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AI Marketing Copy Workflow

Generate copy from a factual brief, then evaluate it against a specific message and audience.

Introduction​

AI can produce many copy variants quickly. Without a precise brief, those variants repeat familiar marketing patterns and make claims the product cannot support.

Use the model for drafting and revision. Keep positioning, factual approval, and experiment design with people who understand the product and audience.

Understanding the Workflow​

A copy brief defines the product truth, audience, message, voice, and desired action for one piece of copy.

Definition

Copy Brief

A written set of facts and constraints that defines the audience, product claim, message, voice, and desired reader action.

Product truth is the evidence the copy may claim. It includes observed behavior, measured results, supported use cases, and approved limitations.

Definition

Product Truth

The verified product behavior and evidence that marketing copy may state or imply.

Copy quality and copy performance are different. A clear sentence can still address the wrong concern or make the reader doubt the claim. Performance requires a defined outcome and measured reader behavior.

Applying It in Practice​

Prepare a brief with:

  • one audience and the problem they recognize
  • one supported product claim
  • evidence for that claim
  • the page's place in the reader journey
  • the action the reader should take
  • voice examples and banned language
  • legal or compliance constraints

Ask for variants that test distinct message hypotheses. Five rewrites of the same claim provide less information than two variants built around different reader concerns.

Evaluate each draft against the brief:

CheckQuestion
TruthDoes every explicit and implied claim have evidence?
AudienceDoes the copy address a concern this reader has?
MessageIs the main point clear after one reading?
VoiceDoes it match approved examples without copying them?
ActionDoes the next step fit the page and reader stage?

Revise with concrete constraints. “Keep the measured deployment result, remove the unsupported cost claim, and shorten the heading to eight words” is testable feedback.

For an existing page, preserve proven structure unless the experiment is designed to test it. Changing the message, layout, and call to action at once makes results hard to interpret.

Measure variants through an agreed experiment or user-research method. A model cannot predict conversion from prose alone.

Engineering Considerations​

Do not ask the model to discover positioning from a feature list. Positioning requires customer evidence, competitive context, and a choice about which audience to serve.

Check implied claims as carefully as explicit ones. Words such as “automatic,” “secure,” and “instant” create expectations even when the copy gives no number.

Protect customer data and unreleased product details in prompts. Use approved tools and sanitize research material before sharing it with a model.

Human review is required for pricing, legal claims, regulated products, testimonials, and comparative statements.

Scaling and Operations​

Maintain briefs as versioned source material. Update them when product behavior, evidence, positioning, or voice changes. Do not reuse a campaign brief where the audience or desired action differs.

Limit generation to the team's evaluation and testing capacity. Unreviewed variants create inventory, not learning.

Record the hypothesis, approved copy, audience, dates, and outcome for each experiment. Feed measured results into later briefs without treating one result as a universal writing rule.

Keep a claim register for important numbers and comparative statements. Link each claim to its evidence, owner, and review date.

Next Steps​