Every marketing team that uses AI writes rules for it. Some live in a prompt. Some live in a brand guide. Some live only in an editor’s head. Together, they decide whether AI content sounds like your company or like everyone else’s.
This topic matters because most of those rules were written for people. “Keep it helpful.” “Don’t be too salesy.” “Mention the product, but tastefully.” A skilled human writer can follow those. An AI can’t do it reliably, and as AI writes more of your content, that gap shows up in every asset.
If you lead content or product marketing, this is where quality is won or lost. The model matters. Your rules matter more.
Short answer: AI content guidelines work best when each rule is specific enough to check. Vague guidance like “don’t be salesy” depends on judgment that AI applies inconsistently. Clear rules, such as “never name our product in awareness content,” can be followed and verified across hundreds of assets. Leave the judgment calls to human editors.
Why your AI content guidelines need to be specific enough to check.
Why can’t AI follow “it’s all in the tone”?
Every experienced marketer knows you can mention a product without selling it. A skilled writer can reference a tool, keep the focus on the reader, and never tip into a pitch.
That skill comes from years of reading pitches and learning what one sounds like. A language model doesn’t carry that judgment in the same way. Ask it to “mention the tool tastefully,” and you’ll get a different answer on every run. Some drafts will be fine. Some will read like a brochure. Across hundreds of assets, the brochure versions add up.
Newer models handle nuance better, but this gap hasn’t closed. We saw it ourselves while building stage-specific content at VelocityEngine. When tone was the only guardrail, product mentions crept back into content that was supposed to stay educational.
Why AI has made vague rules more expensive
When one writer produced ten posts a month, an editor could catch every slip. AI changed the math. Teams now produce far more content, and editors can’t read every line closely.
The result is easy to spot. Wikipedia’s volunteer editors maintain a public field guide, Signs of AI writing, that catalogs the habits AI-generated text leaves behind: puffed-up importance, promotional language, and stock phrases that say little. Those habits thrive where rules are vague, because vague rules leave the AI to fill in the gaps with its defaults.
That’s the content we’re on a mission to eliminate at VelocityEngine: AI output that sounds polished and says nothing. Clear rules are one of the most direct ways to get there.
You can’t check what you can’t define
There’s a second problem. At scale, you need to verify that content follows your rules. In AI product teams, those checks are called evals.
AI engineer Hamel Husain makes the case in Your AI Product Needs Evals: without systematic checks, teams can’t tell whether changes make output better or worse. He and Shreya Shankar expand on it in Lenny’s Newsletter, arguing that good evals start from how a product actually fails.
The same logic applies to marketing content. An automated check can confirm that an awareness blog never names your product. It can’t reliably score whether a mention felt “tasteful.” If a rule can’t be checked, you can’t enforce it across a content program.
How do you write AI content guidelines that work?
Turn judgment calls into clear constraints wherever you can.
| Vague rule | Checkable rule |
|---|---|
| Don’t be too salesy | Never name our product in awareness content |
| Keep it helpful | Include at least two non-software approaches |
| Match our voice | Write numbers under ten as words |
| Make the intro strong | Open with a specific situation, not a definition |
| Stay on message | Use the brief’s key phrase at least once |
Then test each rule the way an engineer would:
- Ask whether a script could check it. If not, rewrite it or assign it to a human reviewer.
- Look at real failures first. Write rules for the mistakes your content actually makes, not every mistake it could make.
- Watch for side effects. A rule that blocks one problem can create another. Too many restrictions can make AI writing vague.
- Keep rules in one place. Guidelines scattered across prompts, docs, and Slack threads drift apart.
This isn’t about removing nuance. It’s about putting your hard lines where a machine can verify them, so human editors can spend their time on the work only they can do.
Where human judgment still wins
Clear rules get you consistent, on-strategy drafts. A human editor still adds the polish: the right example, the turn of phrase, the instinct for what a specific audience needs.
The goal isn’t to replace taste. It’s to stop depending on taste in places where AI can’t supply it.
How VelocityEngine approaches this
We treat content rules as part of the system, not as a style memo.
- Rules live in one Foundation. Messaging, personas, positioning, and brand voice sit in one operational Foundation that every campaign inherits.
- Stage rules are explicit. Awareness content, for example, follows a hard rule: no product names. We explain why in What awareness stage content should and shouldn’t say.
- Human review is built in. Content creation includes review steps, so editors focus on judgment, not on catching rule violations.
As we argue in Is Your Messaging Doc a Suggestion or a System?, guidelines only work when they’re built into how content gets made.
Frequently asked questions
What are AI content guidelines?
AI content guidelines are the rules an AI follows when it writes for your brand. They cover voice, messaging, structure, and what each piece may or may not say. Effective guidelines are specific enough to check automatically.
Why doesn’t “don’t be salesy” work as an AI instruction?
It depends on judgment that AI applies inconsistently. One draft may follow it well and the next may read like an ad. A specific rule, like “never name the product in awareness content,” produces consistent results.
What is an eval for marketing content?
An eval is a repeatable check on AI output. For marketing, it might confirm that a blog avoids product names, uses approved terms, or follows formatting rules. Evals let teams verify quality across hundreds of assets.
Do clear AI rules replace human editors?
No. Clear rules handle consistency. Editors handle judgment, such as choosing the best example or sharpening an argument.
The takeaway
Audit your AI writing instructions. For every rule, ask one question: could a script check this? If not, rewrite it until it could, or hand that rule to a human reviewer.
Vague rules produce generic content, and the web already has plenty of that. Clear rules give your AI a fighting chance to sound like you.
See which stages your content covers today with a free Content Coverage Heatmap.
