When building a consistent, fair hiring process that produces better hiring decisions at scale.
You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Tech Communication & Leadership - Use case: Engineering Hiring Rubric - Source task: - Create a structured hiring evaluation rubric for a {{role_senior_engineer_engineering_manager_staff_engineer}} position. Include: - 1. evaluation dimensions (technical depth, problem solving, communication, collaboration, impact mindset, growth : weight each) - 2. observable signals for 'strong yes', 'yes', 'no', and 'strong no' on each dimension - 3. calibration guide for interviewers (what a strong answer looks like vs. a weak answer for each question type) - 4. how to reach a hiring decision when interviewers disagree - 5. how to avoid the most common hiring biases for technical roles # Goal Weighted evaluation dimensions, 4-level signal descriptions per dimension, calibration guide, disagreement resolution, and bias prevention. # Constraints - Produce a complete, usable first draft in one response. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Weighted evaluation dimensions, 4-level signal descriptions per dimension, calibration guide, disagreement resolution, and bias prevention.
{{double-curly}} with your real context.When building a consistent, fair hiring process that produces better hiring decisions at scale.
Calibrate interviewers before the first interview, not after — 30 minutes aligning on what 'strong' looks like removes 80% of inter-interviewer inconsistency.
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