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Prompt template

Skeptical Fact Checker

Fact-checking prompt for research, claims review, and skeptical analysis. Encourages double-checking, uncertainty handling, and a bias toward evidence over agreement.

analysis
critical-thinking
fact-checking
research
system-prompt
verification

Best for

  • teams that want a reusable prompt instead of rewriting instructions from scratch

Use this when

  • you want a higher-signal prompt template you can reuse with better inputs

Expected outcome

  • A clearer, easier-to-reuse result with stronger structure and less back-and-forth
# Skeptical Fact Checker

Skeptical Fact Checker is a research prompt for users who want less agreement and more verification. It tells the assistant to doubt easy claims, challenge both the user and itself, and treat factual accuracy as more important than conversational smoothness.

## Best For

- Reviewing claims before repeating them publicly
- Checking assumptions in strategy, product, or research work
- Summarizing evidence while marking uncertainty
- Countering overconfident AI answers

## How To Use It

1. Give the model the claim, draft, or statement you want checked.
2. Ask it to distinguish verified facts from assumptions.
3. Request a confidence level or uncertainty notes when needed.
4. Pair it with source material for higher-quality results.

## Prompt Template

You are an expert who double checks things, you are skeptical and you do research. I am not always right. Neither are you, but we both strive for accuracy.

## Good Follow-Ups

- "What in this answer is least certain?"
- "Which claim here needs verification before I rely on it?"
- "Separate evidence from interpretation."

## Limitations

This prompt increases skepticism, but it does not magically grant current or private information. High-stakes claims still need primary sources and human review.

Customization tips

Turn this into a reusable workflow, not a one-off prompt

  • Replace the generic context with your stack, domain, or customer segment before you run it.
  • Add constraints for tone, output format, and level of detail so the model matches your workflow.
  • Paste a real example input or diff to get results that are grounded instead of generic.
Published October 29, 2025

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