Review the Sources Cited in AI Answers
Review AI citations in LLMIC, inspect source URLs and supporting passages, and separate observed citations from content-readiness suggestions.
An AI answer linking to a page tells you where it points. It does not automatically tell you whether that page supports the claim. Citation analysis helps you follow the link, inspect the evidence and distinguish a useful source relationship from a misleading attribution.
What this guide helps you do
AI citations can show which sources supported a measured answer, but they do not prove that every claim is correct. Use LLMIC’s citation analysis to inspect the answer, source URL, quoted evidence and provider context together.
Build availability: This guide describes the updated workflow checked against the September 2026 development build. Some controls may not yet be included in the public installer. Check the release notes and your app version before following those steps.
The workflow at a glance
- Open a measured answer
Keep its question and provider details. - Follow the cited URL
Record the actual destination and redirects. - Inspect the passage
Check whether it supports the claim. - Record your review
Keep observation and human judgment separate.
Workflow illustration. These steps explain the process; they are not measured performance results.
Start with the right kind of evidence
Use the answer and citation details from an AI benchmark when you need observed citations. Citation Engine or readiness recommendations may help you review page structure, but a readiness score is not a measured probability that a provider will cite the page. Keep local suggestions separate from returned provider evidence.
Check the source identity
Inspect the URL attached to the answer and confirm which publisher it belongs to. If it redirects, distinguish the originally cited URL from the observed destination. A familiar domain name does not prove that the source is the page you intended to measure. Preserve the observation date when recording attribution.
Read the claim and the passage together
Find the specific statement the answer makes and compare it with the source passage. Does the page support the whole claim, only part of it, or something different? Check dates, quantities and conditions. Exact quoted words can establish a text match, but they do not certify that the underlying claim is factually correct.
Keep access failures honest
A blocked, missing or unreadable source cannot be treated as verified support. Record the access problem and review it separately. Likewise, a page that your crawler can access is not automatically a source the AI provider cited. Access evidence and answer-linked citation evidence answer different questions and need separate labels.
Turn the review into a useful action
An illustrative action might be to clarify a dated product requirement on a page that an answer cited incorrectly. Confirm the true requirement before editing. Keep the original answer, cited URL and reason with the task. After publication, recrawl the page and run a matched benchmark if needed; a content correction does not force future citation behavior.
Quick reference
| What you see | What to do next |
|---|---|
| Cited URL | A source link observed in a particular answer. |
| Readable source | Access succeeded; support still needs review. |
| Supporting passage | Compare the exact claim, context and date. |
| Readiness suggestion | Use as guidance, not proof of a real citation. |
Your next step
Continue with run a controlled benchmark, understand visibility metrics, share evidence with context. Return to the documentation home for the full workflow. Before sharing an export, check its website, dates and filters, and remove private information. If a control is missing or a result looks wrong, contact support with your app version and a sanitized example.
Frequently asked questions
Review AI citations in LLMIC, inspect source URLs and supporting passages, and separate observed citations from content-readiness suggestions.
Load the relevant crawl, define the prompt or question set, and connect only the supported provider needed for the measurement. Record the provider, model, location, language, and run time so the observation can be interpreted later.
Review the exact prompt, returned answer, mention or citation, cited URL, provider metadata, and any unsuccessful check. Keep measured provider output separate from local readiness checks and editorial recommendations.
Turn the observation into a review task with the affected page, captured answer evidence, intended audience need, proposed change, and owner. A missing mention is an observation from that test, not proof that the brand is invisible everywhere.
Repeat the same saved question set with comparable provider settings and record the new dated observation. Do not compare different prompts, models, markets, or time periods as if they were the same measurement.
A provider run is a dated observation, not a probability or ranking guarantee. Results can change by model, prompt, location, personalization, availability, and time, and a local checklist cannot prove inclusion in an AI answer.
Save or export the evidence, assign the approved action, and use the related guides linked on the page for the next check. After implementation, repeat the same workflow against a fresh crawl so the result is comparable.