When I started working on a grammar checking website, I thought the problem was mostly about syntax. I soon realized it was more about the tension between rules, meaning, and performance.
Looking at existing grammar checkers, each one reflects a different philosophy:
Grammarly focuses on communication optimization. It combines a large rule set with transformer-based contextual ranking, performing well for tone adaptation but sometimes normalizing unique writing styles.
LanguageTool leans on interpretable, rule-based patterns. Its XML-defined rules are easy to debug and extend, but struggle with nonstandard syntax or mixed-tone sentences.
DeepL Write emphasizes fluency, often rephrasing entire sentences rather than flagging discrete grammar points. It feels natural but can overwrite subtle author intent.
Studying these tools helped shape the approach for our own site. Every grammar checker has to balance precision, coverage, and interpretability.
For the website we eventually built—Grammar Checker—the goal was consistency and clarity, rather than scale or aggressive rewriting. Suggestions should be understandable, not dictatorial, so we avoided purely neural rewriting. At the same time, we didn’t want a rigid ruleset that fails on informal or evolving English.
The architecture became hybrid:
A rule layer for deterministic checks (agreement, punctuation, redundancy).
A contextual layer using sentence embeddings to detect softer signals, like ambiguity or inconsistent tone.
A confidence model to determine whether a suggestion should be displayed, based on both syntactic certainty and semantic stability.
A surprising part of the design was handling uncertainty. A grammar checker that never questions itself quickly becomes frustrating; one that questions everything becomes useless. Each suggestion carries a “linguistic confidence” score, which the frontend uses to adjust highlight intensity and phrasing.
UX considerations shaped the technical implementation. Users want signals, not enforcement. Highlight colors, opacity, and wording matter as much as model accuracy metrics.
Performance was another challenge. Transformer-based models improve contextual awareness but can introduce latency. We added caching for repeated patterns and lightweight fallback models for short inputs, while more complex checks run asynchronously in the background.
Looking at competitors again helped prioritize trade-offs. Grammarly optimizes for breadth, LanguageTool for transparency, DeepL Write for fluency. Grammar Checker doesn’t aim to compete at that scale—it focuses on reliability and interpretable feedback.
Ultimately, grammar checking isn’t just about correcting sentences—it’s about encoding judgment.
Every rule, model weight, and UI decision quietly answers the question:
“How confident are we that this writer didn’t intend it this way?”
The site surfaces suggestions thoughtfully, giving writers guidance without overstepping. It’s a technical challenge, but one that highlights how subtle and complex human language can be.
Top comments (3)
Brilliantly written this feels like part engineering case study, part linguistic philosophy. The way you framed grammar checking as encoding judgment perfectly captures the nuance between deterministic logic and interpretive meaning. Love how you emphasized confidence, context and UX empathy rare to see those balanced so elegantly in an AI build.
This is such a fascinating topic, and you’ve done an excellent job conveying the main ideas, especially impressive given how huge the subject is, yet this post is so brief and easy to read. There’s definitely much more to explore in depth and breadth, but this post still managed to answer a few questions I’ve always had. Especially how the layered architecture works. I’d definitely dive into that topic.
I’m also really curious about how different languages, with their unique principles and rules, are handled. For example, I know English and Georgian. These two languages are completely and fundamentally different. When I try to imagine creating something like this for both, I can’t see a single model, even with different configurations, working equally well for them. I think it might be necessary to build fundamentally different models for each language.
Worth reading