We've spent the past eight months testing AI writing tools while developing our content creation software at Good Bloggy. One thing has become crystal clear: AI works best as a supplement, not a replacement for human writers. The output requires significant editing and often complete rewrites, but we've found effective ways to integrate AI into our own writing process.
When we first tried AI copywriting, the results disappointed us. Content felt generic, lacked voice, and sometimes contained fabricated information. As a small two-person team with our day jobs in content writing, we've learned to view AI as a specialized assistant with specific strengths and clear limitations.
Here's what we've discovered about making AI work effectively in a professional writing workflow while maintaining quality and authenticity.
AI for Research: Finding Angles and Supporting Details
Research often consumes more time than writing itself. We've found AI tools can significantly streamline initial information gathering, providing a solid foundation to build upon.
Specific prompting techniques yield better research results. Instead of broad questions, we ask for competing perspectives or historical context to get nuanced information. We also focus prompts on recent information when needed.
Hallucinated facts remain our biggest challenge. We've implemented a verification system where any claim needs at least two independent AI-sourced confirmations, with critical facts requiring human verification through primary sources. This approach has dramatically reduced errors.
When using AI for synthesizing information from multiple sources, we still manually verify all statistics. AI often misses subtle but important variations that can become central to analysis.
AI for Outlining: Creating Better Content Structures
Through our work on Good Bloggy, we've tested several AI tools for outline generation. The key to success isn't just the model—it's how we frame our requests. Generic prompts yield predictable, repetitive structures that rarely serve our needs.
Our best results come from providing AI with specific audience parameters and key points. Rather than requesting a generic outline, we define exactly who the content is for and what key topics must be addressed.
AI outlines often suffer from repetition—sections covering similar ground or following overly predictable patterns. Our solution is to request more sections than needed, then select and rearrange the best ones into a cohesive flow. This hybrid approach maintains efficiency while ensuring the structure feels intentional.
An effective outline prompt template should follow this pattern: "Create an outline for [content type] about [topic] for [specific audience] that addresses [3-4 key points]. Include a mix of practical advice and conceptual background." This balances structure with enough creative latitude to avoid formulaic outputs.
AI for First Drafts: Getting Past the Blank Page
We use AI for generating initial drafts, treating them as starting points rather than finished products. These drafts often have good structure and basic information, but lack nuance and authentic voice. Still, they're better than facing a blank page.
Our testing shows clear patterns in AI's strengths and weaknesses. It excels at summarizing data and explaining technical concepts but consistently falls short with storytelling, compelling hooks, and maintaining brand voice—the elements that make content memorable.
We improve output quality by providing AI with detailed outlines and voice guidelines. Instead of vague requests, we give specific bullet points for each section and examples of our preferred tone, resulting in drafts that need less extensive reworking.
Generating content section-by-section rather than full articles has proven more effective. When requesting entire long articles, quality deteriorates noticeably around the midpoint. Shorter, focused sections maintain higher quality throughout.
Realistically, we spend up to an hour minutes revising a 1,000-word AI draft. That's faster than writing from scratch for us, but far from "instant content." The efficiency gains are modest but worthwhile.
AI for Editing and Polishing: Refining Your Message
AI has transformed our editing process at specific checkpoints. We use multiple tools in sequence: one for basic error correction, another to enforce style guide rules, and custom prompts to check readability and suggest sentence improvements.
For headlines, AI helps generate volume. We create a few headline concepts manually, then ask AI to generate variations. This hybrid method yields more creative options while maintaining our strategic direction. AI often introduces unexpected angles we wouldn't have considered.
When adapting content for different audiences, AI helps transform technical content into simpler language or executive-focused messaging. The results typically require light editing but save considerable time compared to manual rewrites.
In our experience, specialized writing assistants outperform generic AI for technical corrections, while larger language models excel at nuanced tone adjustments and readability improvements.
The limitations remain significant. AI editors struggle with preserving unique brand voices and often miss culturally insensitive language. We've learned to be particularly cautious with humor and idiomatic expressions, which AI frequently misinterprets.
AI for Fact Checking: Verification and Accuracy
While AI promises to simplify fact checking, we've discovered significant limitations. During testing, AI models confidently presented fabricated statistics, misattributed quotes, and even invented non-existent sources. This "hallucination" makes AI unreliable as a standalone verification tool.
We've developed a two-step process that leverages AI's strengths while protecting against weaknesses. First, we use prompts to flag potential inaccuracies: "Identify claims requiring verification" or "What statements might be factually disputed?" This initial scan catches many questionable assertions.
Our follow-up prompts dig deeper: "What are different perspectives on this claim?" and "What evidence contradicts this statement?" These challenges help expose overgeneralizations. Crucially, we never accept AI's verification as final—human oversight remains essential.
Our manual verification has caught numerous errors that AI either missed or introduced, reinforcing why human expertise remains fundamental to fact-checking.
The most effective approach combines AI to flag potential issues and suggest verification angles with human verification of all key facts.
Conclusion: A Balanced Approach to AI in Copywriting
Through our work developing Good Bloggy, we've landed on what we call the 40/60 rule: AI handles about 40% of our copywriting workflow while humans manage the remaining 60%. This ratio has proven most effective in our projects.
Despite AI's advances, writers still need core skills like strategic thinking, audience empathy, and brand voice development. The most successful AI users maintain strong foundations in storytelling and messaging strategy, using AI to enhance rather than replace these fundamentals.
We've found AI most valuable for specific tasks: initial research gathering, outline generation, and first-draft acceleration. It's less effective for creating emotional hooks, maintaining consistent voice, and fact verification—areas where human expertise remains essential.
Our experience aligns with industry findings: the future belongs to writers who view AI as a collaborative tool rather than a replacement. The most successful approach develops "AI fluency"—the ability to effectively direct AI tools while maintaining creative control over the final product.