How to Write Blog Posts Using AI Tools
I’ll be honest with you—when AI writing tools first started gaining traction around 2020, I was skeptical—maybe even a little defensive. After spending over a decade crafting content for clients, building my own blogs, and teaching others how to write, the idea of machines doing “my job” felt threatening.
Fast forward to today, and my perspective has completely shifted. Not because AI replaced my writing skills, but because I learned how to work with these tools in ways that actually made me a better, more productive writer.
This guide is everything I’ve learned about integrating AI into blog writing—what works, what doesn’t, and how to use these tools without losing your authentic voice.
The Reality of AI Writing Tools in 2024
Let’s cut through the noise. AI writing tools aren’t magic wands that turn mediocre ideas into viral content. They’re also not going to steal your job if you understand how to leverage them properly.
What they actually are: sophisticated pattern-recognition systems trained on massive amounts of text. They can generate coherent sentences, suggest structures, help with research summaries, and overcome writer’s block. What they can’t do is replicate lived experience, genuine expertise, or the subtle nuances that make your perspective unique.How to Write Blog Posts Using AI Tools
I think about AI writing assistants the same way I feel about calculators. Did calculators make mathematicians obsolete? No. They freed up mental bandwidth, allowing mathematicians to focus on more complex problems. That’s precisely how AI works in content creation.
Starting With Strategy, Not Software
Here’s where most people go wrong. They sign up for ChatGPT, Claude, or Jasper, type in “write me a blog post about digital marketing,” and wonder why the output feels generic and lifeless.
The problem isn’t the tool. It’s the approach.
Before touching any AI platform, you need the same foundational elements you’d need for any piece of quality content:
A clear target audience. Who exactly are you writing for? What do they already know? What keeps them up at night?
A specific angle. “How to improve SEO” is not an angle. “Why most small businesses waste money on SEO tactics that worked five years ago” is an angle.
Your unique take. What can you say about this topic that nobody else can? What experiences, data, or perspectives do you bring?
I’ve watched countless creators skip these steps, generate AI content, and wonder why it doesn’t resonate. The tool amplifies your input. Garbage in, garbage out.
Building Your AI-Assisted Writing Workflow
After experimenting with dozens of approaches, here’s the workflow that consistently produces results I’m actually proud to publish.
Phase 1: Research and Ideation
This is where AI genuinely shines. I use AI tools to:
- Explore subtopics I might not have considered
- Summarize lengthy research papers or industry reports
- Generate lists of questions my audience might have
- Identify gaps in existing content on a topic
For example, when I was working on an article about remote team management last month, I asked Claude to analyze the common themes across the top-ranking articles on the topic. It helped me identify that most content focused on tools and scheduling, but barely touched on the psychological aspects of isolation. That became my angle.
The key here is using AI as a research partner, not a content factory. You’re gathering raw materials, not finished products.
Phase 2: Outline Development
I never let AI write my outlines entirely, but I do collaborate with it. My process looks something like this:
- I draft a rough outline based on my expertise and research
- I share that outline with an AI tool and ask what I might be missing
- I evaluate its suggestions critically—some are gold, many are generic filler
- I refine my outline, incorporating the valuable additions
This back-and-forth usually takes 15-20 minutes and results in a much stronger structure than either I or the AI would have created on their own.
Phase 3: First Draft Creation
This is where workflows diverge based on personal preference, and I’ve changed my approach multiple times.
Option A: AI-assisted drafting. You write each section yourself, but use AI to generate supporting paragraphs, examples, or explanations you can edit and integrate. This keeps your voice dominant while speeding up the process.
Option B: AI-first drafting. You have AI generate a complete first draft based on your detailed outline, then substantially rewrite it, adding your voice, examples, and expertise. This works best when you’re writing about topics you know well enough to identify and correct inaccuracies.
Option C: Human-first drafting. You write the entire first draft yourself and use AI only for editing suggestions, alternative phrasings, or expanding underdeveloped sections.
I use Option A for most client work and Option C for personal projects where voice matters more than speed. Option B can work, but requires significant rewriting to avoid sounding like everyone else.
Phase 4: Editing and Refinement
Here’s a truth that took me too long to learn: AI-generated content requires more editing, not less.
When I write something myself, I know where I cut corners, where my arguments are weak, and where I need better examples. With AI content, you don’t have that intuitive sense. Every sentence needs scrutiny.
My editing checklist for AI-assisted content:
- Fact verification. AI makes confident claims that are entirely wrong. Every statistic, date, name, and technical detail needs to be checked.
- Voice consistency. Does this sound like something I would actually say? Or does it sound like generic “content”?
- Logical flow. AI sometimes creates smooth transitions that don’t actually make logical sense when examined closely.
- Unique value. Am I saying anything new, or just rephrasing what already exists on page one of Google?
- Awkward phrasing. AI has tells—specific phrases and structures it overuses. Learn to spot and eliminate them.
Maintaining Your Authentic Voice
This is the challenge everyone asks about, and frankly, it’s the most challenging part to get right.
Your voice isn’t just word choice—it’s rhythm, it’s the examples you choose, it’s the opinions you’re willing to express, it’s the specific way you structure arguments. AI can mimic surface-level style elements, but it can’t replicate the substance of who you are as a writer.
Some practical techniques I’ve found helpful:
Train the AI on your existing content. Most platforms allow you to provide examples of your writing. The more context you provide about your style, the closer the output will match it. I typically share 3-4 previous articles and describe my tone as specifically as possible.
Inject personal elements manually. AI can’t write about that frustrating client call you had last Tuesday or the lesson you learned from failing at your first startup. These personal touches are non-negotiable for authentic content. Write them yourself and build the rest around them.
Read everything out loud. If a sentence doesn’t sound like something you’d actually say to a colleague, rewrite it. This simple test catches most voice inconsistencies.
Keep an “anti-phrase” list. I maintain a running list of phrases AI tends to use that I would never write. “It’s worth noting,” “In today’s digital landscape,” “unlock your potential”—whenever I spot these, they get cut or rewritten immediately.
SEO Considerations for AI-Assisted Content
Search engines have gotten remarkably good at identifying helpful content regardless of how it’s produced. Google’s stated position is that they focus on content quality, not content origin. That said, AI-generated content does have some SEO quirks to consider.
Originality still matters. If you’re prompting AI without adding substantial unique value, you’re likely producing content similar to what others are generating. This creates originality problems and won’t perform well.
E-E-A-T signals require human input. AI can’t fake experience, Expertise, Authoritativeness, and Trust. You need genuine credentials, real examples, and verifiable expertise. AI can help you express these elements clearly, but it can’t create them from nothing.
Technical SEO basics still apply. AI-assisted content needs the same optimization fundamentals as any other content—strategic keyword placement, proper heading structure, internal linking, and meta description optimization.
User engagement indicates quality. If readers bounce quickly, don’t share your content, or never return, no amount of AI assistance will save your rankings. The content needs to genuinely serve the reader.
Common Mistakes I See Content Creators Making
After consulting with numerous bloggers and content teams on their AI integration, specific patterns of failure keep appearing.
Mistake #1: Publishing without substantial editing. Raw AI output is recognizable to experienced readers. It lacks conviction, uses overly cautious hedging, and misses the emotional resonance that drives engagement.
Mistake #2: Using AI for topics requiring current information. AI training data has cutoff dates. Anything requiring up-to-the-minute accuracy—news, current events, recent studies—needs human research and verification.
Mistake #3: Neglecting to develop prompting skills. The difference between mediocre and excellent AI output often comes down to how you ask for what you want. Vague prompts produce vague content. Learning to write detailed, specific prompts is a skill worth developing.
Mistake #4: Replacing thinking with generating. The most valuable part of writing isn’t putting words on paper—it’s the thinking that happens before and during that process. If you outsource the thinking to AI, you outsource the value.
Mistake #5: Ignoring ethical obligations. Depending on your industry and audience, disclosure of AI assistance may be expected or required. Consider your professional responsibilities and reader expectations.
The Tools Worth Your Time
I’m not going to give you an exhaustive list of every AI writing tool—those lists are everywhere, and they’re usually outdated within months. Instead, here’s my practical framework for tool selection.
For general-purpose writing assistance, ChatGPT (especially GPT-4) and Claude both produce high-quality output and handle complex prompts well. I personally prefer Claude for longer-form content because it tends to maintain coherence better across extended pieces.
For research and summarization, Perplexity AI has become indispensable for quickly synthesizing information from multiple sources and provides citations, which speeds up fact-checking.
For grammar and style: Grammarly and Hemingway Editor remain valuable even with AI drafting tools available. They catch issues that AI might introduce.
For SEO optimization: Surfer SEO and Clearscope help ensure your content covers topics comprehensively, though I’d caution against letting optimization tools override natural writing flow.
The right tool matters less than the proper process. I’ve seen people produce excellent AI-assisted content with free tools and terrible content with expensive enterprise platforms.
When Not to Use AI
There are times when I deliberately avoid AI assistance entirely.
Deeply personal content. Memoir pieces, vulnerable storytelling, and opinion essays where authenticity is paramount—these deserve to be written by humans.
Content requiring original interviews or reporting. If your value proposition is primary research, AI has nothing to contribute.
Highly technical content in your area of expertise. Sometimes the thinking process is the point. When I’m working through complex ideas in my field, writing them out myself helps me better understand my own thoughts.
When you’re still developing your voice, new writers should focus on finding their authentic style before introducing AI into their workflow. You need to know who you are as a writer before you can guide an AI to sound like you.
Looking Forward
The tools will keep improving. What’s impressive today will seem basic in two years. The writers who thrive will be those who view AI as an ever-evolving toolkit rather than a fixed solution.
What won’t change is what makes content truly valuable: genuine insight, authentic perspective, helpful information, and a real connection with readers. Those elements have always required human intelligence and always will.
My advice? Start experimenting now if you haven’t already. Build AI into your workflow gradually. Please pay attention to what improves your output and what dilutes it. Develop your prompting skills as seriously as you developed your writing skills.
And most importantly, never stop writing things that only you can write. That’s where your real value lies.
Industry-Specific Applications
After working with clients across different sectors, I’ve noticed that AI image editing needs vary dramatically depending on your field. Here’s what I’ve learned about matching tools to industries.
E-commerce and Product Photography
If you’re running an online store or managing product imagery, your editing needs are specific and repetitive. You need consistent backgrounds, color accuracy, and the ability to process high volumes without losing quality.
Background removal becomes your bread and butter. Most e-commerce platforms prefer clean white or transparent backgrounds, and AI tools handle this remarkably well for most product types. I’ve helped several small business owners set up workflows that let them photograph products against a simple backdrop, batch-process them with Remove.bg or Photoshop’s background-removal tools, and export ready-to-upload images in under an hour for dozens of products.
The challenge comes with reflective or transparent items —glass bottles, jewelry, sunglasses—because AI background removers struggle to distinguish between the product and what’s visible through or reflected in it. For these categories, you’ll still need manual refinement, though AI gets you maybe 60-70% of the way there.
Color consistency deserves attention, too. AI enhancement tools sometimes shift colors, leading to misrepresentations of products. A dress that’s actually burgundy might appear cherry red after AI processing. Always compare your edited images against physical products before publishing, especially for clothing, cosmetics, and home décor, where color accuracy drives purchasing decisions.
Real Estate Photography
This industry has enthusiastically embraced AI editing, sometimes to the point of excess. Sky replacement has become standard practice—those dramatic sunset skies you see in property listings rarely existed when the photographer showed up at 2 PM on a Tuesday.
Luminar Neo dominates this niche for good reason. Beyond sky replacement, its ability to adjust interior lighting helps salvage shots where window brightness creates challenging contrast with darker room interiors. The “Relight” feature can bring out shadow detail, making spaces feel more open and inviting.
Virtual staging is another AI application gaining traction in real estate. Tools like Virtual Staging AI and others allow agents to furnish empty rooms digitally. I’ve seen impressive results, though there’s an ongoing debate about whether virtual staging misleads buyers. My take: disclosure matters. If a room is virtually staged, that should be clear in the listing.
One word of caution for real estate professionals: don’t over-process. I’ve seen listings where AI enhancement has made ordinary houses look like architectural digest features. Buyers show up expecting that aesthetic and feel deceived when reality doesn’t live up to it. Enhancement should improve the presentation of what exists, not create false impressions.
Portrait and Event Photography
Portrait photographers have the most nuanced relationship with AI editing tools. The work is deeply personal, and over-editing can quickly cross into uncanny valley territory.
For headshots and professional portraits, AI-powered skin smoothing can save hours of manual retouching. The key is finding tools with adjustable intensity. Facetune and similar apps default to aggressive smoothing that removes skin texture entirely. Better results come from using Photoshop’s Neural Filters or Luminar’s portrait tools with restraint—reducing blemishes while maintaining the texture that makes faces look human.
Eye enhancement is another area where AI helps, but restraint matters. Tools can brighten eyes, enhance catchlights, and increase apparent sharpness. Used subtly, these adjustments add life to portraits. Pushed too far, your subjects look like they’re staring into your soul from a horror movie poster.
Event photographers who handle hundreds or thousands of images per job benefit greatly from AI-powered culling tools. Photo Mechanic Plus and Aftershoot both use AI to identify the best shots from sequences, grouping similar images and flagging ones with technical problems. This can cut hours from the selection process.
Social Media and Content Creation
For content creators and social media managers, speed often trumps perfection. You need good-enough images quickly rather than perfect images eventually.
Canva has positioned itself brilliantly for this market. The ability to remove a background, add text, and export for specific platform dimensions in a single workflow eliminates the need for constant switching between applications. For Instagram Stories, TikTok content, and social posts with a 24-48 hour relevance window, this efficiency matters more than marginal quality improvements from professional tools.
One underappreciated AI feature for content creators: automatic resizing and reformatting. Creating one image and needing it for Instagram feed, Stories, Twitter, LinkedIn, and Facebook used to mean five separate export processes. Tools like Canva and Adobe Express now handle this intelligently, repositioning elements and cropping appropriately for each platform’s requirements.
Batch Processing Strategies
When you’re dealing with large volumes of images, individual editing becomes impractical, regardless of how fast AI can make each edit. Here’s how I approach high-volume work.
Create presets and save them. Most AI-powered tools allow you to save settings. If you’re processing product photos that all need similar treatment, establish your settings on the first image and apply them consistently.
Organize before you edit. Spending 10 minutes sorting images into folders by type or required treatment saves time compared to processing everything identically and then fixing outliers later.
Build staged workflows. Rather than completing each image from start to finish, consider processing in stages. Remove all backgrounds first, then apply color corrections to everything, then do final reviews. This approach is faster for most people because you’re not constantly switching mental contexts.
Accept “good enough” strategically. Not every image deserves perfection. Social media images that will be visible for one day don’t need the same attention as website hero images that will remain for years. Match your effort to the image’s importance.
Use automation where available. Photoshop actions, Lightroom presets, and scripting capabilities in various tools can automate repetitive steps. The initial setup takes time but pays off quickly with recurring workflows.
Quality Control Processes
Speed means nothing if your final images have problems. Here’s my quality control checklist after AI processing:
Zoom to 100% and check edges. AI background removal and object removal often leave artifacts visible only at full resolution. Hair edges, fabric boundaries, and complex shapes deserve close inspection.
Compare before and after. Most tools offer this toggle. Use it. Sometimes AI “improvements” actually degrade image quality in subtle ways you’ll only notice through direct comparison.
Check across devices. Colors can appear differently on various screens. If possible, view processed images on the device where your audience will see them. That perfectly edited photo on your calibrated monitor might look different on typical phone screens.
Get a second opinion. When I’ve been staring at an image for a while, I lose objectivity. A quick review from a colleague or friend often catches issues I’ve stopped seeing.
Maintain originals. Always keep unedited source files. AI tools occasionally make choices you’ll want to reverse, and having the original available means you can reprocess rather than starting from scratch with a flawed edit.
Learning and Improving Your Skills
The tools themselves continue evolving, but so should your ability to use them effectively. Here’s what’s helped me stay current:
Official tutorials exist for reasons. Adobe, Skylum, and other companies produce training content that’s usually higher quality than random YouTube videos. Start there before diving into third-party tutorials.
Join communities. Reddit communities, Facebook groups, and Discord servers dedicated to specific tools provide real-world insights that documentation misses. When I encounter a problem, someone in these communities has usually already solved it.
Experiment on non-critical images. Before using new features on client work, test them extensively on personal photos where mistakes don’t matter. You’ll learn the tool’s limitations without risking professional embarrassment.
Watch the updates. These tools update frequently, sometimes adding significant new capabilities. Staying informed about updates ensures you don’t miss features that could transform your workflow.
Cross-train on multiple tools. Understanding several AI editing applications helps you recognize which tool suits which task, and often techniques learned in one platform apply conceptually to others.
The image editing landscape will continue to evolve. Tools that feel cutting-edge today will seem basic in two years. But the principles—matching tools to tasks, maintaining quality standards, working efficiently at scale—remain constant even as specific software changes. Master the principles, stay adaptable with the tools, and you’ll continue producing excellent work regardless of how the technology develops.