I've been evaluating Grammarly's integration into our technical documentation workflow, and I've noticed a persistent pattern in its 'word choice' suggestions that's counterproductive for precision. While the tool excels at catching grammatical errors, its synonym recommendations often favor more common, but less accurate, terms.
For example, in a recent API spec draft, I wrote: "The cache is **invalidated** upon schema migration." Grammarly suggested replacing "invalidated" with "canceled" or "ended." In a technical context, these are not synonyms. "Invalidated" implies the data is now marked as stale and unusable, a specific state. "Canceled" is an action, not a state, and is semantically incorrect here.
Another instance:
- My text: "The query **coalesces** null values to zero."
- Suggestion: Consider "combines" or "merges."
- The problem: `COALESCE` is a named SQL function with specific behavior. "Combines" is vague and loses the database-specific meaning.
This matters because:
* Technical writing requires unambiguous terminology.
* Replacing precise jargon with generic language can introduce confusion.
* For developers reading docs, a "lease" (as in a distributed lock lease) is not simply a "rental agreement."
The underlying issue seems to be a lexical database optimized for general language, not domain-specific terminology. It lacks context-awareness for technical fields. I've had to disable the 'word choice' feature for code-related documents entirely.
Has anyone else in backend or dev-tool spaces encountered this? What's your workflow for using grammar tools without sacrificing necessary precision? I'm currently leaning towards a layered approach: Grammarly for high-level prose, but a manual pass for all technical sections.
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You're absolutely right about the jargon problem. It's the same reason these tools can be a nightmare for any specialized field, not just technical writing.
I saw it constantly when migrating our customer support data between platforms. I'd write about "resolving a ticket" and it'd suggest "solving" it. In a CRM context, those aren't the same! A "resolved" ticket has a specific workflow state and triggers specific automations. "Solved" is just... vague. It strips the operational meaning right out.
It feels like these algorithms are trained on general, literary English, where variety is king. In our worlds, precision is king. Sometimes the exact, correct word is the boring one. The suggestion to replace "coalesces" with "combines" gave me a little shudder, honestly. That would confuse any developer worth their salt.
Exactly! It's the same in my world with task management systems. If I write that a task is "blocked," it has a very specific meaning in our workflow (waiting on someone else). An AI tool might suggest "delayed" or "paused," which are totally different internal states and would break our reporting.
The core issue is these tools optimize for vocabulary variety, not domain-specific meaning. For general writing, sure. For anything with a glossary or defined terms, it's actively harmful.
dk