Doublecheck is now active. I'll verify factual claims in my responses before presenting them. You'll see an inline verification summary after each substantive response. Say "full report" on any response to get the complete three-layer verification with detailed sourcing. Turn it off anytime by saying "turn off doublecheck."

1. Generate your response normally.
2. After the response, add a

   Verification

   section.
3. In that section, list each verifiable claim with its confidence rating and a source link where available.

---
**Verification (N claims checked)**

- [VERIFIED] "Claim text" -- Source: [URL]
- [VERIFIED] "Claim text" -- Source: [URL]
- [PLAUSIBLE] "Claim text" -- no specific source found
- [FABRICATION RISK] "Claim text" -- could not find this citation; verify before relying on it

**Heads up:** I'm not confident about [specific claim]. I couldn't find a supporting source. You should verify this independently before relying on it.

assets/verification-report-template.md

assets/verification-report-template.md

_Say "full report" for detailed three-layer verification with sources._

assets/verification-report-template.md

Doublecheck is now off. I'll respond normally without inline verification. You can reactivate it anytime.

• Does the text contradict itself anywhere? (e.g., states two different dates for the same event)
• Are there claims that are logically incompatible?
• Does the text make assumptions in one section that it contradicts in another?

• Do you recall this being accurate?
• Is this the kind of claim where models frequently hallucinate? (Specific citations, precise statistics, and exact dates are high-risk categories.)
• Is the claim specific enough to verify, or is it vague enough to be unfalsifiable?

1. Formulate a search query that would surface the primary source. For citations, search for the exact title or case name. For statistics, search for the specific number and topic. For factual claims, search for the key entities and relationships.
2. Run the search using

   web_search

   . If the first search doesn't return relevant results, reformulate and try once more with different terms.
3. Evaluate what you find:
   • Did you find a primary or authoritative source that directly addresses the claim?
   • Did you find contradicting information from a credible source?
   • Did you find nothing relevant? (This is itself a signal -- real things usually have a web footprint.)
4. Record the result with the source URL. Always provide the URL even if you also summarize what the source says.

• Official documentation, specifications, and standards
• Court records, legislative texts, regulatory filings
• Peer-reviewed publications
• Official organizational websites and press releases
• Established reference works (encyclopedias, legal databases)

1. Search for the exact citation (case name, title, section number).
2. If you find it, confirm the cited content actually says what the target text claims it says.
3. If you cannot find it at all, flag it as FABRICATION RISK. Models frequently generate plausible-sounding citations for things that don't exist.

1. Fabricated citations -- The text cites a specific case, paper, or statute that you could not find in Layer 2. This is the most dangerous hallucination pattern because it looks authoritative.
2. Precise numbers without sources -- The text states a specific statistic (e.g., "78% of companies...") without indicating where the number comes from. Models often generate plausible-sounding statistics that are entirely made up.
3. Confident specificity on uncertain topics -- The text states something very specific about a topic where specifics are genuinely unknown or disputed. Watch for exact dates, precise dollar amounts, and definitive attributions in areas where experts disagree.
4. Plausible-but-wrong associations -- The text associates a concept, ruling, or event with the wrong entity. For example, attributing a ruling to the wrong court, assigning a quote to the wrong person, or describing a law's provision incorrectly while getting the law's name right.
5. Temporal confusion -- The text describes something as current that may be outdated, or describes a sequence of events in the wrong order.
6. Overgeneralization -- The text states something as universally true when it applies only in specific jurisdictions, contexts, or time periods. Common in legal and regulatory content.
7. Missing qualifiers -- The text presents a nuanced topic as settled or straightforward when significant exceptions, limitations, or counterarguments exist.

• What would make this claim wrong?
• Is there a common misconception in this area that the model might have picked up?
• If I were a subject matter expert, would I object to how this is stated?
• Is this claim from before or after my training data cutoff, and might it be outdated?

• A specific citation that cannot be found anywhere
• A statistic with no identifiable source
• A legal or regulatory claim that contradicts what authoritative sources say
• A claim that has been stated with high confidence but is actually disputed or uncertain

• Provide links, not verdicts. The user decides what's true, not you.
• When you found contradicting information, present both sides with sources. Don't pick a winner.
• If a claim is unfalsifiable (too vague or subjective to verify), say so. "Unfalsifiable" is useful information.
• Be explicit about what you could not check. "I could not verify this" is different from "this is wrong."
• Group findings by severity. Lead with the items that need the most attention.

Limitations of this verification:

• This tool accelerates human verification; it does not replace it.
• Web search results may not include the most recent information or paywalled sources.
• The adversarial review uses the same underlying model that may have produced the original output. It catches many issues but cannot catch all of them.
• A claim rated VERIFIED means a supporting source was found, not that the claim is definitely correct. Sources can be wrong too.
• Claims rated PLAUSIBLE may still be wrong. The absence of contradicting evidence is not proof of accuracy.

• Case names, citations, and holdings are frequently fabricated by models
• Jurisdictional nuances are often flattened or omitted
• Statutory language may be paraphrased in ways that change the legal meaning
• "Majority rule" and "minority rule" distinctions are often lost

• Check that cited studies actually exist and that the results are accurately described
• Watch for outdated guidelines being presented as current
• Flag dosages, treatment protocols, or diagnostic criteria -- these change and errors can be dangerous

• Verify specific dollar amounts, dates, and thresholds
• Check that regulatory requirements are attributed to the correct jurisdiction and are current
• Watch for tax law claims that may be outdated after recent legislative changes

• Verify CVE numbers, vulnerability descriptions, and affected versions
• Check that API specifications and configuration instructions match current documentation
• Watch for version-specific information that may be outdated