Fact-Checking Workflows

Why Fact-Check AI Output?

AI systems — including Perplexity — can confidently state inaccurate information. The risk is higher for specific statistics, quotes, dates, and causality claims. A fact-checking workflow catches errors before they damage credibility.

The Claim-Verify Pattern

For any important claim, run a dedicated verification thread:

Claim: "McKinsey research shows AI increases productivity by 40% for knowledge workers."

Verification query: "Find the specific McKinsey study that claims 40% productivity increase for knowledge workers from AI. What was the methodology and sample size?"

If Perplexity can't find the exact study, the claim may be misattributed.

The Devil's Advocate Query

After receiving research, actively try to disprove key findings:

"Give me evidence that contradicts or complicates the claim that [X]. What do critics say?"

This surfaces nuance that a single-direction search misses.

Verifying Statistics

For market statistics and numerical claims:

1. Ask for the original source (company, methodology, date)
2. Search for the primary source directly
3. Check if multiple independent sources agree on the number
4. Look for how the number was calculated

Statistics that lack primary sources, are undated, or only appear on one site should be used cautiously.

The Telephone Problem

Research gets summarized, then summarized again, then cited as fact. Ask:

"Is this statistic available from a primary source, or is it being cited secondhand from another summary?"

Perplexity will usually tell you if a claim traces back to a primary source or appears to be derivative.

Building a Fact-Check Checklist

For reports that will be used in decisions or shared externally:

• All statistics have primary sources identified
• No claim is more than 18 months old (or flagged as historical)
• Contradicting evidence was searched for
• Quotes are verified verbatim, not paraphrased
• Market figures have methodology noted