Building a Fact-Checking Workflow

# Building a Fact-Checking Workflow

Knowing that AI hallucinations exist is step one. Having a system for catching them every time is what separates casual AI users from professionals. This lesson gives you a concrete, repeatable workflow.

The Problem: Verification at Scale

When AI generates a 500-word summary, a 10-item list, or a multi-page report, you cannot realistically verify every single word against primary sources. You need a triage system that tells you what to check, what to skim, and what is safe to trust.

The TRUST Framework

This five-step framework works for any AI-generated content, from a quick email draft to a detailed research summary.

T — Triage the Output by Risk Level

Before verifying anything, classify the content by risk:

High risk (always verify): - Specific numbers, statistics, and data points - Named individuals, organizations, dates, and locations - Citations, references, quotes, and URLs - Legal, medical, financial, or regulatory claims - Any claim that would be embarrassing or harmful if wrong

Medium risk (spot-check): - General explanations of well-known concepts - Structural recommendations and frameworks - Lists of categories, types, or approaches

Low risk (quick scan): - Writing style and tone (you can judge this yourself) - Format and structure of the output - Summaries of text you provided as input

R — Research the High-Risk Claims

For every high-risk item, verify against a primary or authoritative source.