Just built a workflow that chains GPT output, Grammarly, and a human pass - results so far

I've been iterating on my blog post creation process for my team's internal knowledge base, trying to balance quality with efficiency. The goal was to get clean, consistent, and technically accurate drafts without burning hours on initial structuring and copy-editing. After a few weeks of tweaking, I've landed on a three-stage workflow that's working surprisingly well.

Here's the basic pipeline:
1. **Initial Draft with GPT-4:** I feed it a detailed outline, key technical terms, and code snippets. The prompt is crucial—it specifies our desired tone, that placeholders for diagrams are needed, and to avoid marketing fluff.
2. **Grammar & Clarity Pass with Grammarly Business:** I run the raw output through Grammarly. I've tuned its goals to focus on "Correctness," "Clarity," and "Conciseness" in a technical context. It catches awkward phrasing and passive voice that I often miss.
3. **Human Edit & Technical Validation:** This is where I add the real value. I verify all AWS service names, Terraform code blocks, and architecture logic. I also inject specific anecdotes or lessons learned from our projects.

The key was creating a simple Node.js script that semi-automates the flow between steps, saving the outputs with timestamps. It's nothing fancy, but it keeps things organized.

```javascript
// Example of the simple orchestration script
const workflow = {
steps: ['gpt-draft', 'grammarly-review', 'human-edit'],
currentStep: 0,
next: function() {
// Logic to pass content to next tool (simplified)
console.log(`Moving to step: ${this.steps[this.currentStep]}`);
}
};
```

The results? My first-draft acceptance rate has gone way up. I spend maybe 25% less time on pure editing and more on deepening the technical content. The main lesson was that Grammarly acts as a great "buffer" between AI and human, cleaning up the prose so I can focus on substance.

Has anyone else set up a similar toolchain? I'm curious how others are handling the technical validation step, especially for infrastructure-as-code examples.

-- Amy