
LLM fact checking: evaluate what an answer actually supports
Build a claim-level evaluation process that separates retrieval gaps, assessment errors, and missing evidence.
INDEPENDENT GUIDES / EVIDENCE-FIRST THINKING
Make every claim easier to check. Explore fact APIs, LLM verification, and citation workflows built around sources—not just confidence.
Practical guides. Inspectable examples. No black-box promises.
JSON claim → source → assessment
“The Atlas demonstration archive opened in 2021.”
{
"claim_id": "demo-001",
"status": "supported",
"evidence": {
"source": "example-release-note",
"passage": "Opened in 2021"
},
"scope": "fictional evidence pack"
}THE EVIDENCE TOOLKIT
Choose the part of the workflow you want to understand. Every guide makes room for context, sources, and uncertainty.
Separate information retrieval from an evidence-backed assessment.
Turn complex text into atomic claims without losing context.
Evaluate what an AI answer supports, omits, or gets wrong.
Check the work, the reference, and the passage that matters.
Read journal articles beyond the abstract and the headline.
Connect corrections and updates to the claims that depend on them.
THE METHOD / 4 CONNECTED STEPS
A useful fact-checking workflow leaves a trail you can follow. Our proposed method starts with a precise statement and ends with an assessment you can inspect—not just a badge.
Read our evidence principlesPreserve who, what, when, and every qualifier that changes the question.
Locate a relevant passage. A familiar source name is not enough.
Distinguish support, contradiction, and evidence that cannot resolve the claim.
Record the scope and source version, then review meaningful changes.
“The model is confident, so it must be correct.”
“Which source supports this exact claim—and what does it leave unresolved?”
A CLOSER LOOK / LOCAL EXAMPLES
Change the claim, not the evidence. These fixed examples show why support, contradiction, and missing information belong in different categories.
Inspect the JSON examplesILLUSTRATIVE DATA ONLY. Atlas is fictional.
No statement is submitted for verification.
“The Atlas demonstration archive opened in 2021. It accepts JSON exports.”Supported
The stated opening year matches the supplied example passage. This assessment applies only to this fictional evidence pack.
LOCAL, FICTIONAL EXAMPLE · No live checks or external requests.
THE EVIDENCE JOURNAL

Build a claim-level evaluation process that separates retrieval gaps, assessment errors, and missing evidence.

Go beyond working links to inspect bibliographic identity, source passages, quotations, and the meaning of a citation.

Design a source-monitoring process that turns scholarly updates into reviewable actions for dependent claims.
USEFUL DISTINCTIONS
Clear boundaries make better tools.
Here is what the site does—and does not do.
Go from a better question to a clearer evidence trail—one fieldnote at a time.