If you publish writing, you’d be crazy not to use artificial intelligence.

It’s like telling a carpenter not to use a drill. You can build a deck without one. But why would you?

Writers have always embraced new tools to improve their craft.

The challenge with AI, or any technology, is that we want the easy way out.

We hope the tech will magically automate everything. And for mediocre content, AI is the perfect solution.

But creating exceptional content is HARD. No matter what tool you use.

For example, I used AI extensively to write this article.

Yet, it still took me 40+ hours to produce.

Why?

AI has made me realize how much I can improve my content. And you can, too.

Below are seven timeless writing practices supported by AI.

Let’s start with planning your writing project.

1. Use AI to Define Your Audience

Without a deep understanding of your audience, even brilliant insights can fall flat.

AI makes reader research way easier.

You can analyze thousands of real conversations in minutes. No need to spend weeks on interviews or surveys.

A Faster Way to Do Audience Research

Using this article as an example, I wanted to understand how people felt about using AI for writing.

The best place for unfiltered thoughts? Reddit.

So, I Googled “reddit using ai to write good content” and found dozens of threads.

I gathered a handful of community discussions and exported them as PDFs.

Then I gave Claude the following prompt:

The result was eye-opening.

The analysis revealed patterns I hadn’t considered.

It also included quotes that captured the audience’s raw emotions and language.

This helped me ground my writing in real experiences.

You don’t have to follow my exact process. For instance, Kate Starr, our senior writer, uses various sources for audience research:

The key is to use AI to:

1. Process large amounts of audience data quickly
2. Identify patterns you might miss manually
3. Connect insights to real human experiences through quotes

2. Use AI to Find Your Unique Angle

It’s easy to get inspired by popular content in your field and remix those ideas.

That’s exactly what AI does by default, too.

A better approach?

Find perspectives that other content and most LLMs overlook. This makes your writing fresh and engaging.

Here’s how to do it in three simple steps.

Step 1: Gather Your Source Material

Find the top 3-5 posts for your topic.

You can do this for social media posts, articles, and emails (if you have access).

For instance, I found three posts on AI writing and copied them to a Google Doc before exporting the text file.

Step 2: Get AI to Summarize the Data and Suggest Ideas

Take your source materials from Step 1 and ask AI to create a comprehensive analysis.

Here’s a prompt:

Here’s the output for using AI in writing:

After analyzing, ask the AI for new ideas based on the opportunities it found.

Here’s what Claude came back with for this article:

Step 3 – Analyze and Refine Your Angle

Sometimes, the angles are usable with the first output.

But often, it’s a dance between you and the robot to find something compelling.

Here’s how this process shaped this article:

While plenty of tool comparisons and basic workflows existed, few posts offered strategic frameworks for using AI effectively.

As I dug deeper, I noticed something more fundamental.

Everyone was focused on making writing easier. Tools, prompts, shortcuts.

But they were missing the tough reality:

Creating top-notch content is difficult. With AI or not.

That became my angle: How to use AI thoughtfully while embracing the necessary work of writing great content.

3. Use AI to Create An Outline

Nearly every exceptional piece of content starts with a detailed plan. But many writers skip this step.

You risk wasting hours writing in circles or missing the brief entirely.

That’s why our team never writes a draft without a thorough outline.

With AI, creating that outline just got a lot easier.

Strengthen Your Draft Outline Through Collaboration

It’s tempting to ask AI to create your entire outline (losing your unique perspective). Or ignore AI completely (missing valuable insights).

A better approach?

Treat AI as a thoughtful discussion partner.

Our senior editor, Chris Shirlow, emphasizes using AI strategically during outlining:

The key is to start with your own research. This ensures the foundation reflects real expertise. Then, use AI to refine and expand your thinking.

With this article, for example, I started with some loose structural notes:

1. Demand
2. Audience
3. Angle
4. Structure
5. Research
6. Writing
7. Visuals
8. Enhancements
9. Editing

Then, I prompted Claude with some attached reference material.

Claude’s analysis (I’ve summarized the output):

From the AI’s first output, we started our collaboration.

Claude helped me name each element “practices” instead of “tips.

We challenged each other on the substructure of each practice.

Then it suggested H2s—many of which I didn’t like.

Through a LONG chat and many pauses of me returning to my document, we landed on an outline.

Here’s how the structure evolved through discussion:

This iterative process helped me:

1. Make headings more action-oriented
2. Create a consistent pattern (“Use AI to…”), which also helps for search engine optimization

Let’s look at how AI can help you turn this outline into content through research.

4. Use AI to Research Your Topic

Research has always been the foundation of excellent writing.

Pre-internet, research meant hours in libraries digging through catalogs, journals, and textbooks.

Then, digital changed everything. But it created a new problem:

Information overload.

Now you have instant access to millions of sources.

Blog posts. Academic papers. Community discussions. Expert interviews. Analytics data.

But do you have time to process it all?

That’s where AI shines.

It can scan thousands of sources in seconds, helping you catch insights you might miss.

Let’s explore how to use it effectively.

Using AI for Primary Research

When you do interviews, gather raw data, or run surveys, you create unique content that’s hard to copy.

The trick is to use AI throughout the research process—not just for analysis.

Planning Better Research Questions

The quality of your research depends on asking the right questions.

For example, when writing this article, I initially planned to use only my own AI experiences as examples. But something felt missing.

So, I started a discussion with Claude:

With Claude’s validation, I developed survey questions through an iterative process.

If you want to try this approach, here’s a template to get started:

Then, the research questions:

Finally, test your questions:

Example:

After several rounds with AI, my original idea of “let’s survey the team” changed to:

• A focused survey using conditional logic
• Clear examples of what I needed
• A friendly, collaborative introduction
• Specific prompts for AI usage

The result? Rich insights from the team that enhanced this article.

Getting More from Expert Interviews

Want to fully engage with your interview subjects while capturing all the details? AI can help.

Start by recording your conversations (with permission, of course). Have a real dialogue. Follow interesting threads. Then, let AI help you extract every valuable insight.

Here are some simple prompts:

Prepare your interview.

Process the recording.

Validate insights.

Making Sense of Raw Data

The challenge of research isn’t gathering data—it’s finding the story in it.

When our editorial team finished the AI usage survey, I faced this exact situation.

I wanted to process the responses quickly, but also to capture every valuable insight.

Here’s how AI helped me analyze the responses:

Get a high-level overview.

Drill down to the specifics.

Extract supporting material.

This analysis revealed that our team uses AI differently for each practice. Some excel at research, others at editing.

For instance, everyone stressed the need to use AI carefully. And not fully depend on it.

Using AI for Secondary Research

Secondary research meant spending hours reading papers, reports, and discussions.

Not anymore.

AI reshapes how we process existing content.

Let’s look at some use cases.

Extracting Audio and Video Content for Gems

Some of the best insights are buried in hour-long podcasts and conference talks.

Founders share behind-the-scenes stories. Experts reveal their frameworks. And industry veterans discuss trends they haven’t written about yet.

But watching hours of video isn’t always practical.

AI can save time here.

Here’s how I used AI to extract powerful insights from founder interviews for my ecommerce growth strategies article:

First, I found a podcast where Who Is Elijah’s founders shared their journey to $20M in revenue.

Then, I used Rev AI to transcribe the full interview.

Instead of reading through 19,000 words of transcript, I had Claude analyze the conversation with this prompt:

The analysis revealed a fascinating story about operational efficiency:

• They cut their team from 44 to 21 people
• Shifted from full-time specialists to agency partnerships
• Rebuilt their systems from scratch
• Turned unprofitable (-60%) campaigns into winners

This single podcast gave me both a compelling case study and practical lessons readers could apply.

Synthesizing Complex Documents

Academic papers and industry reports contain valuable data. But they’re often dense, jargony, and hard to apply practically.

Shreelekha Singh, our senior writer, uses detailed context to get better research results from AI.

Another example:

When writing an article about information gain, I needed to wrap my head around Google’s patent application.

But it’s written in technical language that would make your eyes glaze over.

Instead of getting overwhelmed, I used AI to help me interpret this complex material.

I uploaded the patent application to Claude and asked about information gain signals.

Claude helped identify and explain relevant metrics like “UserActionSignals” and “ClickSatisfaction” in plain language.

I quickly learned Google’s process for evaluating and testing new information.

The same approach works for:

• Academic papers and studies
• Technical documentation
• Industry reports
• Legal documents
• Research data

The takeaway?

Think of AI as your study partner. One that can read a 100-page document in seconds and explain the key points in plain English.

5. Use AI to Write Engaging Content

LLMs generate pretty good output with minimal prompting.

But producing engaging writing in your authentic voice? That’s where AI can be rather underwhelming.

I’ve been trying to write with AI since 2021, and I’m convinced the models have a default writing style.

AI LOVES writing in contrasting pairs: “Not this. But that.”

It also enjoys phrases like “transform,” “game-changing,” “leverage,” and “optimize.” (Not that there’s anything wrong with these words.)

And if AI could write your entire project in a list, it would.

If you’re often dissatisfied with the output, let me show you how to get better results.

Create Excellent Reference Materials

The more specific context you can give AI, the better the output matches your style.

This means defining your writing style clearly.

How?

Create detailed guidelines, including:

• Reader personas
• Target grade level
• Headline formulas
• Tonality
• Examples
• Opening hooks

In addition to your guidelines, make it your mission to create the perfect article or chapter to use as a writing sample.

Once you have your guidelines and examples, you’ll be more satisfied with the AI output.

For example, I’ve created a dedicated project in Claude for Backlinko. It has over 20k words of reference materials.

Every time I start a new conversation, Claude has this context readily available.

There’s no need to explain our style requirements over and over.

Build Progressive Context

Your conversation with AI should evolve as your content develops.

Take this article section as an example. I started a dedicated chat on “Using AI for Writing.” I shared:

• The outline
• The article draft so far
• Team survey responses
• My goals for this section

When I write the next section about supporting elements, I’ll start a new chat. But I’ll include this completed section as reference material.

This progressive approach helps AI maintain consistency while adapting to each section’s unique needs.

Shreelekha uses a similar method.

Depending on your LLM, this sectional approach will help manage your daily credits as long chats burn through your usage.

Embrace Messy Collaboration with AI

The best AI writing output happens through conversation.

Share your half-formed ideas. Question its suggestions. Challenge it to think deeper.

For instance, when writing this section, I asked AI to expand on my outline. But I didn’t just accept the first response.

Instead of settling for general advice about “prompting for a specific tone,” I asked for concrete examples of how AI’s default writing differs from Backlinko’s style.

This led to identifying specific phrases and patterns.

For instance, here’s how my opening hook evolved through AI collaboration:

You might go sentence for sentence, idea for idea, until you strike gold.

It can be tedious, but it’s better than doing it alone.

Find Perfect Examples (When You Need Them)

LLMs excel at suggesting relevant examples and case studies to strengthen your writing.

Shreelekha uses AI to brainstorm examples when she’s stuck:

Chris Shirlow emphasizes this balanced approach:

6. Use AI to Create Content Assets

Content assets like checklists, calculators, and infographics turn your writing into practical tools for readers.

The right asset can clarify complex concepts, aid learning, or guide important decisions.

Creating these resources once required designers and developers.

AI makes it possible to create without these skills.

Create Visual Assets

Many readers don’t consume every word you write.

They scan. They skim. They look for visual anchors to guide them through your ideas.

A study by MIT found that the human brain can process images in as little as 13 milliseconds. That’s up to 600 times faster than text.

But creating professional graphics used to mean:

• Learning design software
• Understanding design principles
• Spending hours on each visual
• Hiring expensive designers

Not anymore.

AI can help you create compelling visuals in seconds.

For example, in our 4 P’s of Marketing article, our senior writer, Yongi Barnard, used a graphic to explain why personalization matters.

The visual tells a compelling story at a glance.

To recreate this, gather your data.

Then, give AI parameters:

Then, you just need to refine the finishing touches (colors, spacing, etc.).

Our senior editor, Chris Hanna, puts it well:

Create Smart Checklists

Converting processes into checklists makes your content more actionable.

But creating an effective checklist isn’t as simple as writing bullet points. You need to:

• Break down complex processes
• Put steps in the optimal order
• Include validation checks
• Add resource links
• Consider different user scenarios

This is where AI can help.

The key is to prompt AI after you’ve written your draft.

This way, the LLM will have full context for your content and can create more detailed, relevant checklists.

For example, our senior editor, Shannon Willoby, made a 12-month checklist to help with her article on starting a blog.

She prompted AI to create the checklist based on her article content. Pretty simple but effective.

Here’s a template to get started:

Build Interactive Tools

Interactive tools like calculators, analyzers, and decision trees turn your knowledge into useful solutions. Readers can use these tools right away.

There are many opportunities, regardless of your industry:

Say you write about productivity. You could create a workload capacity analyzer that helps readers balance their projects.

If you’re a wellness writer, you might develop a habit-stacking planner to help people create healthy routines.

Or, if you’re a gardening expert, you could create a seasonal planting calculator.

For my ecommerce growth strategies article, I used AI to build an interactive profitability calculator.

Instead of explaining formulas, readers can explore different scenarios to understand how variables like cost of goods sold (COGS), shipping, and marketing expenses impact their bottom line.

The best part?

You can bring these AI-designed tools to life using no-code platforms like Calculator Studio. Here’s how:

1. Identify calculation needs in your content
2. Ask AI to help structure the logic and formulas
3. Design the user interface (AI can mock this up)
4. Build it in your no-code tool of choice

For instance, when building the profitability calculator, I prompted AI with:

Start simple.

A basic calculator that solves one specific problem well is better than a complex tool that confuses users.

7. Use AI to Edit Your Draft

Editing is the difference between good content and exceptional content.

But getting quality edits can be expensive and slow. You either:

• Pay editors by the hour
• Lose billable time as a freelancer
• Wait through lengthy review cycles
• Miss issues when editing your own work

AI changes this dynamic.

You can get quick, unbiased feedback and try different versions before your editor reviews a draft.

Let me show you how to do this effectively.

Get Strategic Input First

It’s tempting to jump right into line editing—fixing grammar and polishing sentences.

But start with the big picture.

Here’s how Chris Hanna uses AI for strategic editing:

AI can help by:

• Comparing your piece against successful examples
• Identifying patterns and gaps
• Suggesting structural improvements

This approach saves revision time. Why polish paragraphs you might cut or rewrite anyway?

Create Quick Quality Checks

Once you have your structure solid, create systematic quality checks.

You want to verify your content hits key engagement metrics.

At Backlinko, we track three readability metrics:

1. Single-Sentence Paragraph Percentage: The ratio of paragraphs with just one sentence.
2. Visual Break Density: Number of visual elements per 1,000 words. Higher density means better scannability.
3. Grade Level: We target Grade 7 or below for accessibility.

AI can calculate these instantly and suggest specific fixes. Here’s how:

1. Share your metrics targets with AI
2. Paste in a section of your content
3. Ask for both analysis and specific fixes

Beyond metrics, use AI to check for:

• Redundant ideas and phrases
• Passive voice overuse
• Transition effectiveness
• Brand voice consistency
• Technical accuracy

Test Critical Elements

Some parts of your content matter more than others.

Your headline determines whether people click.

Your introduction decides if they stay.

Your calls-to-action influence if they convert.

These elements deserve extra attention.

Using headlines as an example, I note 3-5 potential titles.

I Google the topic I’m writing about and screenshot the search results.

I upload the screenshot to Claude. Then, I ask how my title ideas compare to the top articles.

Claude will make suggestions based on our title guidance, best practices, and differentiators.

Yongi uses a similar process for introductions:

You can also test:

• Section transitions
• Examples
• Proof point placement
• Technical explanations
• Closing arguments

Balance AI and Human Editing

AI accelerates the editing process, but human judgment remains essential.

Here’s how to make this work:

1. Start with AI for broad analysis and quick fixes
2. Apply your judgment to AI’s suggestions
3. Test variations of important elements
4. Verify technical accuracy independently
5. Maintain your unique voice and perspective

Chris Shirlow supports this balance:

Start Writing Smarter, Not Harder

Pick one project you need to write this week.

Apply just one of these practices—maybe getting AI’s help with audience research or outlining.

That’s all you need to do to start seeing results.

Ready to put these practices to work? Check out our guide on how to write a blog post.

You’ll see how to combine these AI techniques with proven writing principles to create content that ranks and converts.