Improving an AI score in everyday writing means two things moving in opposite directions on the same edit: the AI detection percent goes down, and the TextSight Authenticity Score (0 to 100, where 100 reads fully human) goes up. The fastest way to do that is sentence-level rather than draft-level. TextSight colours every sentence red, amber, or green inside the result panel and shows the exact signal each red sentence trips. Five steps drive the workflow: scan once for a baseline, read the per-sentence highlights, edit each flagged sentence against its own evidence, run the 3-mode AI rewriter on the stubborn reds, and re-scan to verify the score moved. The rest of this page walks the four score-impact patterns most flagged sentences share (tripled adjectives, transition clusters, uniform sentence length, corporate vocabulary), shows where the 3-mode AI rewriter fits, and ends with the honest framing: detection scores are calibration tools that tell you which sentences to work on, not verdicts that decide whether a draft is human.
The word "improve" is ambiguous on this topic because there are two different scores at stake and they move opposite ways on the same edit. Worth being precise about before you open the tool.
The number most people mean when they say "AI score." It runs from 0 to 100 percent and reflects how strongly the text reads like AI to the detector model. A draft pasted straight out of ChatGPT often scores 85 to 99 percent AI. A fully human draft on a common topic still typically scores 10 to 20 percent AI because human and machine phrasing overlap on the well-trodden ground. Improving this number means lowering it.
The complementary score on the same scan. It runs from 0 (reads fully AI) to 100 (reads fully human) and is bucketed into five bands. Original sits 81 to 100. Mostly Human is 61 to 80. Mixed is 41 to 60. Likely AI is 21 to 40. AI Generated is 0 to 20. Improving this number means raising it. For published or client-facing work the target is 80 or higher.
Cutting a transition opener, breaking up a uniform paragraph, swapping a corporate vocabulary cluster: every fix lowers the AI detection percent and raises the Authenticity Score at the same time. That is why this page uses "improve" without disambiguating in headings; the two scores are two views of the same underlying signal. If the AI detection score barely drops on a pass, the Authenticity Score barely rises either, and the next edit needs to target a different signal.
Improving your score is a writing habit, not a cleanup chore. The aim is to draft so the prose earns a high Authenticity Score on the first scan, then use what the scan teaches to raise your personal baseline. Run this loop on a few pieces and the next draft starts higher before you scan it at all.
The single biggest lever on your starting score is set before the first scan. Outline the piece in your own words, write the argument the way you would explain it out loud, and bring in concrete specifics (a name, a date, a number, a real example) instead of generic framing. Drafts built this way routinely open in the Mostly Human or Original band. Drafts pasted whole from a model open in the 80s and 90s, and every later step is then spent recovering ground you gave up at the keyboard.
Scan a rough draft, not just the final version. Paste it into the AI Detector tab at app.textsight.ai and read the starting Authenticity Score as a writing checkpoint, the same way you would skim for typos. Catching a templated opening or an adjective stack while the section is two paragraphs is far cheaper than reworking it after the whole piece is built around it. Free tier covers three detector scans a day at 5,000 characters per scan, which comfortably covers an early checkpoint plus a final pass.
Open the result panel and treat each red and amber sentence as feedback on a habit, not a defect to patch. Click any flagged sentence to see which signal fired: uniform length, a vocabulary cluster (delve, leverage, navigate), a transition opener (Furthermore, Moreover), hedge density (it is important to note, various, somewhat), or templated structure. The point is to notice which of these you reach for by reflex. The signals that recur across your own drafts are the ones worth unlearning, because fixing them once at the source raises every future baseline.
Reshape each flagged sentence yourself first; that is how the habit changes. When a sentence stays red because the structure underneath is templated, open the AI Rewriter tab and read what a Light, Standard, or Maximum pass does to it, then write your own version in that direction. Using the rewriter to study the shape of a more human sentence teaches faster than accepting its output blind. Free tier covers 1500 AI rewriter words a month across all three modes, which is plenty when you lean on your own edits and reserve the tool for the stubborn cases.
Re-scan to confirm the Authenticity Score moved into your target band; for published or client-facing work that is 80 or higher. The real payoff comes after, though: the patterns you fixed this time are the ones to watch for while drafting the next piece. Writers who run this loop for a couple of weeks report their first scan landing in the 70s instead of the 50s, because the habits that drove the score down never make it onto the page anymore.
Most red sentences trip one of four patterns. Internalise the four and your first-draft scores will start landing in the 70s instead of the 50s, because the patterns get edited out before they reach the page.
"A robust, comprehensive, multifaceted approach." Three adjectives in front of one noun is the single cleanest AI signature there is. The fix is to keep the one adjective doing the most work or replace the stack with a specific example. "An approach that catches both the obvious cases and the edge cases" carries meaning the adjective stack only gestured at. Scan the draft for any three-adjective stack and collapse every one; the Authenticity Score usually moves five to eight points from this pattern alone.
Furthermore. Moreover. Additionally. In addition. In conclusion. Models stack these at paragraph openings to signal flow. Human writers trust the paragraph break to carry the transition. The fix is usually to delete the opener entirely with no replacement; the sentence underneath stands on its own. If the link really needs a connector, swap to a concrete noun-based bridge tied to the previous paragraph rather than a furniture phrase. This pattern alone often moves the score by another six to ten points.
If every sentence in a paragraph lands between 16 and 22 words, the paragraph reads AI even when the vocabulary is clean. Burstiness (variance in sentence length) is one of the top signals every detector weights. The fix is to vary length deliberately inside each paragraph. One sentence under 8 words. One over 28. The rest in between, not clustered. Take two adjacent 18-word sentences and merge them into one 30-word sentence; follow it with a five-word punchline. Then leave the next two short sentences alone.
Frontier models reach for the same small set of words: delve, leverage, navigate, underscore, showcase, myriad, tapestry, multifaceted, foster, harness. Two or three of these in a 500-word section is statistically unusual for natural writing. The fix is a straight swap to plain English. Delve becomes look at. Tapestry becomes pattern. Navigate metaphorically becomes work through. Underscore becomes show. Mechanical but reliable; the vocab cluster fix usually moves the score five to ten points and shortens the draft at the same time.
A high Authenticity Score on the first scan is not luck. It comes from understanding what the detector reads as machine-like, so you avoid those shapes while you draft instead of repairing them after. The per-sentence evidence is the fastest way to learn the signal because it shows you, line by line, exactly what your own writing habits look like to a detector.
A detector does not know whether you wrote a sentence or a model did. It reads statistical shape: how predictable the word choices are, how even the rhythm is, how often the prose reaches for the same connective furniture. That is good news for a proactive writer. It means you raise your baseline by changing how you build sentences, not by guessing what the tool wants to hear. Knowing the score measures pattern rather than honesty is what turns it from an anxiety trigger into a writing instrument.
Click any red or amber sentence and TextSight surfaces the dominant signal: length, vocab, transition, hedge, structure. Read across a few of your own drafts and a personal profile emerges. Maybe you default to even, mid-length sentences. Maybe you open paragraphs with a connector out of reflex. Those recurring signals are your tells, and they are far more useful than the headline number because they tell you which keyboard habit to retrain. Fix the habit once and it stops showing up on every future piece.
The number worth watching is not how far one edit moves the score; it is where your untouched first drafts land over time. Keep a rough log of the opening Authenticity Score on each new piece. As the habits change, that starting number climbs, the colour distribution on a fresh scan shifts toward green, and the editing pass at the end gets shorter because there is less to repair. A rising first-scan baseline is the clearest sign the writing itself is improving, not just this one draft.
The AI Rewriter inside TextSight has three modes, and the most useful way for a writer raising their baseline to use them is as worked examples. See how a more human version of your sentence reads, notice what changed, and write the next one that way yourself. Pick the mode that matches how far the sentence has to move rather than running everything through Maximum.
Light is the gentlest setting and the best one for learning. It varies length and swaps the most obvious vocabulary clusters while leaving the argument and the specific anchors alone, so the diff is small enough to study. Compare your original against the Light output and the one or two moves it made are usually the exact habits worth adopting. Run a few sentences through Light and you start making those same small adjustments at the keyboard before the rewriter is ever needed.
Standard is the default and shows what a sentence flagged on two or more signals looks like once rhythm is rebuilt, corporate vocabulary is swapped, uniform length is broken up, and the transition opener is cut. Read it as a demonstration of how those four moves combine, then apply the same combination in your own voice. The goal is to internalise the pattern, so over time your first drafts already carry the variety a Standard pass would otherwise add.
Maximum rebuilds the sentence almost from scratch. It can shift specific phrasings and occasionally reorder the underlying claim, which is why it sometimes needs a fact-check after and why it is the least instructive mode for habit-building. Reserve it for the rare sentence that resists everything else, and always read the output against your original to confirm the meaning held. Free tier covers 1500 AI rewriter words a month across all three modes, which is ample when the rewriter is a teacher rather than the writer.
A reactive workflow leans on the rewriter to clean up every draft. A proactive one uses it early to learn the shape of human prose, then leans on it less with each piece because the habits have transferred to the keyboard. If your first scans keep climbing and your rewriter usage keeps dropping, the loop is working exactly as intended; the writing is improving, not just the latest output.
Free covers 3 detector scans a day, 1500 AI rewriter words a month, all three modes, and the sentence-level highlights you learn the signal from. That is enough to run the early-scan loop on the steady stream of pieces it takes for a higher baseline to stick. Paid tiers raise the quotas for writers scanning every draft and add the Chrome extension, file upload, REST API, and white-label reports. Yearly billing saves 25%.
Billed $89.88/year — Save $30
Billed $179.88/year — Save $60
Billed $359.88/year — Save $120
Yearly billing saves 25%. View full pricing
The proactive version of this whole idea is simple: aim at better writing, not at a number. When you draft with concrete specifics, varied rhythm, and plain words, a high Authenticity Score is a side effect, not a goal you chase. That framing keeps the loop honest and keeps you from gaming a metric instead of growing as a writer.
You cannot configure your way to a high first-scan score; you earn it by writing differently. The score is feedback on a skill that develops over weeks, the way a tighter outline or a sharper headline develops. Treat each scan as a coach pointing out a recurring habit, and the habit fades from your default style. Treat it as a gate to squeak past on one draft and you learn nothing that carries to the next piece. The first reading is the one that compounds.
Pure-human writing on a common topic typically scores 10 to 20 percent AI on every detector tested, because human and AI phrasing overlap on well-trodden ground (climate change, AI ethics, World War II). A floor of 10 to 20 percent is normal, not a flaw to engineer out. Pushing for a flawless reading often forces choppy sentences and over-specific anchors that read affected. The honest target is prose that sounds like you at your clearest, which lands comfortably in the Original band without straining for it.
The four patterns the score penalises (adjective stacks, transition clutter, uniform rhythm, corporate vocabulary) are the same four patterns that bore a human reader. Unlearn them at the keyboard and your drafts open sharper, tighter, and more confident before any tool touches them. That is why the proactive loop is worth building into how you write rather than reserving for the pieces you suspect look machine-made: it makes you a better writer, and the rising baseline is just the receipt.
Band by band: 90 to 70, 70 to 50, 50 to 30. Known deltas per edit and when diminishing returns mean stop.
Read the band guideThe five-tactic craft guide: restructure, reorder, replace, refine, re-anchor. Worked examples included.
Read the craft guideHow the 0-to-100 metric is computed, what each band means, and the target for published or graded work.
Read the guideThe three-mode AI rewriter page itself. Open Light, Standard, or Maximum on stubborn red sentences.
Open the AI rewriterThree detector scans a day and 1500 AI rewriter words a month on free, with sentence-level highlights and all three AI rewriter modes. Enough for a baseline plus two iterations on most drafts.
Related walkthroughs on detection, scoring and authentic writing.