How to Use AI for Writing Exceptional Content (7 Best Practices)
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.
I asked our content team about how they use AI in their writing and editing.
Our senior writers Yongi Barnard, Kate Starr, Shreelekha Singh, and senior editors Chris Hanna and Chris Shirlow shared their workflows and insights, which I’ll feature throughout this article.
These talented folks help Backlinko generate almost 800,000 sessions per month.
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.
I’m researching for a piece about using AI to write good content. I’ve attached five relevant Reddit threads. Please analyze these conversations and create a table of:
Desires: What do people want to achieve?
Pain points: What problems or challenges do they face?
Objections: What concerns or resistance do they express?
For each theme, please include a relevant supporting quote from the discussions.
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:
“I often feed different data sources to AI. This includes Google’s People Also Ask sections and client conversation transcripts. The key is finding real conversations where your target audience expresses their challenges.
I recently took a consulting call transcript and created a detailed customer persona. This helped shape our entire content strategy”.
The key is to use AI to:
Process large amounts of audience data quickly
Identify patterns you might miss manually
Connect insights to real human experiences through quotes
Tip: Create a folder of interesting community discussions. When you find an engaging thread, save it immediately. You never know when that insight will be useful for future content.
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:
I’ve shared several articles about [TOPIC]. Please analyze them and create a detailed breakdown with these categories:
Common Patterns:
List 3-4 approaches that appear frequently
Include specific examples from the articles
Note any patterns in structure or presentation
Assumptions Made:
List 3-4 assumptions these articles make without questioning them
Point out any gaps in evidence or logic
Identify claims that could use more support
Missing Views:
List 3-4 important perspectives that aren’t being discussed
Note any audience needs that aren’t fully addressed
Highlight opportunities for deeper coverage
Notable Elements:
List any unique or particularly effective approaches
Point out strong evidence or examples
Identify innovative structure or presentation
Here’s the output for using AI in writing:
Category
Details
Common Patterns
Tool comparisons focusing on features over strategy
Basic “input/output” workflow examples
Generic productivity benefits without specifics
Heavy focus on prompt engineering
Assumptions Made
More AI usage automatically means better content
Readers already understand AI fundamentals
One-size-fits-all approaches will work
Ethical concerns can be handled with simple disclaimers
Missing Views
Quality control processes and measurements
Strategic frameworks for tool selection
Real examples of human-AI collaboration
Cost-benefit analysis for different approaches
Notable Elements
One article included detailed workflow diagrams
Another featured mini case studies of failures
Several had good ethical guidelines
One included original research on AI output quality
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.
Pro tip: Your first insight often leads to an even better angle. Don’t be afraid to evolve your thinking beyond what the initial analysis shows.
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:
“I use my own competitor analysis to create outlines first. Then, I’ll use AI to spot potential gaps. For example, when outlining an email marketing guide, AI might suggest adding a section on automation that I hadn’t considered”.
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:
Demand
Audience
Angle
Structure
Research
Writing
Visuals
Enhancements
Editing
Then, I prompted Claude with some attached reference material.
Hey Claude, I’m writing an article about using how to use AI to write better content. I’ve prepared my brief and a very loose outline. I’ve also attached an article that inspired the idea.
What are your initial impressions of my approach?
Claude’s analysis (I’ve summarized the output):
Strong Points
AI positioning: Framed as a tool (like a carpenter’s drill) rather than a replacement
Timeless focus: Principles over specific AI tools/prompts for longer relevance
Credibility: Using concrete case studies and personal examples
Areas for Development
Structure: Consider combining overlapping principles (e.g., “Audience” and “Research”)
Implementation: Add more specific examples of AI enhancing each practice
Meta-narrative: Strengthen how we’re using AI to write this very article
Balance: Include section on AI limitations for completeness
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:
Version 1
Version 2
Version 3
Version 4
Version 5
Demand
Deep Research: Moving Beyond Surface-Leval Facts
Talk to Your Customers First
Define Your Audience
Use AI to Define Your Audience
Audience
Audience Understanding: Creating Content That Deeply Resonates
Build Your Audience Map
Find Your Unique Angle
Use AI to Find Your Unique Angle
Angle
Strategic Positioning: Finding Your Unique Angle
Develop Your Unique Angle
Create a Clear Outline
Use AI to Create an Outline
Structure
Narrative Architecture: Building Clear, Compelling Flow
Structure Your Content Flow
Research Your Topic
Use AI to Research Your Topic
Research
Evidence & Examples: Making Ideas Concrete
Show, Don’t Just Tell
Writing Engaging Content
Use AI to Write Engaging Content
Writing
Voice & Style: Crafting a Distinctive Presence
Find Your Brand Voice
Add Valuable Supporting Elements
Use AI to Add Valuable Supporting Elements
Enhancements
Enhancement Elements: Amplifying Impact
Add Powerful Enhancements
Edit for Impact
Use AI to Edit Your Draft
Editing
Refinement & Optimization: Polishing for Performance
Polish Your Final Product
This iterative process helped me:
Make headings more action-oriented
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:
Me: I’m finding limitations in my thinking. I could add more perspectives by surveying our editorial team about how they use AI.
AI: This could significantly strengthen the article. Multiple writers demonstrating how they tackle AI challenges would make your points more powerful than theory alone.
Consider:
What specific insights you need
The most efficient way to gather them
How to make participation easy
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:
Before we design specific questions, please evaluate this research approach:
Target: [describe who you’ll survey/interview]
Goal: [what you need to learn]
Method: [how you plan to gather data]
Timeline: [your constraints]
Identify any:
Potential issues
Missing opportunities
Ways to improve efficiency
Then, the research questions:
Help me create [survey/interview] questions about topic.
Consider:
Audience: [describe participants]
Time constraints: [expected time commitment]
Key objectives: [list 2-3 main goals]
Required output: [what you need to create]
Structure questions to be:
Focused yet open-ended
Easy to answer quickly
Specific about examples needed
Finally, test your questions:
Here are my draft questions. Please analyze them for:
Clarity and potential confusion
Leading or biased language
Gaps in coverage
Logical flow
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.
Please help me prepare for an expert interview about topic.
Review this background material and suggest:
Key discussion areas to cover
Follow-up questions for each area
Potential examples to request
Data points to validate
Process the recording.
I’ve shared a transcript of my expert interview about topic. Please:
Create a structured summary of key points
Extract specific examples and case studies
Identify unique insights or perspectives
Pull compelling quotes
Note areas needing clarification or follow-up
Validate insights.
Here are my key takeaways from the interview. Please:
Check if conclusions are supported by the transcript
Identify any assumptions I’m making
Suggest additional context needed
Note alternative interpretations
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.
I’ve shared our team’s survey responses about AI usage. Please:
Identify common patterns across responses
Note unique or unexpected approaches
Highlight particularly detailed examples
Suggest potential themes to explore
Drill down to the specifics.
For the [specific practice], please analyze:
Different approaches team members use
Most successful use cases
Common challenges or limitations
Specific tools or prompts mentioned
Notable workflow differences
Extract supporting material.
From these responses about topic, please:
Find compelling quotes that illustrate key points
Identify concrete examples with clear outcomes
Note any interesting AI prompts shared
Suggest potential visuals or diagrams
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.
Pro tip: Before using AI to analyze data, clearly define what “valuable insights” means for your project. This helps AI focus on what matters most.
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:
“I’m writing about ecommerce growth strategies. Please analyze this founder interview and:
Identify key decisions that drove growth
Extract specific metrics and results
Find unique insights about their process
Pull compelling quotes to support each point”
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.
“When writing about AI in healthcare, I always share my article’s specific objectives and approach with Perplexity.
I’ll outline that I need evidence-based analysis focused on measurable outcomes. Not just predictions.
This detailed context helps AI find more relevant research papers and case studies.”
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.
Tip: If your AI tool doesn’t have a project feature, you can save your resources in a folder on your computer. Then, you can use them in your chats.
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.
“I create different projects for different aspects of my writing. This helps me maintain focus and ensures AI has exactly the context needed for each task”.
Depending on your LLM, this sectional approach will help manage your daily credits as long chats burn through your usage.
Pro tip: Write the first 10% of your project from scratch. This will set the tone for your piece and give AI a clear direction for better outputs.
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:
“I describe the concept I’m trying to illustrate and the type of example I need. AI often suggests angles I hadn’t considered, which I can then research further.
Here’s my go-to prompt template:
“I’m explaining [concept]. I need an example that shows [specific aspect]. Ideally from [industry/type of company]. The example should demonstrate [desired outcome].”
For instance, while writing about data visualization, I needed examples of companies using charts effectively in their content. I gave AI these parameters, and it suggested looking at HubSpot’s State of Marketing report—which perfectly illustrated my point about making complex data accessible.
But don’t just take AI’s suggestions at face value. Use them as starting points for deeper investigation. When AI suggests an example, I:
Verify the details independently
Look for additional context
Consider alternative examples
Evaluate if it truly serves my argument
Chris Shirlow emphasizes this balanced approach:
“The key is to start with your own ideas and research. Then use AI to expand those concepts and find fresh angles. Never let AI drive the direction of your content.”
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:
“Please help me design a graphic showing these three personalization statistics:
80% of consumers are more likely to purchase if brands offer personalized experiences
48.2% of marketers say personalization improves click-through rates the most
66% of customers expect companies to understand their personal needs
Use:
A clean, minimal design
Progress circles to represent percentages
Dark background with light text for contrast
Short, clear descriptions under each statistic
Space for source attribution”
Then, you just need to refine the finishing touches (colors, spacing, etc.).
Pro tip: Don’t just think about data visualization. Use AI to create:
Process diagrams
Comparison charts
Timeline graphics
Concept illustrations
Feature breakdowns
Our senior editor, Chris Hanna, puts it well:
“The best writers think like producers now. They ask themselves: how can I make this concept visual? How can I show instead of tell?
AI makes that possible without becoming a design expert”.
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:
“I’ve written an article about topic. Please create a comprehensive checklist that:
Breaks down each major step
Includes key decision points
Notes important resources needed
Flags common pitfalls to avoid
Suggests ways to validate progress”
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:
Identify calculation needs in your content
Ask AI to help structure the logic and formulas
Design the user interface (AI can mock this up)
Build it in your no-code tool of choice
For instance, when building the profitability calculator, I prompted AI with:
“I need a calculator that helps ecommerce owners estimate profitability. It should:
Include key metrics like COGS, shipping, and marketing costs
Calculate gross and net margins
Show breakeven analysis
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:
“I feed the draft, outline, and brief to Claude. Then I ask: What’s missing? Where could we strengthen the argument? Which sections need more evidence?”
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:
Single-Sentence Paragraph Percentage: The ratio of paragraphs with just one sentence.
Visual Break Density: Number of visual elements per 1,000 words. Higher density means better scannability.
Grade Level: We target Grade 7 or below for accessibility.
AI can calculate these instantly and suggest specific fixes. Here’s how:
Share your metrics targets with AI
Paste in a section of your content
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:
“I write three different openings and ask AI which one creates the strongest hook. Then we discuss why—looking at elements like curiosity, relevance, and emotional pull”.
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:
Start with AI for broad analysis and quick fixes
Apply your judgment to AI’s suggestions
Test variations of important elements
Verify technical accuracy independently
Maintain your unique voice and perspective
Chris Shirlow supports this balance:
“AI helps us identify potential issues faster. But we still need human expertise to decide what changes actually serve our readers.”
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.