Most companies that publish technical content have the same problem: the people who write best about the product or service are the engineers and specialists. And the people who make buying decisions — executives, directors, owners — don't read content written for engineers.
The usual solution is to hire a content writer who translates. That works, but it's slow and expensive, and it means the content is always behind. The AI solution is faster, cheaper, and runs automatically.
Here's the prompt we use. We'll break down why each part matters.
Why "Rewrite This for Executives" Doesn't Work
The first version of every client's prompt looks something like this:
Article: {content}
The output is mediocre. It removes some jargon but doesn't change the structure. It still leads with the technical mechanism rather than the business outcome. It's still too long. It doesn't tell the executive what to do next.
The problem isn't the AI — it's the prompt. Vague instructions produce vague output. You need to be explicit about what an executive cares about, how they read, and what the output must contain.
The Prompt That Works
This audience:
- Reads the headline and first two sentences to decide if they continue
- Cares about business outcomes, not technical mechanisms
- Thinks in terms of time, money, risk, and competitive advantage
- Has 3 minutes, not 10
Rules for your rewrite:
- Open with the business outcome or problem — never the technical solution
- Replace all technical jargon with plain language (if you must use a technical term, define it in plain English in the same sentence)
- Quantify every benefit you can: "faster" → "50% faster", "saves time" → "saves 3 hours per week"
- Keep the article under 600 words
- Maintain complete factual accuracy — do not invent claims not supported by the source
- End with one specific, actionable next step
- Do not use the words "leverage", "synergy", "robust", "seamless", or "cutting-edge"
Source article:
{blog_post_content}
This prompt produces consistently usable output. Here's why each section earns its place.
Breaking Down Each Section
The audience description does more work than it looks like. By telling the AI that this audience "reads the headline and first two sentences to decide if they continue," you're shaping how the AI prioritises what goes first. Without this, Claude will often preserve the original article's structure — which is usually wrong for executives.
"Thinks in terms of time, money, risk, and competitive advantage" gives Claude a filter for every sentence. If a sentence doesn't connect to one of those four dimensions, the AI learns to cut or reframe it.
The quantification rule is the one clients notice most in the output. Vague benefit statements like "improves performance" get replaced with specific ones. The AI will estimate or flag where quantification isn't supported by the source — which also catches cases where the original article makes vague claims that should be backed up.
The banned words list seems minor but eliminates a whole category of corporate filler that AI models default to. "Robust" and "seamless" appear in roughly 40% of AI-generated content without this rule. Banning them forces more specific language.
A Before and After
Source technical paragraph:
"The new async processing architecture decouples the ingestion layer from the transformation pipeline using a message queue, enabling horizontal scaling of worker nodes without degrading throughput under peak load conditions."
After the prompt runs:
"We redesigned how data moves through the system so it can handle 5× more volume during your busiest periods — without slowing down or requiring additional manual oversight from your team."
Same fact. Completely different reader experience.
Running This Automatically
If you're running this manually in Claude.ai, you already have a significant time saving — 10 minutes of writing becomes 2 minutes of reviewing. But the real leverage is automating the trigger so it runs every time a new technical post is published.
In our blog-to-social pipeline, this prompt runs automatically via Make.com the moment a new post appears in the site's RSS feed. The rewritten article is published to a separate executive blog section on the site, and three social posts are generated from it — all without anyone opening a browser.
If you want to see how that automation is structured end-to-end, the case study is here: How We Built a Blog-to-Social Pipeline Using Claude AI.
Want This Running Automatically for Your Blog?
We set up the full pipeline — technical post in, executive version out, social posts scheduled — in about two weeks. Book a free discovery call to see if it's a fit.
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