjust bucket placements and rationales as directed.

OUTPUT FORMAT
Use a markdown table with the following columns:
- **Candidate** — candidate identifier
- **Bucket** — [Advance / Hold / Decline]
- **Strong Criteria Count** — number of criteria rated Strong
- **Gap Count** — number of criteria rated Partial or No Evidence
- **Rationale** — one sentence explaining the bucket placement, citing the key evidence or gap
- **Change** — [No Change / Moved from X to Y] indicating any adjustment from the draft

Sort the table: Advance candidates first, then Hold, then Decline. Mark any rows changed from the draft with the adjustment reason from the hiring manager feedback.

CONSTRAINTS
Do not change bucket placements unless explicitly instructed in the feedback. Do not remove candidates from the table — only reassign buckets.

Outputs

  • Confirm every candidate is assigned to exactly one bucket
  • Verify each bucket placement rationale cites specific evidence
  • Check that the risk and gaps log has been reviewed before finalizing

Reference

Guardrails

  • Evidence-Based Scoring OnlyEvery rating and bucket placement must cite specific evidence from the candidate’s application — no inferred or assumed qualifications.
  • Separate Extraction From DecisionComplete evidence extraction and verification before generating summary cards or shortlist rankings to reduce confirmation bias.
  • Consistent Criteria ApplicationApply the same must-have criteria to every candidate — do not adjust thresholds mid-screening.

Pitfalls

  • Pasting unredacted candidate contact details or salary expectations into the GenAI prompt.
  • Accepting AI-extracted evidence without verifying quotes against the original application documents.
  • Allowing the AI to infer qualifications not explicitly stated in the candidate materials.
  • Skipping the risk and gaps log review before sharing the shortlist with the hiring manager.

Definition of Done

  • Every candidate summary card cites specific verbatim evidence from the source application.
  • The shortlist table assigns every screened candidate to exactly one bucket with an evidence-backed rationale.
  • The risk and gaps log identifies every Partial or No Evidence rating across all candidates and criteria.

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AGASI AiOS · HR03 v1.0 · Apr 7, 2026