Alright, hear me out. I've been staring at post-incident reports, RFCs, and Slack threads at 3 AM for years. Our team started using Grammarly for Business to "standardize communication and reduce errors." Sounds good on paper, right?
But after six months, I've noticed something off. Every document started to sound... the same. Like it was run through a corporate blandifier. The tool aggressively pushes for its "clarity" and "conciseness" suggestions, which often strips out the subtle nuances that give technical writing its actual meaning.
Take this example from a recent incident summary.
**Original (human):** "The API cascade started when the primary node got wedged. The failover mechanism *hesitated*, then tripped over its own logic, causing a thundering herd."
**Grammarly's "improved" version:** "The API failure sequence began when the primary node stopped responding. The failover mechanism was delayed, then malfunctioned, causing significant load."
See what it did? "Got wedged" is a precise technical state. "Hesitated" implies a specific, almost behavioral flaw in the logic. "Thundering herd" is a well-known pattern. Grammarly replaced them with generic, less accurate terms. It's trading precision for... what, exactly? Polished vagueness?
My theory: It's optimized for business/marketing copy, not for technical or operational clarity. It kills:
* **Specific jargon** that carries exact meaning in our field.
* **Tone** that conveys urgency or irony, which is sometimes necessary.
* Any slightly unconventional but perfectly clear phrasing.
Now I've got juniors who second-guess their own clear writing because Grammarly flags it as "too wordy" or "informal." We're not writing press releases; we're trying to convey complex, often broken, systems accurately.
Anyone else in the infra/devops trenches seeing this? Or am I just too sleep-deprived and cynical?
Pager duty survivor.
NightOps
You've hit on a specific cost of automation that we measure directly in data engineering. We enforce strict linting and style guides on our SQL and orchestration code. The trade off is identical. A perfectly formatted, "clean" DAG often loses the explanatory intent a senior engineer embeds in a slightly unconventional comment or variable name.
In your example, the loss of precision is a real operational risk. "Wedged" and "thundering herd" are diagnostic. They immediately tell an on call engineer the class of failure and point to a known set of remediations. "Stopped responding" and "significant load" are generic starting points that force a re investigation. The tool optimized for superficial readability at the expense of technical accuracy.
We solved a similar issue by creating a domain specific dictionary for our pipeline alerts. The linter must pass our internal terms like "dead letter queue saturation" or "idempotency key collision." Could your team's style guide incorporate a protected glossary of precise technical jargon?
data is the product
That's the right approach. We do the same for our monitoring dashboards and alert text. The allowed term list is crucial.
The problem is tools like Grammarly don't have a feedback loop for their dictionary. If a new technical term emerges in your postmortems, the tool flags it as "unclear" forever. You have to manually whitelist it each time, which breaks the flow.
We ended up disabling its "clarity" and "engagement" scoring entirely. It only gets to check for actual typos now.
Data over opinions
You're absolutely right about the loss of precision. "Wedged" versus "stopped responding" is the perfect example. One describes a specific failure mode, the other just states an outcome. In a post-mortem, that distinction is everything.
We ran into this with vendor security questionnaires. Grammarly kept stripping out the precise, contractual language we needed to use for compliance, suggesting more "concise" alternatives that were actually less legally sound. It was actively undermining the document's purpose.
Your team's workaround of disabling the style suggestions is the way to go. These tools are decent spellcheckers, but they're terrible editors for technical or nuanced content. The generic tone is a feature, not a bug, for them.
Review first, buy later.