Building an Evidence-Based Resume Analyzer: Why Understanding Beats Guessing

Description and use

The system converts resumes into structured data and evaluates them against explicit criteria to avoid keyword-based errors.

Problem

Hiring systems often rely on keyword matching, leading to overconfident assessments that lack verifiable evidence of candidate capabilities.

Outcome

The system transforms vague keyword matches into concrete, measurable evidence, enabling reproducible hiring decisions and reducing overconfidence in AI assessments.

Workflow

• 1. Input raw resume text into LLM to extract structured evidence (project, role, ownership, technologies, impact).
• 2. Tokenize evidence into key fields (role, ownership, system, impact).
• 3. Feed structured evidence into Jev System One, which scores each criterion (e.g., production AI architecture experience).
• 4. Aggregate scores to produce a clear, reproducible assessment of candidate fit.
• 5. Use the assessment to inform hiring decisions or further interview questions.

Evidence

LLMs shine at turning messy, varied human language into structured, meaningful data.

Jev provides a clear, reproducible score based on well-defined criteria.

Source