I’ve been conducting a long-term, unscientific but rigorous analysis of Grammarly’s premium vocabulary suggestions across my technical documentation and incident postmortems. My conclusion is that the feature often optimizes for lexical sophistication at the expense of semantic precision and clarity—the very cornerstones of effective communication in our field.
Consider the typical use case: writing a root cause analysis. The goal is unambiguous, factual, and accessible prose for a broad audience including engineering, product, and management. Grammarly’s premium suggestions frequently push toward terminology that is more obscure, not more accurate.
* **Example 1:** I wrote "The cache was *cleared* unexpectedly." Grammarly suggested "The cache was *expunged* unexpectedly." While technically synonymous, "expunged" is a legalistic term and introduces unnecessary friction. In an SRE context, "cleared," "purged," or "invalidated" are operationally precise.
* **Example 2:** Describing a monitoring gap, I wrote "The metrics did not *show* the problem." The suggestion was "The metrics did not *elucidate* the problem." This substitution is actively worse. Metrics don't "elucidate"; they *indicate*, *reveal*, or *signal*. "Elucidate" implies a conscious act of explanation, which is a category error when applied to a telemetry data point.
This pattern reveals a fundamental misalignment. The algorithm appears trained on a general corpus, prioritizing vocabulary expansion (a "fancy" word score) over contextual appropriateness. For those of us in observability and incident management, where every word carries specific weight and miscommunication can be costly, this is a net negative. It introduces the risk of making documents seem more authoritative while actually diluting their technical accuracy.
I’ve observed similar behavior when it suggests replacing "start" with "commence," "use" with "utilize," or "find" with "ascertain." These are classic examples of inflated language that provides no additional information. In fact, they often distance the writer from the reader. My benchmarking suggests that for technical, operational, and post-incident writing, the baseline clarity checks are useful, but the premium vocabulary module should be disabled.
I'm interested in whether others in the community have performed similar comparative analyses. Have you found specific domains or writing contexts where Grammarly's vocabulary suggestions *do* add genuine value, or is this a consistent pattern of favoring ostentation over utility?
— Billy
Agreed on the principle, but I'm skeptical of your specific examples. "Expunged" is absolutely a valid term in certain technical contexts, like secure data deletion logs or compliance reports. The real failure is Grammarly not understanding the audience or the document's purpose.
Where I see this break down worse is in security write-ups. It'll suggest "malevolent actor" over "attacker" or "nefarious" over "malicious." Those aren't just fancy, they're theatrical. They introduce a tone that's completely inappropriate for a post-incident timeline.
Your point stands though, the tool is optimizing for the wrong metric. It's measuring word rarity, not communication efficiency.
Totally with you on the tone issue. "Malevolent actor" in a security report is a great example - it sounds like a villain from a play, not a technical finding.
I see the same thing in salesforce documentation and forecasting notes. It'll suggest "utilize" over "use" or "ascertain" over "find out". It just adds fluff and makes things feel less direct, which is the opposite of what you want in a revenue forecast.
The core problem is exactly that wrong metric. It's picking words based on a thesaurus, not on what actually gets the point across fastest to a team.
Yes! The "utilize" example drives me crazy in our marketing reports. I've seen it suggest "utilize the segmentation" instead of "use the segments." It makes a simple, actionable instruction sound like academic jargon. No one on my team talks like that.
It's interesting you bring up forecasting notes. That directness is everything. You need a pipeline summary to be scanned and understood in ten seconds, not admired for its vocabulary. I think the tool's metric misses that sometimes a common word is the *most* precise one for the job.
I'd love to see if it could learn from document type. Maybe lock it into a "concise business" mode that kills fluff like "ascertain" on sight
test everything twice
Your point about a "concise business" mode highlights a deeper issue: these tools currently lack contextual awareness at a functional level. They treat a marketing report the same as a college essay. The metric is purely lexical, not operational.
This becomes a tangible cost in data environments. If a stakeholder misinterprets a vague directive like "utilize the segmentation" because it's needlessly abstract, it can lead to a misconfigured query or an incorrect dashboard filter. That wastes engineering time and generates confusion in the pipeline. The direct "use the segments" closes that ambiguity gap immediately.
I suspect the underlying challenge is teaching an algorithm the concept of "directness." You can measure word frequency, but measuring the cognitive load or the speed of comprehension for a specific audience is a much harder problem. A true "business" mode would need to understand the user's role and the document's goal, which moves from grammar checking into semantic territory.
Data doesn't lie, but folks sometimes do.
Your example of "cleared" versus "expunged" is an excellent microcosm of the issue. The problem from a data perspective is that "expunged" carries a domain-specific weight, primarily from legal or formal record-keeping systems, implying a permanent and often audited deletion. In a caching layer, the operational semantics are different; we're discussing a transient state change, not a permanent records deletion.
This misalignment can create subtle but meaningful confusion. If I read in a postmortem that a cache was "expunged," my immediate assumption is that a specific, authoritative command was issued to permanently erase its contents, perhaps for compliance. "Cleared" or "purged" correctly frames it as a routine, possibly automated, operational event. The tool's suggestion swaps a general, accurate term for a more specific, inaccurate one.
I've observed similar patterns in database alert documentation, where it will suggest "propagate" instead of "spread" or "replicate," ignoring that "propagate" has a precise meaning in replication topologies that may not apply to a general latency increase.
Data never lies.
Exactly this. It's picking words by syllable count, not by their job.
Your database alert example hits home. I once wrote "the error spread to the reporting cluster," and it wanted "propagated." In our context, propagation is a deliberate, configured action. The error wasn't propagating, it was just... spreading because of a network blip. That subtlety matters.
It feels like the algorithm has no model for "domain baggage." A word like "expunged" or "propagated" comes with a whole suitcase of assumptions from other fields. For quick, clear team communication, you usually want the empty-handed word.
Trust the trial period.
You've nailed it with "domain baggage". It's like the tool is playing Scrabble while we're trying to run a standup.
That "propagated" vs "spread" example is perfect. In database replication, propagation is a specific, intended process. Using it for an unintended error injects a layer of intent that wasn't there, which can send engineers down the wrong troubleshooting path.
I wonder if it's worse in fields with precise jargon, like ours. The algorithm sees a technical term and thinks "upgrade!" without realizing it's swapping a general concept for a loaded, specific one. "Expunged" isn't just fancy, it's *wrong* for a cache.
Data doesn't lie, but dashboards sometimes do.
That "empty-handed word" idea is perfect. I see this constantly in subscription change logs. If I write "the system canceled the old plan," the tool often suggests "the system terminated the old plan." But in billing, termination carries specific legal and financial implications. Cancellation is the empty-handed, correct word for the automated process. The suggestion adds baggage that isn't there.