This content originally appeared on DEV Community and was authored by Максим
You asked AI for help, and it answered with lukewarm mush. Yosu said, “Build me a website,” and it gave you a vague outline that sounded like every generic blog on the internet. Then you tried again, this time with a bigger ask, and it folded. If that feels familiar, you’re not alone.
Most marketers don’t have an AI problem. They have a communication and process problem with AI. The upside? Once you fix how you choose models, frame tasks, and orchestrate prompts, you’ll see speed, quality, and throughput jump together—often on the very next project.
This is a senior-level field guide to AI-powered marketing that you can actually use. No fluff, no “just be creative” platitudes. You’ll get frameworks, prompts, and a way to ship real work—consistently.
Why must you use AI in marketing now?
Competitive advantage: Not everyone knows how to use AI effectively. Many still don’t. If you learn to use it well, you’re already ahead.
Speed and quality together: Done right, AI increases output speed without sacrificing quality—and often improves it.
Better economics: More throughput with smaller budgets.
Survival: If you don’t use AI the right way, you lose competition, time, quality, and money. That’s not theory; it’s already visible across markets.
Think of it as switching from walking to driving. You’ll get to the same destination—but faster, less tired, and more consistently.
Which AI model should you choose—and how do you keep up as new ones launch?
You’ll never pick a “forever model.” Don’t try. Pick for the job at hand, and maintain a short list of “core models” you consult for recommendations and research.
Core models to keep in your rotation:
- ChatGPT (OpenAI)
- Claude (Anthropic)
- Gemini (Google)
- Grok (xAI)
- Deepseek
- Copilot (Microsoft)
How to stay current without drowning:
Ask the models themselves: “Recommend the best text-to-video tools right now. Include options released in the last 30 days.” Ask two or three models and compare their answers. Different models will surface different strengths.
Check leaderboards occasionally: Sites like Livebench.ai give you a directional sense of progress across reasoning, coding, math, etc. Treat scores as signals, not scripture.
Stay flexible: If a model surpasses your current favorite, switch. Loyalty is expensive.
Pro tip: Evaluate by task. A model that shines in research might be middling in copy nuance; another might excel at long-chain reasoning. Let the job define the tool.
How do you learn any tool using AI—especially when interfaces keep moving?
Stop hunting YouTube tutorials for “where did they move the button.” Ask AI to teach you, step by step, in your context.
Use straightforward teaching prompts:
“Teach me how to use the latest version of [tool] with actionable steps. Use numbered steps and sub-steps.”
“There’s a feature that opens a new panel on the right. What’s it called and how do I enable it?”
“Facebook Ads changed again. Provide the current steps to create a custom audience. Keep it simple and actionable.”
AI is a patient instructor that never gets tired of repeating itself. Use it.
How do you talk to AI so it delivers senior-level work?
Three rules separate amateurs from pros:
Don’t ask too much at once
Keep requests small and precise. “Create a homepage headline for my agency” beats “Build me a website.” Break big projects into small tasks; you’ll get higher-quality answers and fewer hallucinations.AI is forgetful—remind it
Restate core context as threads get long. Re-anchor with the goal, constraints, audience, tone, and format. The longer the thread, the more you should remind.Provide examples
Examples unlock quality. If you want a specific tone, cadence, or layout, show it. Examples reduce back-and-forth and produce consistent outputs, even when your instructions aren’t perfect.
The Context Formula: Who / What / Why / Where / How
When you’re asking AI to produce marketing assets, your context quality is your output quality. Use this simple checklist in your prompt.
Who: Target audience details
- Demographics, roles, interests, pain points, goals
What: Deliverable elements
- Asset type and required sections; callouts; constraints; character limits; CTAs
Why: Purpose and goal
- What outcome should this asset drive? What action should the reader take?
Where: Platform or channel
- Channel norms matter (LinkedIn vs Instagram vs email)
How: Tone, style, format preferences
- Emotional vs rational, concise vs narrative, bullet-heavy vs flowing, formal vs conversational
Example of adding context that actually helps:
Weak: “Create Facebook ad copy for our AI marketing course targeting digital marketing managers.”
Strong: “Create Facebook ad copy for our AI marketing course targeting digital marketing managers who feel overwhelmed by new technology. Emphasize time-saving benefits, include a clear CTA, keep the tone professional but conversational, and cap copy at 125 characters.”
Output Template vs Output Format: the two levers pros use
They sound similar. They are not.
Output template = the internal structure of the response
You tell AI what sections to produce and in what order.
Example:
“Create a 5-day launch email sequence. Use this template for each email:
- Subject line
- Preview text
- Headline
- Body
- Pain point addressed
- Benefits
- CTA”
Output format = the file or representation you want
You specify the delivery shape so it integrates cleanly into your workflow.
Useful formats in marketing work:
- CSV for schedules, matrices, and imports
- HTML for email templates
- JSON for structured handoff to devs
- Markdown for content publishing
- Tables and charts for analysis and decks
- Plain text when you just need to copy/paste quickly
The same request, different formats:
- “Create a 7-day Instagram content schedule about AI digital marketing in CSV.”
- “Now present the same plan as a table.”
- “Now visualize posting frequency by topic as a chart.”
Amateurs focus on “what” they want; professionals specify “how” they want it delivered. That one shift saves hours.
Can “Act As” do more than a gimmick?
Yes—if you use it for role-play and feedback loops, not as a magic title.
Two high-yield uses:
Expert simulation: “Act as an SEO specialist. Given these five keywords and this page, recommend the primary keyword and outline on-page improvements.”
Role-play that trains you or your team: “Act as the interviewer for a social media marketing role. Ask me questions. After each answer, critique it, point out weaknesses, and tell me how to improve before asking the next question.”
You can flip roles too:
- “I’m hiring. Act as three candidates—bad, average, and great—and answer my interview questions in three variants so I can calibrate my scoring.”
What is the ABA framework—and why does it rescue important projects?
ABA stands for Ask Before Answer. It compensates for the context you forget to provide.
How to use it:
In your prompt, instruct the model: “Before answering, ask me any clarifying questions you need to deliver a high-quality result.”
Answer those questions.
Then let it produce the output. You’ll avoid rework and protect high-stakes deliverables.
Use ABA anytime the cost of a wrong answer is high (ads, pricing pages, complex email sequences, executive comms).
The 90-year-old Grandma Test: ruthless simplicity that converts
Marketing is direct communication. If your message burns too much mental energy, the brain skips it. We’re wired to conserve energy; complicated messaging is friction. Simple messages get read, understood, remembered, and acted on.
How to apply the test:
Paste your current message into AI and say: “Simplify this so both a 10-year-old child and a 90-year-old grandma would understand it instantly.”
Iterate: “Keep this sentence; simplify that one; remove this term; make the CTA crystal clear.”
Before/after illustrates the gap:Before: “Our proprietary AI-driven marketing automation platform leverages advanced algorithms to optimize cross-channel campaign performance through predictive analytics and data-driven insights.”
After: “Our tool uses AI to show you what’s working and what isn’t, so you can focus on what grows your business.”
Exampling: the fastest way to consistent results
“Exampling” means showing AI exactly what you want—besides describing it.
Why it works:
AI misinterprets vague instructions. Examples create patterns to imitate.
Examples can reduce revisions by as much as 80%.
You get consistency across a project (e.g., a 20-slide deck or a 5-email sequence).
Where to source examples:
- Your top-performing emails
- Screenshots of competitor landing pages you admire
- Snippets that reflect your brand voice
- Past ads with strong CTR or conversion
- Well-structured posts or carousels you want to emulate
Prompt pattern with exampling:
“Write email copy for our AI marketing course launch. Here are two examples of our past successful launch emails. Follow a similar structure and tone.”
Micro-Stepping: shipping big outcomes in tiny steps
Never ask AI to complete massive tasks in one go. It can’t. You’ll get generic or broken work. Micro-step instead.
The micro-stepping framework:
Plan first
“Create a detailed plan with all required steps to [goal]. Include order of operations, dependencies, and deliverables per step.”Do one step at a time
Focus attention and context. Don’t move on until the step is complete.Document each step
You’ll hit chat limits and context windows. Save plans and key outputs as a PDF or doc so you can pick up in a fresh chat: “We completed through step 10. Here’s the plan. Let’s continue with step 11.”Troubleshoot in-system
When errors arise (they will), explain exactly what failed and paste logs or screenshots. Ask for root cause and fixes.Assemble the final product
Connect the completed parts into your campaign or asset.
A real-world application of micro-stepping
A small team built a production-level iOS app—with no prior Swift knowledge—by breaking the work into 200+ micro-steps and completing them one by one with AI’s guidance. They started with a plan (folders, files, authentication), implemented features in sequence (profiles, signup, email verification), and iterated when issues surfaced (moving from local storage to an external database so user data persisted after reinstall). The key was relentless micro-stepping, plus brainstorming with AI whenever they hit a wall: “Propose alternative architectures,” “Suggest an out-of-the-box approach,” “What would you do if you had to avoid [constraint]?”
This approach translates directly to marketing:
Complex funnels that combine ads, landing pages, email nurture, and retargeting
Multi-platform content launches
Analytics overhauls across Google Analytics, ad platforms, and dashboards
Ask AI for the plan. Then do the work step-by-step.
Practical examples you can use today
Context-rich ad prompt
“Create Facebook ad copy for our AI marketing course targeting digital marketing managers who feel overwhelmed by new technology. Emphasize time-saving benefits, include a clear CTA, keep the tone professional but conversational, and cap copy at 125 characters.”
Output template for emails
“Create a 5-email launch sequence for our private piano classes starting Monday. Use this template for each email:
- Subject line
- Preview text
- Headline
- Body
- Pain point addressed
- Benefits
- CTA”
Output format for schedules
“Generate a 7-day Instagram post schedule about AI digital marketing in CSV format with columns: date, topic, caption, hashtags.”
Role-play with feedback
“Act as the interviewer for a social media marketing position. Ask me one question at a time. After each answer, critique it, point out what was missing, and show me a stronger version before asking the next question.”
ABA for high-stakes assets
“I need a 3-email reactivation sequence for dormant customers. Before answering, ask me any clarifying questions you need to deliver a high-quality result.”
Exampling for consistency
“Write a launch email for our AI marketing course. Here are two of our best-performing emails—follow their structure and tone.”
Micro-step a website lead-capture upgrade
“Create a detailed plan with all steps required to add a professional lead capture form to my WordPress site, integrate it with MailerLite, and set up automated follow-up sequences. Include plugin recommendations, data fields, double opt-in, and GDPR considerations. Then we’ll start with Step 1.”
Beginner checklist: your first AI-powered marketing workflow
Use this as a literal checklist. It’s how you get momentum without chaos.
Choose your core model for today’s task
- Start with ChatGPT, Claude, or Gemini; cross-check with a second model for tool recommendations or edge cases.
Define the outcome
- What asset are you producing? What does success look like? Who is it for?
Use the Context Formula in your first prompt
- Who, What, Why, Where, How. Write them out explicitly.
Add ABA to protect quality
- “Before answering, ask clarifying questions if needed.”
Decide on output template and output format
- Template = sections and order; Format = CSV/HTML/JSON/Markdown/table/chart/plain text.
Bring examples
- Attach two to three examples (your own or external) that show structure and tone.
Micro-step the project
- Ask AI to generate a detailed step-by-step plan.
- Tackle one step per exchange.
- Save the plan as a PDF for continuity across chats.
Expect forgetfulness
- Re-state the goal, audience, and constraints every few turns as threads get long.
Simplify messaging with the Grandma Test
- Run crucial copy through simplification until it’s obvious and memorable.
Iterate deliberately
- Provide feedback: “Keep A and C, rewrite B; make the CTA more direct; reduce jargon in paragraph 2.”
Visualize and structure where possible
- Ask for tables or charts when analyzing schedules, budgets, or performance.
Role-play tough scenarios
- Interview practice, skeptical-customer handling, stakeholder buy-in—simulate it before the real moment.
Benchmark occasionally
- Glance at leaderboards (e.g., Livebench.ai) to see if a new model fits today’s task better.
Combine strategies as needed
- Example: “Act as an expert email marketer. I need a 5-part sequence for our AI course launch. Here are three of our best emails. First, create a micro-stepping plan. Then help me execute each step individually, starting with audience segmentation.”
Troubleshooting mindset: innovate your way out
AI will sometimes produce errors or take you down the wrong path. When it does:
Provide precise feedback: “Section 2 ignores our audience’s pain point,” “We’re over the character limit,” “This JSON doesn’t validate—here’s the error.”
Brainstorm alternatives: “Suggest three different approaches,” “Propose a non-obvious workaround,” “If we couldn’t use [tool], what else would you do?”
Break it smaller: If a step keeps failing, split it in two. Narrow the scope until it succeeds.
Final Thoughts
The playbook isn’t “use AI because it’s trendy.” It’s use AI because it lets you ship better work faster—if you communicate precisely and structure your projects intelligently. Pick models by task, not brand loyalty. Feed context like a pro. Ask before answer. Show, don’t just tell. Micro-step everything.
The marketers who master these strategies today will enjoy an unfair advantage tomorrow.
You don’t need to “be more creative.” You need to be more specific. What’s the one marketing project you’ve been deferring because it feels big? Write the plan with AI. Then micro-step the first two tasks today.
This content originally appeared on DEV Community and was authored by Максим