How many terms does the jargon remover know?

145, all documented in the tool's lexicon with a plain alternative and a note. The page states the count from the list itself rather than hardcoding it, and unit tests fail if two entries collide or an entry loses its note or replacement.

Does the tool understand my sentence?

No. It matches whole phrases against a fixed list, longest first, case-insensitively, and drops overlapping matches. It does not parse grammar, judge tone or measure whether the sentence became clearer — the change table and the visible notes are the whole of its reasoning.

Why did it suggest replacing a word that is correct in my field?

Because the list deliberately includes terms that are precise in one context and vague in another — robust, deprecated, idempotent, stipulate. Each of those entries says so in its note, and the substitution is a proposal you can decline. The tool never edits your text in place.

Why is my industry's jargon not flagged?

Because a curated list is a snapshot, not a dictionary. Internal project names, new coinages and field-specific vocabulary that the 145 entries do not contain will pass through untouched. The list is the tool's entire vocabulary, which is what makes every finding auditable.

What happens to words the list does not contain?

They are returned exactly as written. There is no fallback rule, no model and no generic simplification pass, so a text with no listed terms comes back byte-identical and the tool says that no listed terms were found rather than implying the writing is plain.

Jargon Remover

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Replacements

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What this tool is, and is not

The remover looks up 145 hand-written terms and substitutes the plain equivalent from a fixed table. It does not understand the sentence, it never paraphrases, and it never invents a word: a term not on the list leaves the text exactly as it was.

The replacement is a proposal, not a correction. Several entries are precise terms in the right context — "robust" in statistics, "idempotent" in computing, "deprecated" in an API changelog — so read the "when it fits" column before accepting a change, and keep the original where it is the accurate word.

The jargon remover replaces the 145 jargon terms on its documented list with a plain-language equivalent, lists every substitution with a note on when it fits, and leaves anything the list does not carry exactly as written.

What is Jargon Remover?

Jargon is usually not one word but a habit, and the honest way to remove a habit is a list you can read. This jargon remover ships a documented list of 145 terms drawn from four registers — workplace and corporate, technical, academic and legal, and marketing — each with a plain-language equivalent and a note explaining when the replacement fits and when the original is the better word. The list lives in the source as data, is counted on the page, and is covered by unit tests, so the claim about its size is checkable rather than rhetorical.

Matching follows the shared phrase engine, and the rules are simple enough to predict. A term is matched as a whole phrase, case-insensitively, with the longest entry winning where two could overlap, and overlapping matches dropped. That means leverage becomes use, synergy becomes cooperation, ideate becomes brainstorm, reach out becomes contact, and a term the list does not carry is never touched. Nothing is inferred: the tool does not decide that a sentence is too technical, does not measure reading level, and does not rewrite in plain language beyond substituting the entry it found. Where a substitution begins a sentence, the replacement is capitalised so the result still reads correctly.

Every change is listed before the output, with three columns: the term found, the plain alternative, and the note on when that alternative fits. That third column is the point of the tool. Robust is a precise word in statistics; idempotent is a precise word in computing; deprecated is a precise word in an API changelog; stipulate is a precise word in a contract. Each of those entries carries a note saying so, and the enforcer will happily report a substitution that you should decline. Word counts before and after are shown beside the list, so the size of the change is visible as well as its content.

Why a fixed list instead of a model? Because a fixed list can be audited. There is no training data, no network request, no probability and no drift between runs: the same input produces the same substitutions every time, and every one of them can be traced to a numbered entry in a file that ships with the tool. The trade-off is equally clear. A curated list is a snapshot of the jargon one team noticed, not a dictionary of everything a field says. Terms that arrived last year, internal project names, and vocabulary specific to your industry will pass through untouched — the tool cannot flag what it does not contain.

The other honest limitation is context. The matcher sees words, not sentences: it cannot tell that ten plays a different role in each of two paragraphs, that a phrase is inside a quotation and belongs to someone else, or that a technical audience needs the exact term. That is why the output is presented as a draft with a change log rather than as a corrected document, and why the notes tell you when to keep the original. The tool never edits your text in place; you copy what you want.

Used well, it is a fast pass over marketing copy, internal documents and anything that has accumulated vocabulary from a dozen meetings. Paste the text, read the change table from the top, accept the substitutions that improve the sentence, and keep the ones that carry a precise meaning. The full rewritten draft is available to copy or download if you want to read it as prose rather than as a diff.

Everything runs in this browser. Your text never leaves the device, nothing is stored between visits, and the list itself is loaded as part of the page.

How to use Jargon Remover

Paste the text; the tool matches it against the full published list as you type.
Read the change table, starting with the terms that appear most often.
Check the note for each entry — several terms are precise in some contexts and vague in others.
Compare the word counts before and after to see how much the text actually shrank.
Copy or download the plain-language draft, then read it through before using it.

When to use Jargon Remover vs related tools

Use this remover on drafts that have drifted into workplace or marketing vocabulary, and use its neighbours for the overlapping questions. The Wordy Phrase Simplifier targets padding and circumlocution rather than jargon, and the Cliché Detector catches stock phrases that have lost their force.

When the problem is a phrase the audience will not know rather than a word you want plainer, the Readability Grade Scorer measures how hard the text is overall, and the Style Guide Adherence Checker tests the copy against a named guide.

Privacy & Security

This tool runs entirely in your browser — no data ever leaves your device. There is no server round-trip, no upload, no logging, and no account required. Your input is processed locally using client-side JavaScript and is never stored, transmitted, or accessible to anyone else. When you close the tab, everything disappears.