Spot lookahead bias in analyses

Prompt that finds lookahead bias where future labels inflate past results. Paste it into an AI tool of your choice, which then spots lookahead bias in analyses.

Topic: Spreadsheets & data
Type: Prompt
Level: Intermediate
Time: about 15 min

Results depend on the tool, the model and what you enter.

Result preview

Example: written by our editors, not real AI output.

Churn model AUC 0.91 Feature: current_plan_status at month end Decision time: day 1 of month Flag: status known only later As-of join: plan at day 1 AUC drops to 0.74 without future label Guard: point-in-time feature store check

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AnalysisWhat is evaluated?

Time fieldWhat clocks exist?

RiskWhat already happened?

Your prompt

Help me spot lookahead bias in an analysis.
Analysis: [Analysis]
Time field: [Time field]
Risk: [Risk]
Steps: 1. Read the claim, the features and when each input became known. 2. Flag joins or filters that use future labels, revised figures or post-event status. 3. Rebuild the timeline as-of the decision moment. 4. Estimate how much the result changes without future data. 5. Propose guards in the pipeline and in review. 6. Write a short note for stakeholders.
Output: bias findings, corrected timeline, impact estimate and guards.

Take particular care to use only what was known at decision time, never future labels.

Before you answer, check: Are all instructions met? If key information is missing or a placeholder in square brackets is still in the text, ask instead of guessing. Mark assumptions as assumptions.

This is an instruction for an AI, not the result itself.

Where this prompt works

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How to use it

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Copy the finished prompt into an AI tool of your choice.
Paste schema, sample rows and the decision timestamp, without personal data.

Why does this work?

Joining today’s status onto last month’s cohort is lookahead. The past never knew that label.

Tips

Freeze features at the decision timestamp and test with deliberately shifted labels.

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Spot lookahead bias in analyses

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