Why I Built This System

When I started developing content in the healthcare and pharmacy space, I quickly realized that most blogs fell into one of two traps:

• They were highly accurate but painfully slow to publish.
• Or, they were fast and AI-generated but lacked domain expertise.

I needed a third way—something scalable, smart, and still trustworthy. What I built is a content sprint engine designed for pharmacy, healthcare, and adjacent verticals. It blends AI acceleration with pharmacist-level editing and system-level optimization. Below is my full writing process when optimized for speed—raw, real, and ready for adaptation.

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Step 1: Align Keyword Research with Website Categories

Before anything gets written, the first step is strategic alignment. I categorize potential content under these primary topic zones, but of course, these can be adapted to preferences, especially as they change. My interests have already shifted from the list below:

• The Future of Pharmacy
• Healthcare Navigation and Insurance Literacy
• Patient Education and Medication Management
• Opinion
• Technology and AI in Pharmacy
• Managed Care Pharmacy
• (Additional categories added as needed)

Using ChatGPT, I build seed keyword lists to explore for each category, then use SEMrush’s Keyword Strategy Builder to evaluate performance and search intent. Much like Palantir builds vertical platforms around domain logic, I intend to design my content engines around real-world expertise pillars. If that sounds like your language, we might work well together.

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Step 2: Manual Keyword Curation and Excel Cleanup

For each category, I select five distinct keywords. These are entered into the SEMrush builder. The results are:

• Exported into Excel or CSV
• Cleaned by keyword difficulty (KD%)
• Reviewed for relevance and search volume
• Combined into clusters using TEXTJOIN to enhance planning

Optional tip: Each keyword can represent a unique article opportunity, or multiple keywords can be integrated into one idea. The single keyword approach helps better match search intent with specialized content. Also, you don’t always have to go chasing traffic. Build your own concepts and eventually, with enough promotion, they will be seen.

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Step 3: Keyword Expansion into Outlines with ChatGPT

The cleaned keyword list is pasted into ChatGPT, where I prompt it to generate multiple outlines (sometimes in batches). Each outline is designed to incorporate a few or several of the SEO keywords in a natural, thematic way. I evaluate these outlines for coverage depth and topical relevance.

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Step 4: Outline Review for Intent and Voice

Next, I manually edit the outlines to better reflect:

• The intent behind the search term
• My brand’s tone of voice
• Areas where my personal expertise adds real value

This step ensures the articles don’t just rank—they resonate.

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Step 5: Article Drafting with Parameters

Once the outline is finalized, it goes back into ChatGPT with clear parameters:

• Reading level (usually 8th to 10th grade)
• Target angle or tone (e.g. professional but conversational)
• Word count range (600–800 for snippets, 1000+ for long-form)

The AI generates first drafts rapidly, which is where velocity finally kicks in.

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Step 6: Assemble Drafts into a Word Document

The drafts are pasted into a single Word document. I use a delimiter (like ////) to separate articles. This makes it easier for batch processing in a txt file.

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Step 7: Convert Drafts via Python Script

In VSCode, I run a Python script that:

• Reads the delimited txt file
• Splits articles by delimiter
• Exports each to a new Word doc
• Saves with filenames based on the article title (first line)

This step saves hours of manual work. You can also integrate this with Notion, Airtable or other workstation hubs.