Why Δ matters
High Δ means your gut instinct about AI output quality is miscalibrated. You either trust too much (overestimation) or too little (underestimation). Both cost time and credibility.
Understanding Your Δ
Delta (Δ) = Your Prediction − Actual Score
| Your Δ | Meaning | Impact | |--------|---------|--------| | +3 or higher | Overestimation | You approve outputs that need revision | | +1 to +2 | Slight overestimation | Minor efficiency loss | | -1 to +1 | Calibrated | Trust your judgment | | -1 to -2 | Slight underestimation | Over-editing good outputs | | -3 or lower | Underestimation | Wasting time on unnecessary revision |
The 7-Day Drill Plan
Day 1: Baseline (15 min)
- Select 5 AI outputs from your recent work
- For each, write a prediction (1-10 quality score)
- Score each with your rubric
- Calculate Δ for each
- Record your average Δ
Day 1 Log: | Output | Prediction | Actual | Δ | |--------|------------|--------|---| | 1 | | | | | 2 | | | | | 3 | | | | | 4 | | | | | 5 | | | | | Average Δ: | | | |
Day 2: Pattern Recognition (10 min)
- Review Day 1 results
- Identify: Are you consistently over or under?
- By how much?
- On what types of outputs?
Pattern Analysis:
- Direction: [ ] Overestimate [ ] Underestimate [ ] Mixed
- Magnitude: Average Δ = ___
- Pattern: Worse on [ ] Long outputs [ ] Technical [ ] Creative [ ] Other: ___
Day 3: Adjusted Predictions (10 min)
- Select 5 new outputs
- Make your gut prediction
- Apply correction: If you overestimate, subtract your average Δ
- Score and compare
Day 3 Log: | Output | Gut | Adjusted | Actual | Gut Δ | Adj Δ | |--------|-----|----------|--------|-------|-------| | 1 | | | | | | | 2 | | | | | | | 3 | | | | | | | 4 | | | | | | | 5 | | | | | |
Day 4: Criteria Anchoring (10 min)
- Select 5 outputs
- Before predicting, write down which rubric criteria might be weak
- Predict, score, calculate Δ
- Were your pre-identified criteria actually the weak points?
Day 5: Time Pressure Practice (10 min)
- Set a 60-second timer per output
- Predict 5 outputs quickly
- Score at normal pace
- Does time pressure affect your Δ?
“"Day 5 was eye-opening. Under time pressure, my Δ jumped from +2 to +5. Now I know to slow down on deadline reviews."”
Day 6: Confidence Calibration (10 min)
- Select 5 outputs
- Predict AND rate your confidence (1-5)
- Score and calculate Δ
- Plot: High confidence predictions should have lower Δ
Day 6 Log: | Output | Prediction | Confidence | Actual | Δ | |--------|------------|------------|--------|---| | 1 | | | | | | 2 | | | | | | 3 | | | | | | 4 | | | | | | 5 | | | | |
Analysis: Correlation between confidence and accuracy: ___
Day 7: Final Assessment (15 min)
- Repeat Day 1 protocol (5 outputs, predict, score)
- Compare Day 1 vs Day 7 average Δ
- Document your improvement
Progress Summary: | Metric | Day 1 | Day 7 | Change | |--------|-------|-------|--------| | Average Δ | | | | | Direction | | | | | Worst output type | | | |
Weekly Log Template
Prediction Practice Log — Week of [Date]
Day 1: Baseline
- Average Δ: ___
- Pattern: ___
Day 2: Pattern Recognition
- Direction: Over / Under
- Magnitude: ___
- Trigger: ___
Day 3: Adjusted Predictions
- Improvement: ___
Day 4: Criteria Anchoring
- Accurate pre-identification: ___ / 5
Day 5: Time Pressure
- Normal Δ vs Pressured Δ: ___ vs ___
Day 6: Confidence Calibration
- Correlation: ___
Day 7: Final
- Δ improvement: ___%
Next Steps
- Continue practicing: Daily / 2x week / Weekly
- Focus area: ___
Maintenance Protocol
After the initial week:
- ✓Weekly check: 5 predictions + scores, calculate Δ
- ✓Monthly calibration: Full 7-day drill if Δ creeps above ±2
- ✓Trigger review: If you notice a bad approval or unnecessary edit, log it
Advanced: Team Calibration
Once individual Δ is below ±2, calibrate with your team:
- All members predict same 5 outputs
- Compare predictions before scoring
- Score together, discuss variance
- Align on rubric interpretation
“"Our team's average Δ dropped from ±4 to ±1.5 after two group calibration sessions. Fewer revision cycles, faster shipping."”
Citations
- Tetlock, P. E. (2015). Superforecasting: The Art and Science of Prediction. Crown.
- Kahneman, D. & Klein, G. (2009). "Conditions for Intuitive Expertise." American Psychologist, 64(6), 515-526.
- MIT CSAIL. (2024). "Calibration Training for AI Evaluation: A Controlled Study." CHI '24 Proceedings.
Apply this now
Practice prompt
Complete Day 1 of the drill today—it takes 15 minutes.
Try this now
Write down your prediction for the next AI output you review. Score it. Note the Δ.
Common pitfall
Skipping the log—improvement requires data. No log, no learning.
Key takeaways
- •Measure your baseline Δ before trying to improve—you can't fix what you don't track
- •Apply a mechanical correction based on your pattern: if you overestimate by +3, subtract 3
- •Time pressure amplifies miscalibration—slow down on high-stakes reviews
See it in action
Drop this into a measured run—demo it, then tie it back to your methodology.
See also
Pair this play with related resources, methodology notes, or quickstarts.
Further reading
Next Steps
Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.