Automated Support Ticket Quality Assurance Scoring
Build a system that automatically scores support ticket responses against quality criteria, identifies coaching opportunities, and generates QA reports.
Scale quality assurance from reviewing 2% of tickets to 100% with AI scoring, enabling consistent coaching and quality improvement.
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Prompt objective
Scale quality assurance from reviewing 2% of tickets to 100% with AI scoring, enabling consistent coaching and quality improvement.
Real use case
A support team of 30 agents has 2 QA analysts who manually review 2% of tickets. Quality issues go undetected for weeks, and coaching is inconsistent.
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Prompt
Act as a support quality assurance specialist. Design an automated ticket QA scoring system for [COMPANY NAME] with [NUMBER] agents and [NUMBER] tickets per month. **Context:** - Support team: [NUMBER] agents across [NUMBER] tiers - Monthly tickets: [NUMBER] - Current QA coverage: [PERCENTAGE]% manual review - Quality criteria: [CRITERION 1], [CRITERION 2], [CRITERION 3], [CRITERION 4], [CRITERION 5] - QA tool: [SPREADSHEET/MAESTRO/OTHER] **Deliverables (numbered):** 1. QA rubric: define [NUMBER] scoring criteria with clear definitions and examples for each score level (1-5): tone and empathy, accuracy of information, completeness of resolution, professionalism, and process adherence 2. AI scoring engine: analyze each closed ticket against the rubric, generate scores per criterion with supporting evidence (quotes from the ticket), and calculate overall quality score 3. Calibration: compare AI scores to human QA scores on a sample of [NUMBER] tickets, measure inter-rater reliability, adjust scoring weights to match human standards 4. Coaching insights: identify per-agent patterns (strong areas, improvement areas), suggest specific coaching topics, and track improvement over time 5. Alert system: flag tickets with quality score below [SCORE] for immediate review, detect critical errors (wrong information, rude tone, policy violations) in real-time 6. QA dashboard: team and individual quality scores, score distribution, trend analysis, top coaching themes, and correlation between quality scores and CSAT 7. Reporting: weekly QA summary, monthly quality trends, agent scorecards, and calibration report comparing AI vs. human scoring accuracy **Constraints:** - Must provide explainable scores (show which parts of the ticket led to each score) - Must handle different ticket types with appropriate rubrics (billing vs. technical vs. general) - Must never replace human QA -- augment and prioritize for human review
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- 1Replace the key placeholders first: COMPANY NAME, NUMBER, PERCENTAGE, CRITERION 1.
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