Repurpose One Article Without Factual Drift

• M

  Make

  AI Automation & Agents

  Visual automation platform for scenarios and AI agents, billed in credits

  Free tier◦Documented

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   Step 1: Lock the source version and release rules

   Save the exact article version, URL or file hash, publication date and owner. Define the derivative channels, audience, length constraints and claims that must not appear. If the source contains stale or uncertain statements, resolve or exclude them before repurposing. Do not let later source edits silently change the basis of review. Verification requires that every reviewer can open the same source and see the same prohibited-claim list.

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   Step 2: Create the source-of-truth fact sheet

   Run the fact-sheet prompt and extract one row per factual claim with exact supporting passage, source location, approved wording boundaries and expiry or review date where relevant. Separate facts from opinions, recommendations and marketing language. Preserve units, qualifiers and uncertainty. A fact is not approved merely because it appears in the article; verify it against the article’s cited primary source when the derivative will repeat it.

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   Step 3: Draft for each channel without changing the facts

   Use the channel-native derivative prompt with one channel at a time. Adapt structure, hook, tone and call to action, but supply the locked fact sheet as the only factual authority. Require the model to identify fact IDs used in each draft and to leave unsupported gaps unresolved. Do not compress a qualified claim into an absolute promise. The output is ready for audit when every factual sentence maps to a fact ID or is clearly labelled as opinion.

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   Step 4: Run a claim-by-claim drift comparison

   Use the drift-detection prompt to compare each derivative against the fact sheet. Check numbers, names, time periods, causal language, scope, superlatives and omitted qualifiers. Record additions as well as contradictions; a plausible new detail is still unsupported. Keep a rejected draft example so editors can recognize common failure patterns. Verification succeeds when the audit accounts for every factual sentence and identifies all prohibited claims.

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   Step 5: Route discrepancies through human review

   If automating handoffs, configure Make to create review items rather than publish directly. Attach source version, derivative, fact IDs and audit findings. Send ambiguous, high-risk or changed claims to the source owner. Require an explicit approve, revise or reject state and store the reviewer and timestamp. Test retry behavior so the same asset cannot be released twice. A successful scenario execution does not validate the content itself.

   Tool: Make

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   Step 6: Release only approved assets and monitor corrections

   Publish or schedule only the approved version and retain its audit record. Confirm the live asset matches the approved text, links and disclosures. If the source changes, identify derivatives that depend on affected fact IDs and reopen them for review. Record corrections rather than silently overwriting evidence. The workflow ends with a traceable release register, not with the model’s first acceptable-looking draft.

• Visit Make ↗

• Create a source-of-truth fact sheet · tested on OpenAI GPT-5 (Codex) — editorial dry run
• Build channel-native derivatives · tested on OpenAI GPT-5 (Codex) — editorial dry run
• Detect factual drift across assets · tested on OpenAI GPT-5 (Codex) — editorial dry run

• OutputIllustrative scenario: drift detected before release

  The synthetic fixture yielded eight approved fact rows and three channel drafts. The audit caught one removed qualifier, one unsupported superlative and one prohibited claim. After revision, all retained factual sentences mapped to a fact ID.

• DatasetTesting limitation

  The article, facts and channels were synthetic. Codex ran the editorial sequence on 2026-10-03. Make, publishing platforms and live audience data were not used.

✓Last verified Oct 3, 2026