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AI Agents · Lesson

Source Verification and Citation

Cross-referencing claims across sources and attaching source URLs to facts.

Why Source Verification Matters

An agent can retrieve a false claim from a low-quality source and present it as fact. Without verification, the research agent is just a sophisticated search engine with no quality filter.

Verification adds a layer that checks whether claims are corroborated by multiple independent sources.

The Two-Source Rule

A claim is considered verified when it appears in at least two independent sources. 'Independent' means different domains — finding the same claim on two sites that both cite the same original article does not count.

from urllib.parse import urlparse

def extract_domain(url: str) -> str:
    return urlparse(url).netloc.replace('www.', '')

def is_verified(fact: str, all_facts: list[dict]) -> bool:
    matching_domains = set()
    for f in all_facts:
        if fact.lower() in f['fact'].lower() or f['fact'].lower() in fact.lower():
            matching_domains.add(extract_domain(f['source']))
    return len(matching_domains) >= 2

# Example
facts = [
    {'fact': 'Inflation peaked at 9.1% in June 2022', 'source': 'https://bls.gov/cpi'},
    {'fact': 'US inflation hit 9.1% peak in mid-2022',  'source': 'https://reuters.com/article/a'}
]
print(is_verified('inflation peaked at 9.1%', facts))  # True

All lessons in this course

  1. Multi-Step Research Loop Design
  2. Source Verification and Citation
  3. Structured Report Generation
  4. Fact-Checking and Hallucination Prevention
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