10 Creative Ways to Write with AI (Without Losing Your Soul)

Thanks to AI, the content industry was derailed by people who flooded social media with promises to fire your marketing team, replace your agency, and let a magical black-box workflow handle all your content. Just plug in a keyword, hit a button, and watch the traffic roll in.

So, after years of hearing the same message, people started associating AI-generated content with low-effort, mass-produced slop. AI-assisted content earned a bad reputation before it had a chance to mature.

This article is my attempt to reset the conversation.

I’ll share how we use AI at Ahrefs to create content, along with some content experiments we’ve been running. Not to replace human thinking, but to make possible things that used to be too difficult, too expensive, or simply impossible.

My goal isn’t to convince you to automate more. It’s to help you see AI as a creative tool rather than a content factory.

And one more thing: I think you’ll actually enjoy most of these ideas. People often say AI makes creative work less fun. Used well, I think it can do the opposite.

Agent-To-Agent Marketing Was Just Born on Moltbook. I asked Letaido (AI marketing platform by Ahrefs) to get some data on Moltbook.com, gave it some notes from my manual research, and an arc of the story that I already had in my head, and asked it to combine everything together in an article.

Our Director of Content Marketing, Ryan Law, tried out this method and said: “it was the most fun I’ve had writing for Ahrefs in ages.” Check out his video:

Vibewriting also works with other types of content, like presentation decks. Here’s one I made for a webinar. You can check out the full interactive deck
here, and here’s the webinar where I used it.

Starting prompt

I want to vibewrite a blog post about [topic]. Here's my general idea for the article [describe the idea]. I've gathered these materials so far [attach anything you'd like the AI to use and reference] and here is the type of article I'm after [link]. Let's start with the abstract of the article and the outline.

Try with:

Newsletters
Opinion pieces
Essays
Short research pieces

https://github.com/mmakosiewicz/self-building-articles-app

Try with:

Research-heavy articles
Long-term writing projects
Topics you’re still exploring

search intent into the top 10. That’s easier to pull off with low-KD keywords, I know—but I’ll take it.

The only reason I could put this article together so quickly was that I’d already built the infrastructure behind it: a “source of truth” repository containing product documentation, Ahrefs how-tos, insights from our data studies, and other key resources.

Whenever I come across an important internal page, I add its URL to the app. It distills the key information and syncs it on GitHub, so later I can simply ask, “What do the SoTs say about this?” and instantly pull the relevant context into a draft.

Starting prompt

Search my documentation for everything related to [topic]. Pull together the most relevant information, identify recurring themes, remove overlap, and draft an article that builds on existing knowledge instead of inventing new content.

And if you want an SOT app like mine, show this link to your AI agent:
https://github.com/mmakosiewicz/sots_webinar

Try with:

Product explainers
Evergreen articles
Documentation
Guides and how-tos
Updating old content

Louise Linehan.

We built these with Letaido, which has been a huge unlock for working with Ahrefs data. Compared with a standard MCP setup, it gives us access to more data endpoints, can work autonomously, and comes with native integrations like WordPress, so we can publish content directly from the tool.

Letaido handled the heavy lifting: connecting to Ahrefs data, calling APIs for specialized databases, generating visualizations, and even helping write parts of the articles.

Si Quan from our content team even built a custom Letaido app to automate the process of updating data-driven articles like these.

Instead of rebuilding each article from scratch whenever the data changes, the app refreshes the numbers and generates an updated draft, making it much faster to keep our research current.

In this guide, he explains how he built it, walks through the full process, and shows how it sends an email notification when new data is ready to review—so you can follow the same approach yourself.

Starting prompt:

I'm attaching a dataset from our business. Don't write an article yet.
First, analyze the data like an investigative journalist or analyst. Look for:
- surprising patterns or outliers
- trends over time
- correlations worth exploring (don't assume causation)
- rankings and benchmarks
- anything that contradicts common assumptions
- questions the data raises
- findings that would make a strong headline Once you've analyzed it, propose 10 article ideas based on the most interesting discoveries. For each one, explain why it's interesting and what additional analysis (if any) would strengthen the story.

Try with:

Original research
SEO studies
Industry reports
Product insights
Data journalism
Example

OpenAI model solved a geometry problem that had stumped mathematicians for 80 years. The breakthrough was that it explored an approach humans had dismissed. Researchers spent decades trying to prove the accepted answer instead of following an unpromising path. The AI had no such bias or impatience, so it found what everyone else overlooked.

Brainstorming works the same way. Most people stop after their first few decent ideas—the same obvious ones everyone else has. AI keeps going.

You can literally ask AI for “100 ways to think about this,” then cluster the ideas or expand the best ones. It will surface angles you probably wouldn’t have considered. Your job is deciding which ones are worth pursuing.

Example

My colleague Si Quan told me about this method, and I’ve always been impressed by the titles and angles he comes up with. So I decided to try it with an idea that keeps coming back to me whenever I research AI SEO: brand is content.

It surfaced a few angles I’d already explored, which gave me confidence it was on the right track. But it also uncovered several ideas I’d never considered.

Here are some of the new perspectives I discovered thanks to this approach:

By the way, this method is a good example of how AI can augment your work, not only automate it.

Starting prompt

Give me 100 ways to think about [topic with a brief explanation of how you interpret it]. Cluster similar ideas.

Try with:

Brainstorming angles and topics should work with any type of content.
Could be a good technique for repurposing longer content pieces for social media short-form content.

mental model.

Example

This is another technique my colleague Si Quan introduced me to. I already knew you could ask AI to take on a role—like a data analyst, a lawyer, or a tough editor—but this approach felt more structured and controlled. So, let’s try it in Letaido using Opus 4.8.

The result was a detailed report with the entire reasoning process laid out in front of me. Two sections stood out in particular.

The first was where the AI challenged its own conclusions, questioned its assumptions, and worked its way toward what it considered the strongest explanation.

The second was seeing those insights make their way into the article itself. It wasn’t just reasoning for reasoning’s sake—the AI actually carried its conclusions through into the final draft.

I don’t know whether the AI genuinely reasoned its way through the problem or simply simulated the process. And it definitely didn’t produce something I could publish as is.

But that wasn’t the point.

It got me much further than a blank page would have, and it helped me organize my own thinking.

That’s incredibly valuable because good writing starts with good thinking—and thinking is still the hard part. It’s not something we can fully outsource to AI.

Starting prompt

Use the Theory of Constraints Logical Thinking Process to analyze
[topic]. First, build the appropriate logic tree for this type of
article. Identify the visible symptoms, root causes, assumptions, constraints, and likely effects of the proposed solution. Challenge weak causal links before writing. Once the tree is sound, turn it into a clear article with a strong argument.

Try with:

Opinion pieces
Product decision-making guides

Starting prompt

Before we start, here’s one tip for using AI to analyze data: don’t ask it to interpret data you haven’t looked at yourself. Instead of asking for the final answer right away, ask AI to show you the available data first and explain what it’s seeing.

AI can still hallucinate or take shortcuts, especially when analyzing large datasets. For example, we had around 7,500 Intercom conversations in a single month—far too much to analyze reliably in one pass.

Here’s a prompt to start that kind of analysis:

I want to identify gaps in our documentation, but don't generate recommendations yet.
First, analyze our customer conversations and show me the data.
Please:
- Group similar customer questions into themes.
- Count how often each theme appears.
- Include representative examples from real conversations.
- Show the exact wording customers use whenever possible.
- Flag any uncertainty or themes that may overlap. Do not suggest new articles yet. I want to review the grouped questions before we decide what to document.

After reviewing the output, you can follow up with:

Now compare these themes with our existing help center and documentation.
For each theme:
- Tell me whether it's already covered.
- Point to the existing article if one exists.
- Identify missing or outdated content.
- Rank the gaps by how often customers ask about them. Then suggest the top 10 documentation opportunities, explaining why each one deserves to exist.

A more reliable approach is to have AI monitor new conversations as they come in instead of asking it to dig through months of historical data all at once. Breaking the task into smaller, ongoing analyses is both easier for the AI and much less likely to produce misleading results.

From now on, monitor new customer conversations instead of analyzing the entire history every time.
Whenever new conversations are available:
- Group recurring questions into themes.
- Highlight any new topics that haven't appeared before.
- Track which questions are becoming more common.
- Compare new questions against our existing documentation.
- Alert me when a recurring question isn't answered by our help center. For every recommendation, include:
- How many conversations mention it.
- Example customer messages.
- Related documentation (if any).
- A suggested article title and a short outline. Never assume conclusions without showing the supporting conversation data first.