Use a source-first process. Collect credible sources before drafting, record what each source actually supports, extract every factual claim from the draft, match each claim to one or more sources, and mark it Supported, Partial, or Unsupported. Repair or remove anything that does not have enough evidence. Finish with a human approval step before publishing.
AI makes drafting faster, but fluent text can still contain stale product details, invented statistics, altered qualifiers, unsupported comparisons, and citations that do not support the sentence beside them. The failure is often subtle: the sentence sounds normal, the link is real, but the source never actually says what the draft claims.
Google's current Search Central guidance tells site owners using generative AI to focus on accuracy, quality, and relevance, and specifically says AI-generated content should be manually fact-checked before publication. Google also warns that generating many low-value pages primarily to manipulate rankings can fall under scaled content abuse. That makes a verification workflow useful for both readers and publishers.
Why rereading is not enough
A normal editorial reread is good at finding awkward wording and obvious contradictions. It is weaker at answering a different question: Which exact source supports this exact claim?
That is why a claim ledger works better. It separates factual verification from style editing. You first decide what is supported. Only after the evidence is closed do you polish the prose.
The 8-step source-ledger workflow
- Define the article promise.
Write one sentence describing what the reader should know or be able to do after reading. This prevents research from expanding into unrelated material. - Build a research plan.
Separate official or primary sources, credible secondary sources, competitor coverage, and claims that will need explicit verification. - Create the Source Ledger.
Give every useful source a stable ID such as S001. Record the URL, publisher, visible date, source type, and the exact claim it can support. - Draft from the ledger.
Give the writing model the relevant source notes instead of an empty prompt. Tell it to mark missing evidence rather than filling gaps. - Extract checkable claims.
Pull out numbers, dates, prices, product capabilities, named people or organizations, legal or policy statements, comparisons, quotations, and causal claims. - Match claims to sources.
For every claim, attach one or more source IDs and assign a status: Supported, Partial, or Unsupported. - Repair the draft.
Delete unsupported claims or rewrite them so they match what the evidence actually says. Restore qualifiers such as “may,” “up to,” “in this sample,” or “as of [date]” when those qualifiers are part of the evidence. - Run human approval.
Check links, metadata, CTA wording, and the final edited version. Editing can introduce a new error after the first fact-check, so verify the final text, not only the first draft.
What a Source Ledger should contain
| Field | Purpose | Example |
|---|---|---|
| source_id | Stable reference used in the claim map | S001 |
| url | Direct source location | Official documentation URL |
| publisher | Who is responsible for the source | Vendor, regulator, research group |
| date | Helps detect stale claims | 2026-11-10 |
| claim_supported | The exact fact the source can support | “Plan X includes feature Y” |
| source_type | Primary, official, secondary, vendor, study | Primary |
| confidence | Your confidence in fit and freshness | High |
| notes | Limits or qualifiers that must stay attached | Only on annual plan |
Build a separate Claim Check table
The Source Ledger describes evidence. The Claim Check table describes the draft. Keeping them separate is useful because one source can support multiple claims, and one claim may require several sources.
| claim_id | claim_text | source_ids | status | repair_action |
|---|---|---|---|---|
| C001 | Example factual claim | S001 | SUPPORTED | Keep |
| C002 | Claim broader than source | S002 | PARTIAL | Narrow wording |
| C003 | Claim with no evidence | — | UNSUPPORTED | Research or remove |
Claims that deserve priority
Not every sentence has the same downside if it is wrong. Review high-consequence claims first:
- Prices, plan limits, refund terms, deadlines, and availability
- Legal, financial, health, safety, employment, or regulatory statements
- Statistics, survey results, and research findings
- Product capabilities and compatibility
- Quotations and close paraphrases
- Comparisons such as cheaper, faster, safer, or more popular
- Claims about named people or organizations
Use AI as a reviewer, not the authority
AI is useful for bounded review tasks. It can extract potential factual claims, convert source notes into a table, flag sentences that appear unsourced, compare two versions for changed numbers, and format a verification log.
What it cannot do is make its own unsupported answer reliable simply by being asked again. “Are you sure?” is not a verification method. The evidence still needs to come from an appropriate source.
A practical prompt for claim extraction
Prompt:
Extract every externally verifiable factual claim from the draft. Include numbers, dates, prices, product capabilities, comparisons, named entities, policy statements, statistics, quotations, and causal claims. For each claim, return the exact claim text, section, evidence needed, and risk level. Do not judge the claim true merely because it sounds plausible.
When to use primary sources
Use official or primary sources whenever the detail can change or carries a meaningful consequence. Product pricing should come from the current pricing page. A policy should come from the organization responsible for the policy. A research claim should point to the original study when practical. A legal deadline should be checked against the responsible authority rather than a generic summary.
Do the SEO pass after the evidence pass
Fact-checking and SEO solve different problems. First make the article supportable. Then review the title, H1/H2 structure, search intent, internal links, duplicated sections, meta description, alt text, and structured data. Google explicitly includes metadata and structured data in its guidance to prioritize accuracy when AI assists content creation.
Want the editable workflow instead of rebuilding it?
The AI Research-to-Publish Engine includes the Source Ledger CSV, Claim Check CSV, article brief, publishing checklist, core prompt workflow, operations guide, and an n8n starter workflow.
See the AI Research-to-Publish Engine — $29Final pre-publish checklist
- Every material factual claim has evidence.
- Every source actually supports the wording used.
- Prices, dates, policies, specifications, and statistics were rechecked.
- Unsupported claims were removed or narrowed.
- No fabricated firsthand experience, testimonials, or quotations were added.
- All links resolve.
- Title, metadata, schema, and CTA wording were reviewed as claims too.
- A human reviewed the final edited version before publication.
Related guides
- AI content workflow with a human approval gate
- n8n AI blog workflow with fact checking and human review
- How to build a Source Ledger for AI writing