I’ve been using Claude to help me write blog posts for a few months now. The process feels remarkably similar to how I use AI as a programming copilot. Same principles, different domain.
When I use Claude Code for coding, I don’t just accept whatever it generates. I give it clear requirements, review the output critically, and iterate until I get what I need. The AI is a junior developer on my team. Smart, fast, but needs good direction and careful review.
Writing with Claude works the same way.
Starting with Clear Requirements
Good AI output starts with good prompts, whether you’re generating code or prose. When I ask Claude to write a blog post, I don’t just say “write about Docker.” I give it specific requirements.
I’ll tell it the target audience (developers, not beginners), the tone (educational, conversational), and the scope (focus on practical usage, not theory). I’ll specify what I want to avoid (don’t rehash basic concepts everyone knows) and what I want to emphasize (real-world problems and solutions). I instruct Claude to ask specific questions to clarify perspective and share anectodes. I also clearly instruct Claude not to invent scenarios. Everything you read in this blog has actually happened. It’s not a hallucination.
For this post, my prompt was specific. Write about using AI for writing, compare it to programming copilots, explain my iterative process, match my voice from previous posts. I gave Claude access to my entire blog archive so it could understand my style.
Just like with code, vague requirements get you vague output. “Make this function faster” produces different results than “optimize this function for memory usage, target sub-100ms response time, maintain thread safety.” Same with writing prompts.
Treating Output Like Code Review
When Copilot suggests a function, I don’t just accept it. I read through the logic, check for edge cases, consider maintainability, and test it. Same process with writing.
Claude’s first draft of this post was decent, but it had issues. It referenced some technologies that I don’t use. This entire paragraph in particular contained some hallucinations. Instead of starting over, we iterated. I treated it like a pull request review.
Each iteration gets closer to what I actually want. Sometimes Claude nails it on the first try. Usually it takes three or four rounds of feedback. Occasionally I’ll scrap a section entirely and ask for a different approach.
The key is being specific about what’s wrong and what I want instead. “Make it better” helps nobody. “This paragraph buries the main point in the third sentence. Lead with that insight” gives Claude something actionable.
When AI Shines vs When It Struggles
AI copilots are great at generating boilerplate code, implementing well-understood patterns, and handling repetitive tasks. They struggle with complex business logic, domain-specific requirements, and architectural decisions.
Writing AI has similar strengths and weaknesses. Claude excels at structure, transitions, and explaining technical concepts clearly. It’s good at maintaining consistent tone once it understands what you want. It can research topics and synthesize information from different sources.
But it struggles with personal experience and unique insights. The best parts of my blog posts are usually stories from my career, lessons learned from specific projects, or contrarian takes on conventional wisdom. Those have to come from me.
Claude can’t tell the story about debugging a race condition at 2 AM or explain why I disagree with popular opinion on microservices. It can help me structure those stories and make them more readable, but the content has to be mine.
The Iterative Loop
My writing process with Claude looks like this:
- Give Claude a detailed prompt with requirements, context, and examples
- Review the first draft like I’m doing code review
- Give specific feedback on what to change, add, or remove
- Repeat until it matches my standards
- Do a final edit pass myself for personal touches and voice consistency
This usually takes 3-5 iterations. The first draft establishes structure and covers the main points. Subsequent rounds refine tone, improve flow, and add depth. The final edit is where I add personal examples and make sure it sounds like me.
Using Claude this way has definitely increased my writing output. I publish more consistently because the initial draft happens faster. I can explore ideas that might not be worth a full manual writing session. I can write about topics where I have opinions but need help with research and structure.
The risk is that AI-assisted writing can feel generic if you’re not careful about maintaining your voice and perspective. The same risk exists with programming copilots. Over-reliance on generated code can lead to applications that work but lack coherent architecture.
Trust But Verify
The principle I use for both coding and writing AI is “trust but verify.” The AI output is usually a good starting point, but it needs human judgment and iteration to become something valuable.
With code, I verify by testing, reviewing, and considering edge cases. With writing, I verify by reading it critically, checking that it makes the points I want to make, and ensuring it sounds like something I would actually say.
Neither approach replaces human expertise. They’re tools that augment it. Used well, they let you focus on the high-value creative work while handling the mechanical aspects more efficiently.
The key is knowing when to trust the AI and when to override it. That judgment comes from experience in the domain, whether it’s software engineering or technical writing.