Slide 1

The Workflow to Mass-Produce
20 FX Articles a Week with ChatGPT + Claude

A 3-layer check system that keeps quality high
20/wk
Output target
3 layers
Quality check
2 AIs
Split by role
Advanced
Target level
📌 What you'll learnThe 2-AI role split, the 5-step workflow, the 3-layer check, NG patterns, and the tool stack — all in 9 slides.
Slide 2 / Role Split

ChatGPT vs. Claude
Role-Split Map

GPT for research & proofreading, Claude for structure & writing
StepBest-fit AI
Keyword researchChatGPT
Article structure designClaude
Body writingClaude
Proofreading / trimmingChatGPT
Fact-check assistBoth
Slide 3 / 5 Steps

The 5-Step
Workflow

Research → structure → writing → check → publish (130 min per article)
  1. Research, 30 min (ChatGPT, batched on Monday)
  2. Structure design, 20 min (Claude + human edits)
  3. Body writing, 40 min (Claude to ~70% done)
  4. 3-layer check, 30 min (most important)
  5. Publish, 10 min (WordPress, etc.)
💡 The math20 articles/week × 130 min = 43 hours. A realistic range for one full-time person.
Slide 4 / 3-Layer Check, Layer 1

Layer 1:
Logic & Structure Check

A human reads it through to prevent broken logic and bloat
✓ ChecklistLogical leaps / validity of the H2 hierarchy / flow toward the conclusion / paragraph bloat / keeping the reader's perspective
  • Common issue
    AI's logical leapsConnections between paragraphs tend to become abrupt. Always fix them with a human eye.
Slide 5 / Layer 2

Layer 2:
Fact-Check

A human + web search to cross-check against primary sources
⚠️ The most important layerAlways cross-check numbers, statistics, proper nouns, and dates against official sources. Reference primary sources such as the Fed, the Bank of Japan, and Kingfin.
  • Typical AI misinformation
    "Plausible-sounding falsehoods"AI tends to invent figures like RevShare rates, minimum withdrawal amounts, and regulatory licenses. Always verify against the latest official information.
Slide 6 / Layer 3

Layer 3:
E-E-A-T + Compliance Check

Author, sources, update date, ad-labeling law, PR disclosure
✓ Turn it into a checklistRunning it mechanically every time keeps quality stable
  • Author profileAlways place an author byline at the end of the article
  • Cited sourcesSource links for numbers and statistics
  • Last-updated dateAlways display it
  • Ad-labeling & financial-instruments lawRemove NG phrases like "guaranteed to earn"
  • PR / sponsored disclosureRequired on affiliate links
Slide 7 / NG

3 NGs of
AI Mass Production

Full hand-off, mass same-prompt generation, skipping E-E-A-T
  • NG1
    Publishing AI output as-isQuality collapses from logical leaps, misinformation, and bloated writing
  • NG2
    Mass-generating with the same promptStructure becomes uniform and risks being flagged as spam
  • NG3
    Skipping E-E-A-T elementsNo author, sources, or update date makes it easy to be flagged as AI
Slide 8 / Tool Stack

The Tool Stack
Behind the Efficiency

Notion + Ahrefs + ChatGPT + Claude + Midjourney + WP
UseTool
Article managementNotion
Keyword researchAhrefs/Ubersuggest
AI writingChatGPT+Claude
Image generationMidjourney/DALL-E
PublishingWordPress+AddQuicktag
💰 CostAbout $40/month for the two AI services. At 20 articles a week, that's roughly ¥75 of AI cost per article.
Slide 9 / Wrap-Up

Today's Action:
Start with 5 a Week

Scale up to 20 in stages over 3–6 months

📋 Recap

① Role split: GPT for research/proofreading, Claude for structure/writing
② 5 steps: research/structure/writing/check/publish
③ Layer 1: logic & structure check (human)
④ Layer 2: fact-check (most important)
⑤ Layer 3: E-E-A-T + compliance check
+α: avoid the 3 NG patterns and use the right tool stack
✅ Conclusion: As of 2026, AI mass production is no longer a "quality vs. quantity" either/or — it's a space where "AI role split + 3-layer check + tool stack make both achievable." A pace of 20 a week is realistic for advanced users. First establish a quality standard at 5 a week, then scale up in stages over 3–6 months — that's the realistic roadmap.

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