Problem: models invent quotes/stats when asked for SEO content. You need prompt constraints + verification gates so drafts can’t go live with fabricated claims.

Recommendation:
Adopt a two-track workflow: (A) strict factual drafts for publishable pages and (B) creative drafts for ideation. Only track A goes through automated + human fact-checking before publication. Use a strict system prompt that forbids fabrication and requires sourceable outputs, plus a standardized verification pass (automated SERP check + human reviewer). This reduces risk without killing productivity.

Quick decision criteria (choose one):
- Use strict factual flow when: pages will quote stats, named experts, or make comparative claims; legal/regulatory risk exists; brand reputation matters.
- Use creative flow when: brainstorming headlines, meta descriptions, or exploratory outlines where quotes/stats are unnecessary.
- Choose tools based on budget/skill: low budget = manual Google checks + simple prompts; medium = add SERP API for automated checks; high = subscribe to a retrieval-augmented-generation (RAG) workflow or research team.

Practical prompt constraints (copy-paste):
System prompt (required):
You are a factual content assistant. Do NOT fabricate quotes, statistics, or attributions. For every factual claim, include a citation with a URL. If you cannot find a reputable source, mark the claim as UNVERIFIABLE and do NOT invent a source.

User prompt (required output format):
Write the requested SEO section for [TOPIC]. Output two parts: (1) JSON array “claims” with objects {"claim_text","source_url_or_UNVERIFIABLE","verbatim_quote_if_any"}, and (2) a draft paragraph with inline numeric citations [1],[2] matching the claims array. Keep tone: [tone].

Example prompt snippet: "Topic: X. Length: 150–220 words. Provide claims JSON and draft. Use authoritative sources (gov, academic, major publications)."

Automated + human verification steps (practical):
1) Claim extraction: run model to produce the claims JSON (structure above). AI only extracts claims and candidate URLs.
2) Automated SERP check: use a SERP API (or manual site:google.com search) to confirm each source_url is real and matches the claim. If the URL is missing or dead, mark UNVERIFIABLE automatically.
3) Human verification: researcher/editor reviews each claim and its top 3 sources, flags discrepancies.
4) Editorial gate: only content with 100% verified claims or UNVERIFIABLE labels treated as remove-or-rewrite can be published.

Practical checklist (who does what):
- Writer/AI: produce draft + claims JSON.
- Auto-checker (tool): validate URLs, retrieve snippet, match claim keywords.
- Researcher: confirm at least one reputable source per claim; replace UNVERIFIABLE claims or rewrite.
- Editor: final approval and cite formatting.

Best-for / Avoid-if:
- Best-for: teams that publish research-like content, regulatory industries, and high-traffic SEO pages.
- Avoid-if: pure brainstorming/idea generation—use a separate creative prompt that explicitly allows hypotheticals.

Notes on tradeoffs: stricter pipelines increase time and cost (researcher time, SERP API fees). If you have a small team or low budget, enforce the system prompt + manual Google checks and a simple “no-source = no-publish” rule. For high-volume enterprise SEO, invest in RAG + automated source-matching.

Tools: start with your default model (ChatGPT) for drafting and consider a model focused on careful analysis (Claude) for the verification/extraction pass if you need longer context or more conservative outputs.

If you want, I can provide a ready-made system+user prompt tailored to one of your typical article templates and a simple regex/JSON schema you can plug into your QA script.

Compare ChatGPT and Gemini