sasha volkov

Prompt Engineering for Marketers: What Actually Works

october 23, 2025

I've written probably 5,000 prompts in the last year. Marketing prompts, specifically — ad copy, blog posts, email sequences, competitive analysis, customer research summaries, social media calendars, landing page variants. And I can tell you that about 90% of the "prompt engineering" advice floating around is either too generic to be useful or flat-out wrong.

So here's what I actually use. Not theory. Not "10 tips for better prompts." These are the specific techniques, with real examples, that I use every day to get consistently good output from Claude, GPT-4o, and Gemini for marketing work.

System Prompts for Brand Voice

The single highest-ROI prompt engineering technique for marketers is setting up a system prompt that captures your brand voice. Not just "write in a professional tone." An actual, detailed description of how your brand sounds.

Here's the system prompt I use for our fintech blog:

You are a content writer for a fintech company. Your audience is
CFOs, controllers, and finance directors at mid-market companies
(100-2,000 employees).

Voice guidelines:
- Conversational but authoritative. You know more than the reader,
  but you're not condescending about it.
- Use "you" and "your" frequently. Address the reader directly.
- Short paragraphs (2-3 sentences max). Break up walls of text.
- Use specific numbers and examples instead of vague claims.
  "Saves 12 hours per month" not "saves significant time."
- Avoid: "leverage," "synergy," "best-in-class," "cutting-edge,"
  "seamless," "robust." If a word appears in every enterprise
  marketing brochure, don't use it.
- Avoid: Starting sentences with "In today's" or "In the
  ever-evolving." Just start with the point.
- Tone is confident but not aggressive. We don't trash competitors
  by name. We just make our case clearly.
- Use contractions (it's, don't, we're). Formal writing sounds
  stiff for this audience.

This goes into every single prompt as the system message (if you're using the API) or as the first message in a conversation. The difference in output quality is dramatic. Without it, you get generic marketing content. With it, you get content that at least sounds like it could have come from your team.

The key details that matter most: specific words to avoid, the audience definition, and the paragraph length guideline. Those three things eliminate probably 80% of the "this sounds like AI" problem.

Chain-of-Thought for Strategy Work

When I need an LLM to help with strategic thinking — positioning, competitive analysis, campaign planning — I use chain-of-thought prompting. The idea is simple: instead of asking for the answer, you ask the model to show its reasoning step by step.

Here's a real example I used last month for a competitive positioning exercise:

I need to position our expense management product against Brex,
Ramp, and Navan for mid-market companies.

Before giving me positioning recommendations, work through these
steps:

Step 1: List the key buying criteria for a CFO choosing an expense
management tool. Rank them by importance.

Step 2: For each competitor (Brex, Ramp, Navan), assess their
strengths and weaknesses against each buying criterion.

Step 3: Identify the criteria where competitors are weakest and
where we could credibly claim superiority.

Step 4: Based on steps 1-3, recommend 2-3 positioning angles,
explaining why each one would work and what the risks are.

Show your reasoning for each step before moving to the next.

The output from this is dramatically better than asking "how should we position against Brex, Ramp, and Navan?" The model doesn't skip steps, doesn't jump to conclusions, and you can see where its reasoning is weak so you can correct it.

I use this technique for:

Basically, anytime you need thinking, not just writing.

Few-Shot Examples for Ad Copy

For short-form content — ads, subject lines, CTAs, headlines — few-shot prompting is the most reliable technique. You give the model examples of what good output looks like, then ask it to generate more in the same style.

Here's how I generate Google Ads headlines:

Write Google Ads headlines (max 30 characters) for our expense
management software. Target audience: CFOs at mid-market companies.

Here are examples of headlines that performed well (CTR > 4%):

Good examples:
- "Cut Expense Reports by 80%"
- "Your CFO Will Thank You"
- "Expenses on Autopilot"
- "Ditch the Spreadsheets"
- "$0 Setup. Cancel Anytime."

Here are examples that performed poorly (CTR < 1%):

Bad examples:
- "Best Expense Management"
- "Streamline Your Finances"
- "Enterprise-Grade Solution"
- "Award-Winning Platform"

Generate 15 new headlines following the patterns in the good
examples. Avoid the patterns in the bad examples. For each
headline, note which good example it's most similar to and why.

The negative examples are just as important as the positive ones. They tell the model what to avoid, which is often more useful than telling it what to do. "Enterprise-Grade Solution" is the kind of headline any LLM would generate without guidance. Showing it that this style performs poorly steers it away.

I keep a running document of high-performing and low-performing copy for every channel. This becomes my few-shot library. Every time I have new performance data, I update the examples. The prompts get better over time because the examples get better.

Temperature Settings: When They Actually Matter

Most prompt engineering guides mention temperature but don't give you practical guidance. Here's what I've found through actual testing across hundreds of generations:

Task Temperature Why
Ad copy variants 0.9 - 1.0 You want diversity. 10 similar headlines are useless.
Blog post drafts 0.6 - 0.7 Creative enough to be interesting, structured enough to be usable.
Email sequences 0.5 - 0.6 Needs consistency across emails. Too much variation breaks the narrative.
Data analysis / summaries 0.1 - 0.3 Accuracy matters more than creativity. You want the same answer every time.
SEO meta descriptions 0.4 - 0.5 Needs to be accurate and include keywords, but still engaging.
Social media posts 0.8 - 0.9 Voice and personality matter. Higher temp = more character.
Competitive analysis 0.2 - 0.3 Factual accuracy is critical. Creative interpretation is dangerous.

One nuance: temperature matters less with Claude than with GPT models, in my experience. Claude's output variance between temperature 0.5 and 0.9 is smaller than GPT-4o's. If you're using Claude, I'd focus less on temperature tuning and more on prompt structure.

Also, temperature only matters if you're using the API. The web interfaces for ChatGPT and Claude don't expose temperature controls (Gemini does in AI Studio). If you're doing serious volume, you should be on the API anyway — it's cheaper and more controllable.

Context Window Management

This is the technique nobody talks about, and it might be the most important one for long content.

Every LLM has a context window — the total amount of text it can "see" at once. Claude Sonnet handles 200K tokens. GPT-4o does 128K. Gemini 1.5 Pro does 2M. But here's what the marketing articles won't tell you: models get worse as you fill up the context window. The quality of output at 80% context utilization is measurably worse than at 20%.

For marketing work, this means:

Here's my rule of thumb for prompt length by task:

Ad copy / headlines:     200-500 tokens  (keep it tight)
Email sequences:         500-1,500 tokens (brief + examples)
Blog posts:              1,000-3,000 tokens (brief + voice guide)
Strategy / analysis:     2,000-5,000 tokens (context + framework)
Long-form content:       3,000-8,000 tokens (max before quality drops)

If your prompt is over 8,000 tokens, you're probably trying to do too much in one shot. Break it into stages.

The Prompt Patterns I Use Most

Here are five prompt patterns that cover about 80% of my daily marketing work:

1. The Rewrite Pattern

Here's a draft of [content type]. Rewrite it following these rules:

[Your specific rules]

Keep: [What's working in the original]
Change: [What needs to be different]
Tone: [Specific tone guidance]

Original:
[Paste original text]

I use this more than any other pattern. It's faster and produces better results than generating from scratch because the model has a concrete starting point.

2. The Variation Pattern

Here's a [headline/CTA/subject line] that works well:

"[Your proven copy]"

Generate 10 variations that:
- Keep the same core message
- Vary the structure and word choice
- Range from conservative (close to original) to experimental
  (very different)
- Are each under [character limit] characters

Number them 1-10 from most conservative to most experimental.

The "conservative to experimental" framing is key. Without it, you get 10 variations that are all basically the same.

3. The Persona Filter

Read this content from the perspective of [specific persona]:

[Persona description: role, company size, pain points, goals,
 technical sophistication]

Then answer:
1. What would make them stop reading?
2. What questions would they have after reading?
3. What's missing that they'd want to know?
4. Rate the relevance to their daily work (1-10) and explain why.

I use this as a quality check before publishing. It catches blind spots that come from being too close to your own content.

4. The Extraction Pattern

Here's a [long document / transcript / report]:

[Paste content]

Extract:
- The 3 most surprising or counterintuitive findings
- Any specific numbers, percentages, or data points
- Direct quotes that would work in marketing content
- Potential blog post angles based on this content

Format as a structured brief I can hand to a content writer.

This is how I turn webinar transcripts, research reports, and customer call notes into content ideas. It replaces what used to be an hour of manual note-taking.

5. The A/B Framework

I need to write [content type] for [context].

Give me two versions:

Version A: [Approach 1 - e.g., "leads with the problem"]
Version B: [Approach 2 - e.g., "leads with the solution"]

For each version, explain:
- Why this approach might work better
- What audience segment it's optimized for
- The risk / potential downside

Then recommend which one to test first and why.

This forces the model to think in terms of tradeoffs rather than just giving you one "best" answer. Marketing is full of legitimate disagreements about approach, and this pattern surfaces them.

Common Mistakes I See Marketers Making

After helping a dozen marketing teams set up their AI workflows, here are the patterns that consistently produce bad output:

The uncomfortable truth: Prompt engineering for marketing isn't a skill you learn once. The models change, the best practices evolve, and what worked three months ago might not work today. The real skill is building a system for testing and iterating on your prompts — a prompt library, performance tracking, and a feedback loop. The specific techniques in this post will get you 80% of the way there. The last 20% is your own experimentation.

I maintain a personal library of about 60 prompts that I use regularly. I update them every month based on what's working and what isn't. If there's interest, I might share the whole library in a future post. But honestly, building your own is more valuable than copying mine — because the best prompts are the ones tuned to your specific brand, audience, and workflow.