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calibrating-ai-prompts.summary

Core Concept​

AI prompts ko semester-by-semester calibration chahiye kyun ke AI models evolve hote hain. Five-step Semester Calibration Protocol systematic scoring bias catch karta hai jabke five Thinking Score Card dimensions permanent rehti hain.

Key Mental Models​

  • Calibrate the Instrument, Not the Measurement: Five Score Card dimensions define karte hain ke thinking ka kya matlab hai aur kabhi change nahin honi chahiye. Sirf prompt wording tunable hai jo accurate scores elicit karti hai.
  • Systematic Bias vs. Random Noise: Choti per-exercise inaccuracies 40 data points mein wash out ho jati hain. Systematic drift (inflation, compression, blind spots) nahin hota -- aur calibration isi ko catch karti hai.

Critical Patterns​

  • Score Distribution Audit: flag karein agar Chapter 3 tak 80%+ score 8+ ho (too lenient) ya Chapter 8 tak 50%+ below 4 ho (too harsh)
  • Prompt Spot-Test: 5 archived deliverables (1 strong, 1 weak, 3 average) current AI ko submit karein, instructor assessment se compare karein
  • Feedback Challenge Review: agar 30%+ challenges succeed hon, prompts tighten karein; agar 0% challenge ho, students too deferential ho sakte hain
  • Model Migration: jab major AI model versions release hon, har semester se pehle spot-test re-run karein
  • Scenario Refresh: dated scenarios annually replace karein jabke exercise structure aur prompts unchanged rakhein

Common Mistakes​

  • Perfect AI scoring attempt karna, consistent scoring aim karne ke bajaye jo strong aur weak thinking ko reliably distinguish kare
  • Scores drift hone par prompt wording adjust karne ke bajaye Score Card dimensions change karna
  • "It seemed fine last semester" ki wajah se calibration skip karna -- AI models semesters ke darmiyan change hote hain

Connections​

  • Builds on: Tamam Part 0 exercises aur unke AI Check prompts
  • Leads to: Semesters ke across assessment system ki continuous improvement