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Productivity at WorkPremiumintermediate
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The Decision Journal — Stop Repeating Bad Decisions

Document decisions BEFORE outcomes, then review to separate luck from skill.

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You are a decision scientist who designs decision journals for executives, investors, and professionals.

Create a decision journal system and help me document a current decision.

My decision: [DESCRIBE THE DECISION]
Stakes: [LOW / MEDIUM / HIGH / LIFE-CHANGING]
Deadline: [WHEN MUST YOU DECIDE?]
Reversibility: [EASILY REVERSIBLE / PARTIALLY / IRREVERSIBLE]

Create:

1. DECISION ENTRY (fill BEFORE deciding):
   - Date, mental state, all realistic options
   - For each option: expected outcome, probability, best/worst case, biggest fear
   - "What would I advise a friend?"
   - Cost of being wrong, what would change your mind

2. COGNITIVE BIAS CHECK:
   □ Sunk cost, □ Confirmation bias, □ Status quo, □ Social proof, □ Recency, □ Loss aversion
   For each checked: how it's distorting this decision

3. PRE-MORTEM — 6 months later this was a disaster. What went wrong?

4. DECISION RECORD — What you chose, why, confidence level, review date

5. REVIEW TEMPLATE — What happened, prediction accuracy, luck vs reasoning, lessons

6. JOURNAL SYSTEM — Tool, trigger, monthly review ritual
#decision-making#journal#cognitive-bias#metacognition#self-improvement

Works with

chatgptclaudegemini

💡 Pro Tips

  • •Write the entry BEFORE you decide — after, you'll rationalize
  • •The 'friend advice' question bypasses most biases
  • •Review old entries monthly to discover patterns

✨ Example Output

DECISION: 'Should I leave my job to freelance?'

BIAS CHECK:
☑ Status quo — Am I staying because it's good, or because change is scary?
☑ Social proof — Friends went freelance and post highlight reels.

PRE-MORTEM: 'Failed because I underestimated client pipeline time.'

FRIEND ADVICE: 'Don't quit until you have 3 months runway AND 2 paying clients.'

🧠 Why This Works

This prompt implements the decision journal practice advocated by Annie Duke (Thinking in Bets) and Shane Parrish (Farnam Street) — one of the few proven methods for improving decision quality over time. It works by separating the quality of a decision from its outcome, capturing your reasoning at the point of decision before you know the result. This prevents outcome bias — the tendency to judge past decisions by what happened rather than what you knew at the time. The AI serves as a structured interviewer, prompting you to articulate your assumptions, alternatives considered, confidence level, and what would change your mind. Over time, this practice reveals systematic biases in your decision-making that are invisible in the moment.

📅 When to Use This Prompt

Use before making any significant decision — career moves, large purchases, strategic bets, hiring choices, or project pivots. Essential when you notice a pattern of regret about past decisions and want to break the cycle. Perfect for leaders who make 10+ consequential decisions per week and need to track their reasoning. Ideal during uncertain situations where you're deciding under ambiguity and want a record of your thinking. Also valuable for quarterly reviews — comparing predictions to outcomes reveals calibration patterns.

🎯 What You'll Get

The AI produces a structured decision record with: the decision statement, context and constraints, alternatives considered, your chosen option with rationale, confidence level, key assumptions, what would change your mind, and a review date. Expect a format designed for future comparison — when you revisit in 3-6 months, you can evaluate your reasoning process independently of the outcome.

🔗 Related Prompts

Decision MakingPremium

Cognitive Bias Detector

Identify cognitive biases affecting your decisions with debiasing techniques.

cognitive-biaspsychologycritical-thinking
★4.6
intermediate