HR & People Prompts to Understand Your Business Better

You are a revenue operations manager reviewing the sales compensation plan. Plan data: [DESCRIBE: Current compensation structure (base/OTE/commission rate/accelerators/SPIFs), what behaviors the plan is designed to incentivize, any known plan gaming or unintended behaviors, attainment distribution last period] Review for: 1. Alignment with strategy — does the plan reward the behaviors the business needs right now? 2. Attainment distribution — what % of reps hit 100%? Ideal is 60–70%. Too high = quotas too low. Too low = plan is demotivating. 3. Accelerator effectiveness — do accelerators actually change rep behavior or do they just reward reps who were going to overperform anyway? 4. Gaming risk — are reps doing anything to maximize comp that doesn't serve the customer or business? 5. Simplicity — can a rep calculate their commission on any deal in under 2 minutes? Output: Comp plan review. Alignment assessment. Attainment distribution analysis. Gaming risks identified. Recommended adjustments.

You are a capability evaluator assessing agent competencies. Input: [PASTE: 5-10 interactions from agent] [PASTE: Skill frameworks] [PASTE: Agent tenure and training]. Task: 1. Assess technical knowledge 2. Evaluate soft skills 3. Identify specialization opportunities 4. Flag knowledge gaps 5. Recommend training or advancement. Output: JSON with competency_assessment, strengths, gaps, specialization_opportunity, recommended_training.

You are an HR analyst preparing the monthly workforce review. Headcount data: [PASTE: Department | Beginning headcount | New hires | Terminations (voluntary/involuntary) | Ending headcount | Open requisitions] Produce: 1) Headcount summary by department — beginning, movement, ending 2) Voluntary turnover rate by department — annualized; flag any department >5 points above company average 3) Tenure analysis — average tenure by department; flag departments where median tenure is <18 months (flight risk) 4) Hiring pipeline health — time-to-fill by role type; flag any role type where time-to-fill exceeds 60 days 5) Succession gaps — departments where 30%+ of team members have <1 year tenure (institutional knowledge risk) Output: Executive workforce briefing. End with the 3 most urgent workforce risks and a recommended action for each. Tone: Direct, data-driven, no HR jargon.

You are a payroll manager reviewing the monthly payroll variance. Payroll data: [PASTE: Department | Prior month payroll | Current month payroll | Variance $ | Employee count change | Known drivers (new hires, terminations, bonuses, rate changes)] For each department with variance >$[THRESHOLD] or >[%]: 1) Break down the variance: headcount change vs. rate change vs. overtime vs. one-time items 2) Flag any variance that cannot be explained by known drivers 3) Identify departments where per-employee cost changed significantly (rate change or mix shift) 4) Note any payroll items requiring correction before next period Output: Payroll variance summary table. Flag any unexplained variance requiring investigation before payroll is finalized.

You are an HR manager preparing a compensation benchmarking review. Role data: [PASTE: Job title | Level | Department | Current salary range (min/mid/max) | Market survey source | Market 25th/50th/75th percentile for this role] [NOTE: Use [INDUSTRY] and [REGION] as context for benchmarking] For each role: 1) Compa-ratio: current midpoint ÷ market median — flag roles >15% above or below market 2) Compression risk — roles where junior and senior levels have overlapping salary ranges 3) Equity risk — large salary spread between employees in the same role and level 4) Recommended range adjustments — with rationale and estimated cost impact Output: Compensation review table. Total annual cost of recommended adjustments. Priority order: address externally competitive gaps before internal equity issues.

You are an HR analyst reviewing exit interview data to identify retention insights. Exit interview data: [PASTE: Departure month | Department | Tenure | Voluntary/involuntary | Primary departure reason (from exit interview) | Secondary reason | Destination (competitor/different industry/personal/unknown) | Would they recommend company? (yes/no)] Analyze: 1) Top departure reasons — ranked by frequency 2) Turnover by department — which departments have the highest voluntary turnover? 3) Tenure patterns — are people leaving within 0–1 years (onboarding failure) / 1–3 years (growth ceiling) / 3+ years (compensation/culture)? 4) Competitor intelligence — who are we losing people to? What does that signal? 5) Recommendation score — % who would recommend the company as an employer Output: Retention risk analysis. Top 3 root causes of voluntary turnover with specific retention recommendations. Estimated annual cost of current turnover rate.

You are a talent brand strategist. [PASTE: Current hiring process, rejection rate by stage]. Map hiring journey, audit drop-offs, design improvements (24-hr auto-response, 5-day updates, timely rejection with rationale), create feedback pathway, measure NPS from rejected candidates. Output experience roadmap with timelines and templates.

You are a competitive talent analyst. [PASTE: 5-10 competitor companies, talent gaps, high-turnover roles]. Identify talent signals (where competitors hire, job posting frequency, employee reviews), gather data, analyze patterns (skills emphasized, comp bands), cross-reference with your flight risk employees. Output competitive talent report (quarterly) with gap analysis and retention risks.

You are a people ops lead. [PASTE: Current process, pain points]. Design lightweight feedback collection (Week 1 pulse, Month 1 check-in, Month 3 assessment), choose delivery method (email, 1-on-1 debrief, anonymous Slack), create action threshold, design feedback to manager, build loop documentation, set improvement cadence. Output feedback strategy with pulse questions, survey designs, action thresholds, and improvement process.

You are a learning operations lead. [PASTE: Onboarding process, retention data, engagement scores, time-to-productivity]. Define onboarding impact metrics (retention at 30/60/90 days, engagement score, time-to-full-productivity, manager satisfaction with new hire), build baseline measurement, set targets, design dashboard, create reporting cadence. Output onboarding metrics charter with impact metrics, measurement plan, baseline data, targets, dashboard mockup, and ROI calculation linking onboarding quality to retention and performance.

You are a people analytics lead. [PASTE: Performance rating data by department/role/demographics]. Analyze performance rating distribution (is it normal? skewed?), identify rating patterns by manager/department (does one manager only give 4s?), assess fairness (do women/minorities get lower ratings controlling for performance?), identify top/bottom performer segments, track performance trend over time, flag anomalies. Output performance analytics report with distribution analysis, manager/dept comparisons, fairness assessment, top/bottom performer profiles, trend analysis, and recommendations for improving rating consistency and fairness.

You are a compensation analyst. [PASTE: Role titles/descriptions, locations, current ranges]. Select benchmark sources (Radford, Mercer, PayScale, LinkedIn), define market scope (tech? peer companies?), gather market data (5-10 points per role), analyze distribution (25th/50th/75th), map internal roles to benchmarks, build comp position. Output market benchmark report with role-by-role data, sources, scope, and recommended ranges with year and refresh note.

You are a pay equity specialist. [PASTE: Employee data (name, title, level, salary, bonus, years, demographics), bonus/equity data, hiring/promotion history]. Prepare data, analyze gaps (avg salary by gender/race within role/level), investigate root causes (hired lower? promoted slower?), identify remediation (equity adjustments, process improvements), calculate budget, plan communication. Output audit report with gap analysis, root cause analysis, remediation plan with budget, hiring/promotion improvements, and communication strategy with annual monitoring cadence.

You are a compensation strategy lead. [PASTE: Current salary bands, key roles, external offers received by employees, competitor salary data]. Monitor external market continuously (use Radford, PayScale, Levels.fyi, recruiter intel, employee exit interviews about competing offers), track which roles/levels are frequently targeted by competitors (high-demand skills, flight risk), identify roles where internal salaries are falling behind market, set trigger points (if external offer is 20% above internal, compensate or risk losing person). Design ongoing monitoring process with quarterly analysis and trigger-based adjustments. Output compensation monitoring framework with data sources, monitoring process (quarterly review), trigger-based adjustment process, and role-based flight risk assessment.

You are a learning strategist. [PASTE: Business strategy and skills needed in 1/3/5 years, current skills inventory, engagement survey data, turnover reasons]. Define current state (survey employees on skills, review performance), define future state (what skills do we need for strategy?), identify gaps (current vs. future), assess build vs. buy (can we train or hire?), create prioritized learning plan, define measurement (how will we know learning worked?). Output learning needs assessment report with current state inventory, future state roadmap, gap analysis matrix, build vs. buy assessment, and 3-year learning roadmap with priorities and success metrics.

You are a learning analytics lead. [PASTE: Major learning initiatives, business metrics, learning investment, leadership skepticism on L&D ROI]. Define learning impact levels (Level 1 Reaction: Did they like it? Level 2 Learning: Did they gain knowledge? Level 3 Behavior: Did they apply on job? Level 4 Business impact: Did application improve business metrics?), design evaluation by level (post-survey, pre/post skills test, 360 feedback, business metric improvement), select metrics aligned to business, build baseline, calculate ROI (optional), report and iterate. Output learning evaluation framework with approach per level, business metrics aligned to learning, baseline/measurement plan, sample ROI calculation, and reporting template with limitations and recommendation for balanced scorecard approach.

You are a people analytics lead. [PASTE: Attrition data (headcount, % by dept/role/tenure), exit interview feedback, engagement survey results, comp data, performance data, promotion history]. Define attrition baseline (% departed / avg headcount, compare to industry), segment attrition (regretted vs. unregretted), conduct exit analysis (who's leaving? why? patterns?), build predictive model (factors that predict departure: tenure <2yr, recent role change, low engagement, performance feedback, comp below market), design interventions (by root cause), build retention program (stay interviews, manager training, comp monitoring, culture). Output attrition analytics framework with baseline analysis, segmentation, exit analysis by reason, predictive risk factors, intervention strategy by risk category, and retention program with effectiveness metrics (attrition rate trend, regretted vs. unregretted ratio).

You are a people analytics lead. [PASTE: Current HR data available (HRIS, payroll, recruiting, performance, engagement survey), business metrics, HR challenges]. Define dashboard components (Headcount metrics, Talent acquisition metrics, Retention metrics, Engagement metrics, Compensation metrics, Learning metrics), establish data infrastructure (HRIS as source of truth, integrations needed, data governance—who owns what?), create operational metrics (recruiting pipeline health, sourcing effectiveness, hiring manager responsiveness, onboarding completion rate, learning participation), create strategic metrics (engagement index, attrition trend, high-performer retention, diversity metrics, productivity metrics), design dashboards (Executive quarterly view, Department head team-specific, HR team day-to-day), build data governance (metric definitions, data quality, ownership, update frequency). Output HR analytics framework with dashboard components, metric definitions, data sources and infrastructure, data governance policy, and sample dashboards (executive, department, HR team) with data quality assessment and gaps.