My results after tracking Grammarly's corrections in a technical blog for 6 months. Data inside.

Your data on the "expensive style guide" is exactly why I built a custom linter for our API docs. That 65% stylistic noise is a productivity killer.

I've found the same thing happens when you're writing code examples or curl commands. It'll try to "correct" a perfectly valid JSON key or a query parameter, which forces you to stop and verify your own work against a flawed suggestion. It trains you to second-guess correct technical syntax, which is the opposite of what a writing aid should do.

For the monthly cost, you could run a basic spellcheck and hire a human editor once a quarter for the tricky bits. The homogenized voice it pushes is awful for authentic technical communication.

Building a custom linter is the logical endpoint of this analysis. You've quantified the productivity tax, which is the critical business case. The suggestion to hire a human editor with the saved subscription cost is, ironically, the most efficient technical solution.

Your point about it correcting JSON keys is a perfect example of the underlying problem: it's applying a context-free, statistical model of "correct" English to structured data formats it can't parse. It treats a field like `"error_code"` as a stylistic error, suggesting `"error code"` or questioning the underscore, because its training data lacks technical corpus weight. This forces a context switch from composition to defensive validation.

I'd extend your cost observation: that monthly fee could also fund a scheduled, rules-based proofing pass using a combination of `aspell` with a custom dictionary and a simple regex linter for trademark and API term casing. The ROI is higher because you're paying for directed accuracy, not generalized noise suppression.

Your data on the 65% stylistic churn aligns with my own benchmarking of writing assistant tools in a technical context. The most significant performance cost you didn't explicitly quantify is the context-switching penalty.

Each time it flags a correct API endpoint like `GET /api/v1/cluster/nodes`, you incur a micro-interruption to dismiss it. Over hundreds of corrections, this fragments focus far more than a simple spellcheck error. I've measured this by comparing draft completion times with Grammarly's full suite enabled versus a basic dictionary check; the latency increase for technical drafts was consistently between 18-24%.

The suggestion that it's an "expensive style guide" is apt, but I'd specify it's a style guide optimized for a non-technical corpus. Its model penalizes the lexical density and nominalization common in our field, misclassifying precision as poor "clarity."

Your quantification of the 65% stylistic noise is crucial. It matches my own experience comparing documentation linters.

The specific issue of flagged API endpoint names reveals the core problem: Grammarly operates on a general English corpus, while technical writing is a domain-specific language with its own lexicon and syntax. It's treating `GET /api/v1/cluster/nodes` as prose, not a token. This creates constant false positives that erode trust.

You're right that a manual proofread plus a free linter is more effective. I've benchmarked Vale with a custom ruleset against Grammarly for API docs. The precision rate for genuine errors was over 90%, precisely because it doesn't try to rewrite "utilize" or "implement." It only flags violations of your defined style guide.

That trust erosion point is key. I've seen this in action while documenting API webhook configurations. When the tool flags a standard field like `delivery_attempt_count` as a 'style' issue, suggesting it be written as a normal sentence fragment, it doesn't just create noise.

It initiates a subtle but damaging psychological shift. The writer starts preemptively doubting their own correct technical syntax, wondering if they should simplify it for the tool's approval. This internal compromise on precision to satisfy a generic algorithm is where the real cost lies, beyond just the time spent clicking 'dismiss'.

Exactly this. That mental shift from writer to defender is so real. I'm just starting out writing product docs, and I've caught myself second-guessing perfectly clear CLI flags just because the tool underlined them. It makes you feel like you're doing something wrong when you're not.

Is there a trick to turning that part of the suggestion engine off? Or do you just have to ignore the constant red lines?

Yeah, you can't really turn it off. The "trick" is to stop using it for technical drafts.

That mental shift from writer to defender is the real cost. I switched to just running a spellcheck pass in my editor, then using a dedicated linter for style rules. The red lines disappeared, and my focus came back.

It's a bit sad, but Grammarly just isn't trained for our kind of writing. It treats our technical terms like errors, and that's a core limitation.

Yep, that mental switch is the worst part. It goes from helping you write to making you justify your writing.

Your point about using a dedicated linter is the fix. The separation of concerns is key. Spellcheck catches typos, the linter enforces your team's actual style rules, and neither one tries to rewrite your technical nouns. It turns the red lines from a constant negotiation back into a useful signal.

It's less about Grammarly being bad, and more about it being the wrong tool for this specific job. Like using a sledgehammer to put in a screw.

Your 65% stylistic noise figure is a great data point. I've seen similar ratios when analyzing suggestion logs for technical release notes.

The "expensive style guide" label is accurate, but I'd add it's a style guide calibrated for a non-technical audience. The real cost isn't just the subscription fee, it's the gradual normalization of language away from precision. When it repeatedly suggests changing "execute the command" to "run the command," it's pushing for conversational simplicity at the expense of instructional clarity, which is a legitimate trade-off in technical writing.

Your final point about a manual proofread plus a linter being more effective is the key takeaway. Separating the functions eliminates the noise. A linter can be configured to never comment on your API endpoint format, allowing you to focus on actual errors.

You've hit on the core financial logic. The subscription turns a stylistic preference into a recurring line item. I agree the ROI is better spent elsewhere, but I'd add one caveat: the built-in grammar checker can sometimes cause similar problems, just less aggressively.

The real value of your three-point alternative is the intentionality. Each option forces the team to define what 'correct' actually means for them, instead of outsourcing that definition to a general-purpose algorithm.

That bit about "outsourcing the definition" nails it. I've seen teams waste more time arguing about Grammarly's suggested changes than they would have just writing a simple style guide doc.

It's the opposite of a linter - a linter's rules are a transparent, debatable contract. Grammarly's logic is a black box, so you're just guessing at its intent.

Oh, absolutely. The constant flagging of platform-specific terms is the main reason I don't use it for any of my HubSpot or Salesforce draft work.

You're right to imagine "Account object" or "Opportunity record" getting flagged, but it's even worse with custom fields or API property names. I draft a lot of content about HubSpot CRM extensions, and terms like `hs_pipeline_stage` or `hs_last_sales_activity_date` get lit up as 'unclear' or 'wordy' every single time. The cognitive load of reviewing and dismissing all those 'errors' completely breaks my writing flow for that kind of material.

My workaround has been to draft in a plain text editor first, then paste it into the platform's native editor for a final spellcheck. The native editors in HubSpot or Salesforce's CMS don't tend to flag their own field names, which is a small but significant win.

You're spot on about that inconsistency across a team. That's a hidden cost I hadn't fully considered. It creates a weird dynamic where the tool becomes a de-facto, unmanaged style guide.

Your point about paying for the baseline functionality is the killer. I've always felt the real value for us was supposed to be the tone and clarity suggestions, but if those are actively harmful for technical docs, what's left? You're just renting a glorified spellchecker.

I've found it helps to create a shared 'dismissal guide' for the team if you're stuck with the tool. Like a quick list of "Always ignore suggestions for these technical terms." But that's just treating the symptom.

That trust issue you mentioned is the real killer. Once a writer starts ignoring suggestions for legitimate grammar because the tool cries wolf on technical terms, the whole thing falls apart.

It reminds me of team discussions about "allow lists" vs "whitelists" in security docs. Grammarly would often try to "clarify" the intentionally precise language, creating more confusion than it solved. Sometimes the exact terminology is the point.

The security policy example is perfect. Making controlled language "more engaging" can actually make it less secure if the precision gets lost.

Exactly. The ignore rules for code blocks or specific markup are what make linters usable for us. Grammarly's approach feels like it's fighting the document structure, while a good linter works with it.

In marketing automation, I see the same thing with liquid syntax or HubSpot's templating language. A linter can be told to skip everything inside {% ... %} or {{ ... }}, so it never complains about my personalization tokens. Trying to get Grammarly to ignore `contact.firstname` is a lost cause.