ChatGPT и Claude для копирайтинга: рабочий процесс MOFU, который конвертит

Шаг 1: начните с полного MOFU brief

Сильный brief — это разница между полезной AI-копией и отполированным filler. Прежде чем писать prompt для любой из моделей, подготовьте one-page offer packet, который объясняет, кто buyer, почему offer важен сейчас, какое proof существует и какие claims запрещены.

Если вы все еще выбираете более широкий stack инструментов, сначала используйте основной гид по AI copywriting tools for networks and offers, прежде чем строить model-specific prompts. Приведенный ниже workflow предполагает, что вы уже знаете offer, stage funnel и traffic source.

Определите buyer и next action

Читатели MOFU не холодные. Они уже распознают проблему и нуждаются в помощи, чтобы решить, достаточно ли credible этот offer, механизм или следующий шаг, чтобы его предпринять.

Ваш brief должен прямо указывать next action: book a call, watch a VSL, start a trial, join a list, compare an offer или continue to checkout. Черновик, который не может объяснить это действие в одном предложении, не готов к тестированию.

Задайте границы claims и proof

Укажите claims, которые copy может делать, claims, которых она должна избегать, и стандарт proof, требуемый для каждого major statement. Для affiliate и paid acquisition работы это защищает и compliance, и trust.

Используйте практичные labels:

• Proven: подтверждено cited source, platform data, customer evidence или internal test result.
• Plausible: разумно, но еще требует validation.
• Hypothesis: полезно как angle, но небезопасно как claim.
• Prohibited: запрещено правилами сети, policy платформы или legal review.

Соберите reusable offer packet

Используйте одни и те же поля для каждого prompt:

• Название offer и category
• Network или platform, например ClickBank, Digistore24, BuyGoods или owned funnel
• Primary audience и geography
• Funnel stage и conversion action
• Promise, mechanism и proof type
• Top three objections
• Banned claims и required disclaimers
• Competitive benchmark, если он есть
• Tone rules и length limits

Шаг 2: используйте один prompt shell для ChatGPT и Claude

Используйте одинаковую структуру prompt в обеих моделях, чтобы outputs можно было сравнивать. Менять model, task, audience и format нормально; менять весь prompt каждый раз делает тест труднее для интерпретации.

Prompt shell

Role: You are a senior direct-response copywriter writing MOFU copy.
Objective: Create copy that helps an aware prospect take the next action.
Brief: [paste offer packet]
Constraints: No guaranteed outcomes, no unsupported claims, no pressure tactics.
Audience state: [problem-aware / solution-aware / offer-aware]
Task: Create [asset type] in [number] variations.
Output format: Hook, body, objection handling, proof line, CTA, revision notes.
Scoring: Rate clarity, mechanism specificity, proof credibility, objection handling, and CTA friction from 1-5.

Почему shell работает

Единый shell заставляет обе модели отвечать на одну и ту же коммерческую задачу. Это делает сравнение вопросом качества copy, а не удачи prompt.

ChatGPT часто эффективен для объема hooks, exploration angles и short-form variations. Claude часто полезен для более длинной структуры, transitions и argument flow. Рассматривайте это как tendencies, а не fixed rules, и дайте decision rubric решить.

Шаг 3: генерируйте парные черновики без потери version control

Запустите ChatGPT и Claude на одном и том же brief, затем сравните output side by side. Для большинства MOFU-кампаний три prompt passes с четкими ограничениями полезнее, чем двадцать разрозненных prompts.

Полезные requests на variation

Просите каждую модель о distinct strategic angles, а не о случайных переписываниях:

• Pain-first: начните с cost of staying stuck.
• Mechanism-first: объясните, почему этот approach работает иначе.
• Proof-first: начните с strongest credible evidence.
• Objection-first: сразу ответьте на biggest hesitation buyer.
• Comparison-first: позиционируйте against known alternative without making false claims.

Правила именования и хранения

Используйте имена файлов или labels вроде offer_angle03_proof_v1 и сохраняйте model, prompt version и test date. Это важно, когда выигрышный hook позже появляется в email, VSL и landing page.

Version control также предотвращает common AI workflow problem: teams remember that a line worked, but not which prompt, claim limit or traffic source produced it.

Шаг 4: оценивайте черновики перед любым paid test

Ни один AI-generated draft не должен попасть в paid test только потому, что звучит fluent. Используйте scoring gate перед публикацией, а затем используйте performance data после публикации.

Use minimum score 16 out of 25 with no category below 3. That threshold is not a guarantee of performance; it is a quality filter that keeps weak drafts away from media spend.

Шаг 5: сделайте affiliate copy safer и more believable

Affiliate copy fails when it overstates outcomes, hides uncertainty, or borrows proof it cannot support. Prompt the model to reduce claim risk before the draft exists.

Add these lines to affiliate prompts:

• Do not imply guaranteed results.
• Tie every performance claim to evidence or label it as a hypothesis.
• Do not invent testimonials, earnings, timelines, medical outcomes or platform relationships.
• Assume the reader is skeptical and reduce pressure tactics.
• Preserve required disclaimers and network restrictions.

This improves trust as well as compliance. A skeptical MOFU reader usually needs stronger reasoning, not louder promises.

Шаг 6: превратите один brief в VSLs, emails и ads

Лучшее использование AI не в том, чтобы писать isolated assets. А в том, чтобы переносить одно tested message across channels without changing the underlying claim.

Для VSL work используйте структуру из what is a VSL как spine:

• Hook
• Problem and cost of inaction
• Mechanism
• Proof
• Offer structure
• Objection handling
• CTA sequence

Когда кампании нужен более глубокий script work, объедините этот процесс с AI VSL writer and sales letter workflows. Для broader scaling подключите ту же message map к VSL copywriting guide for scaling offers.

Практичный flow прост: use ChatGPT for five opening hooks, pass the strongest hook to Claude for a three-minute VSL draft, turn that script into landing bullets and a follow-up email, then test the top line set against the current control.

Шаг 7: тестируйте с controlled splits

Copy test полезен только тогда, когда variable clear. Если вы одновременно меняете hook, landing page, CTA, traffic source и offer framing, вы можете получить result, но не lesson.

Используйте этот minimum protocol:

1. Pick one primary metric, such as cost per lead, opt-in rate, booked-call rate or cost per sale.
2. Test 8-12 variants across 2-3 angles.
3. Keep tracking, landing page structure and traffic source consistent.
4. Pause variants only after a meaningful minimum sample for that channel.
5. Promote the top 1-3 candidates into a second test, not straight to full scale.

As an estimate, disciplined MOFU iteration may produce a 5-15% improvement in progression rate, but results can be flat or negative depending on traffic quality, offer maturity and proof strength. Treat every range as planning guidance, not a promise.

Шаг 8: добавьте live market signals before scaling

Model output is only as useful as the market context around it. A clean prompt can still produce stale copy if it is based on old competitor angles, saturated claims or expired offer positioning.

Use public references such as Meta Ads Library to understand active creative direction, then verify whether the angle still appears to be current. For teams that need fresher offer-level context, Daily Intel Service methodology explains how Daily Intel Service evaluates VSLs, creatives and funnel signals before treating an offer as pre-scale, scaling or saturated.

When saturation is visible, prompt for differentiation instead of more volume:

• sharper objection rebuttals
• more specific mechanism language
• narrower audience framing
• stronger proof hierarchy
• less speculative outcome language

Шаг 9: назначайте роли моделей по workflow, а не по preference

The best model mix depends on the asset and team rhythm. Use ChatGPT where speed and variation matter most, and use Claude where long-form continuity and sequencing matter most.

A realistic planning estimate is 3-5 hours of human review per week for 60-120 candidate lines and 5-10 publishable candidates. Review time is not overhead; it is where weak claims, vague mechanisms and risky CTAs get removed.

Шаг 10: keep the loop clean and documented

A repeatable weekly loop makes the prompt library smarter over time. It also gives editors a record of why a line won or failed.

Use this cadence:

• Monday: refresh the offer packet and objections.
• Tuesday to Thursday: generate, score, edit and prepare variants.
• Friday: review test data and archive decisions.
• Weekend or off-cycle: update claim rules and proof standards.

For long-term search and trust quality, align public content with Google's guidance on helpful content and Google structured data policies. The same discipline applies to campaign copy: make the evidence visible, avoid unsupported exaggeration, and do not mark up FAQ content that users cannot read on the page.

Daily Intel Service is not required to use ChatGPT for copywriting, but it can help teams compare AI drafts against fresher market signals before larger launches. That is the useful balance: let AI speed up production, then let scoring, evidence and live performance decide what deserves budget.

Frequently Asked Questions

Q: Is ChatGPT good for copywriting?
A: Yes. ChatGPT is useful for copywriting when the prompt includes a clear offer brief, audience state, proof limits and output format. It is weakest when asked to create finished persuasive copy from vague inputs.

Q: Should I use ChatGPT or Claude for MOFU copy?
A: Use both when the campaign matters. ChatGPT is often efficient for hooks and short-form variants, while Claude is often useful for longer sequencing and transitions. Compare the outputs with the same scoring rubric.

Q: What is the best prompt structure for affiliate copywriting?
A: Start with the offer, audience, funnel stage, mechanism, proof, objections, claim restrictions and desired action. Then ask for hooks, body copy, objection handling, proof lines, CTA options and a 1-5 self-score for each criterion.

Q: How many AI copy variants should I test?
A: A practical starting point is 8-12 variants across 2-3 angles. Test one major variable at a time, keep tracking consistent, and move only the best 1-3 variants into the next test.

Q: How do I keep AI copy compliant?
A: Give the model explicit banned claims, required disclaimers, proof requirements and platform rules before generation. Do not allow invented testimonials, guaranteed outcomes or unsupported performance claims.

Q: Do I need Daily Intel Service for this workflow?
A: No. The workflow can run with your own briefs, tests and market research. Daily Intel Service is useful when you want external signal on what appears to be scaling before committing more production or media budget.