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.
The workflow is intentionally sentence-level rather than draft-level. Editing the right sentences for twenty minutes moves the score more than editing the whole draft for an hour. The per-sentence highlights inside TextSight are the part that makes the difference.
Paste the draft into the AI Detector tab at app.textsight.ai and run a scan. Record the starting Authenticity Score and the AI detection percent. The single most common mistake is skipping the baseline; without it you cannot tell whether an edit actually moved the score or you imagined it did. Free tier covers three detector scans a day at 5,000 characters per scan, which is enough for one baseline plus two iterations on most pieces.
Open the result panel and read the sentence-level highlights. Every sentence is coloured red, amber, or green. Reds trip the strongest AI signal. Ambers are borderline. Greens are clear. Count the reds. A typical first-draft scan returns roughly 30 percent red, 40 percent amber, 30 percent green. Focus the next edit on the red sentences specifically; the greens almost never need work, and editing them is wasted effort that risks breaking sentences that were already fine.
Click any red or amber sentence to see which signals fired on it. Length too uniform, vocabulary cluster (delve, leverage, navigate), transition opener (Furthermore, Moreover), hedge density (it is important to note, various, somewhat), or structure (templated paragraph shape). Edit each sentence against its own evidence rather than running a blanket find-and-replace across the draft. A sentence flagged for length needs a length rework; running a vocabulary swap on it leaves the dominant signal untouched and the score barely moves.
Sentences that stay red after one manual edit usually have a structural problem rather than a surface one. They are too long, too templated, or too generic, and a word swap will not fix any of those. Select the stuck sentence, open the AI Rewriter tab, and pick a mode. Light preserves meaning closely. Standard is the default for general rewriting. Maximum is aggressive enough that it can shift claims, so reserve it for sentences you plan to fact-check after. Free tier covers 1500 AI rewriter words a month across all three modes.
Re-scan the edited draft. After one focused pass on the flagged sentences the Authenticity Score should rise by 15 to 30 points and the AI detection percent should fall by 25 to 45. If the delta is much smaller than that, you edited green sentences instead of red ones, or you applied the wrong fix to the right sentence. Reopen the highlights and target the reds specifically on the second pass. Two iterations are usually enough to clear publishing-grade targets.
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 single overall score does not tell you what to edit. It just tells you the average. Per-sentence highlights pinpoint the exact lines that trip the detector, which is the slowest part of the workflow if you have to guess.
A draft that scores 65 percent AI overall could be 100 percent AI in the first paragraph and 30 percent in the rest, or evenly mixed throughout. Those two drafts need very different edits. The first one needs the opening paragraph rewritten; the second needs a uniform pass on the whole piece. A single number cannot tell them apart. Sentence-level highlights can, which is why most writers who run TextSight report cutting editing time by roughly half against tools that show only the overall percent.
Click any red sentence and TextSight surfaces the dominant signal: length, vocab, transition, hedge, structure. That tells you which fix to apply. A sentence flagged for length needs a length rework; running a vocab swap on it moves the score by nothing because the dominant signal is untouched. Most plateaus on the way to a higher Authenticity Score come from editing the wrong signal repeatedly. The per-sentence evidence is the cheapest way to avoid that.
A typical workflow runs two passes. First pass: edit every red sentence using its own evidence. Re-scan. Second pass: edit the ambers that survived the first pass. Re-scan. After two passes the colour distribution usually flips from 30 percent red, 40 percent amber, 30 percent green into 5 percent red, 25 percent amber, 70 percent green. Three passes are rarely worth it; if the score will not move on a third pass, the underlying argument or topic is the limit, not the prose.
Sentences that stay red after one manual edit usually need a rewrite, not a word swap. The AI Rewriter tab inside TextSight offers three modes calibrated to different gaps. Pick the one that matches the sentence rather than running everything through Maximum.
Light is the safest first-pass setting and the right pick for a sentence you mostly trust. It varies length and swaps the most obvious vocabulary clusters but leaves the argument and the specific anchors alone. Score delta on a single sentence is usually 5 to 10 points up on the Authenticity scale. Right for sentences that need a polish, not a rebuild.
Standard is the default and covers most rewrites. It rebuilds rhythm, swaps corporate vocabulary, breaks up uniform sentence length, and cuts transition openers. Score delta on a single sentence is usually 10 to 20 points. Right for sentences flagged on two or more signals at once, which is the common case on a stuck red.
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. Reserve it for sentences that stay red after a Standard pass. Score delta on a single sentence is usually 20 to 35 points. Free tier covers 1500 AI rewriter words a month across all three modes; that is enough for two or three stubborn sentences per draft, which is the realistic need on a 1000-word piece.
Running the AI rewriter on the whole draft flattens the parts that were already fine. The green sentences come out smoother but lose some of the texture that made them green in the first place. Running it sentence by sentence on the reds preserves the rest of the draft and uses the monthly word budget more carefully, since the bucket is shared across all three modes regardless of which one you pick.
Free covers 3 detector scans a day, 1500 AI rewriter words a month, all three modes, and the sentence-level highlights that drive the workflow. Paid tiers raise the quotas and add the Chrome extension, file upload, REST API, and white-label reports. Yearly billing saves 25%.
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Treating the AI score as a verdict ("this draft is AI") is the wrong framing and leads to bad editing decisions. Treating it as a calibration tool ("which sentences need work") is the right one. The difference shapes how you read every scan.
Pure-human writing on a common topic typically scores 10 to 20 percent AI on every detector tested. The reason is that 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 problem to fix. Chasing zero often forces choppy sentences and over-specific anchors that read affected. Stop when the prose reads natural to you, not when the score caps out.
The most productive way to read a scan: the overall score tells you roughly how much editing the draft needs; the sentence-level highlights tell you where. Then ignore the overall score until the next re-scan. Trying to lower the headline number directly leads to the wrong edits because the headline is an aggregate; lowering it by 10 points on one paragraph is worth more than lowering it by 2 points on every paragraph.
The four patterns the score penalises (adjective stacks, transition clutter, uniform rhythm, corporate vocabulary) are the same four patterns that bore a human reader. Lifting the Authenticity Score from 50 to 85 almost always sharpens the writing at the same time. Treating the score as a writing-quality feedback loop, not a defensive shield, is what makes the workflow worth running on every draft instead of only the ones you ran through ChatGPT.
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.