Grammarly's blanket suggestions can create more problems than they solve for junior engineers writing technical docs or commit messages. You need a filter.
Don't rely on manual review. That doesn't scale. Instead, control the input.
* **Use the Grammarly API or a CI/CD pipeline.** Scrub the text *before* it's seen by a human.
* **Build a denylist.** Reject suggestions that change technical accuracy.
* Block over-correction of: technical acronyms, product names, code snippets, CLI commands.
* Block passive voice enforcement in security/incident documentation where agency is critical.
* **Example rule for a markdown linter in CI:**
```yaml
# Example in a GitHub Actions workflow
- name: Audit Grammarly-like changes
run: |
# Reject changes where 'K8s' was replaced with 'Kings' or 'its' was replaced with 'it is'
if git diff --word-diff HEAD~1 | grep -E "[-(K8s|its)-]"; then
echo "ERROR: Technical term or clarity altered."
exit 1
fi
```
The goal isn't to review every comma. It's to automatically block the high-risk, contextually wrong "corrections" that break technical communication. Let the juniors focus on real grammar, not fighting a tool.
Simplicity is the ultimate sophistication
I'm a junior data engineer at a mid-size fintech. We don't use the Grammarly API, but we do use automated linting for our technical docs in the CI pipeline.
**Cost to scale:** The custom CI script is basically free in terms of software cost, but the engineering time to build and maintain it adds up. At my shop, it probably took a senior engineer a week. For the Grammarly API, you're looking at a separate monthly cost per user on top of your existing CI/CD bills.
**Deployment effort:** The CI script approach is immediate if you already have CI/CD (just add a job). Integrating a separate API into your workflow requires setting up a service to call it, handle auth, and manage API keys securely, which is a bigger lift.
**Accuracy and control:** A simple denylist in CI can't catch nuanced issues like inappropriate passive voice changes. It's great for blocking exact matches, but it breaks on complex rewrites. A proper API integration could potentially parse suggestions before applying them, but that's a much more complex project.
**Maintenance overhead:** The denylist is a living document. Every time a new product name or acronym is flagged, someone has to update the list. It can feel like whack-a-mole. A vendor API would handle its own rule updates, but you're trusting their changes.
If you have tight control over your docs format and a known list of technical terms, start with the CI script. It's the fastest way to block the obvious problems. If you need to audit the *suggestions themselves* before any text is changed, you're looking at the API path. Tell us your team's size and if you already have a dedicated platform engineering group to handle the integration.