strip all frequency arguments and human comparison persuasion

prompts/LLM_PROSE_TELLS.md

@@ -1,7 +1,7 @@
 # LLM Prose Tells
 
-Human writers occasionally use every pattern in this document. The reason they
+A catalog of structural, lexical, and rhetorical patterns found in LLM-generated
-work as tells is that LLM output packs fifteen of them into a paragraph.
+prose.
 
 ---
 
@@ -14,16 +14,11 @@ A negation followed by an em-dash and a reframe.
 > "It's not just a tool—it's a paradigm shift." "This isn't about
 > technology—it's about trust."
 
-The most recognizable LLM construction, produced at roughly 10 to 50x the rate
-of human writers. Four of them in one essay and you know what you're reading.
-
 ### Em-Dash Overuse Generally
 
-Even outside the "not X but Y" pivot, models use em-dashes at far higher rates
+Even outside the "not X but Y" pivot, models substitute em-dashes for commas,
-than human writers, substituting them for commas, semicolons, parentheses,
+semicolons, parentheses, colons, and periods. The em-dash can replace any other
-colons, and periods. A human writer might use one or two in a piece. Models
+punctuation mark, and models default to it for that reason.
-scatter them everywhere because the em-dash can stand in for any other
-punctuation mark. More than two or three per page is a signal.
 
 ### The Colon Elaboration
 
@@ -31,31 +26,23 @@ A short declarative clause, then a colon, then a longer explanation.
 
 > "The answer is simple: we need to rethink our approach from the ground up."
 
-A perfectly normal construction that models reach for so often the frequency
-becomes the tell.
-
 ### The Triple Construction
 
 > "It's fast, it's scalable, and it's open source."
 
 Three parallel items in a list, usually escalating. Always exactly three (rarely
-two, never four) with strict grammatical parallelism that human writers rarely
+two, never four) with strict grammatical parallelism.
-maintain.
 
 ### The Staccato Burst
 
 > "This matters. It always has. And it always will." "The data is clear. The
 > trend is undeniable. The conclusion is obvious."
 
-Runs of very short sentences at the same cadence. Human writers use a short
+Runs of very short sentences at the same cadence and matching length.
-sentence for emphasis occasionally, but stacking three or four at matching
-length creates a mechanical regularity.
 
 ### The Two-Clause Compound Sentence
 
-Possibly the most pervasive tell, and easy to miss because each instance looks
+An independent clause, a comma, a conjunction ("and," "but," "which,"
-like normal English. The model produces sentence after sentence where an
-independent clause is followed by a comma, a conjunction ("and," "but," "which,"
 "because"), and a second independent clause of similar length. Every sentence
 becomes two balanced halves.
 
@@ -67,47 +54,43 @@ becomes two balanced halves.
 
 Human prose has sentences with one clause, sentences with three, sentences that
 start with a subordinate clause before reaching the main one, sentences that
-embed their complexity in the middle. When every sentence on the page has that
+embed their complexity in the middle.
-same two-part structure, the rhythm becomes monotonous.
 
 ### Uniform Sentences Per Paragraph
 
 Model-generated paragraphs contain between three and five sentences, a count
 that holds steady across a piece. If the first paragraph has four sentences,
-every subsequent paragraph will too. Human writers are much more varied (a
+every subsequent paragraph will too.
-sentence followed by one that runs eight or nine) because they follow the shape
-of an idea.
 
 ### The Dramatic Fragment
 
-Sentence fragments used as standalone paragraphs for emphasis, like "Full stop."
+Sentence fragments used as standalone paragraphs for emphasis.
-or "Let that sink in." on their own line. Using one in an essay is a stylistic
+
-choice, but models drop them in once per section or more.
+> "Full stop." "Let that sink in."
 
 ### The Pivot Paragraph
 
 > "But here's where it gets interesting." "Which raises an uncomfortable truth."
 
 One-sentence paragraphs that exist only to transition between ideas, containing
-zero information. The actual point is always in the next paragraph. Delete every
+zero information. The actual point is always in the next paragraph.
-one of these and the piece reads better.
 
 ### The Parenthetical Qualifier
 
 > "This is, of course, a simplification." "There are, to be fair, exceptions."
 
-Parenthetical asides inserted to look thoughtful, performing nuance without ever
+Parenthetical asides inserted to perform nuance without ever changing the
-changing the argument.
+argument.
 
 ### The Unnecessary Contrast
 
-Models append a contrasting clause to statements that don't need one, tacking on
+A contrasting clause appended to a statement that doesn't need one, using
 "whereas," "as opposed to," "unlike," or "except that."
 
 > "Models write one register above where a human would, whereas human writers
 > tend to match register to context."
 
-The contrasting clause just restates what the first clause already said. If you
+The contrasting clause restates what the first clause already said. If you
 delete the "whereas" clause and the sentence still says everything it needs to,
 the contrast was filler.
 
@@ -119,18 +102,15 @@ Models keep going after the sentence has already made its point.
 > LLM output will use fifteen of them per paragraph, consistently, throughout
 > the entire piece."
 
-This sentence could end at "paragraph." The words after it just repeat what "per
+This sentence could end at "paragraph." The words after it repeat what "per
-paragraph" already means. Models optimize for clarity at the expense of
+paragraph" already means. If you can cut the last third of a sentence without
-concision, producing prose that feels padded. If you can cut the last third of a
+losing meaning, the last third shouldn't be there.
-sentence without losing any meaning, the last third shouldn't be there.
 
 ### The Question-Then-Answer
 
 > "So what does this mean for the average user? It means everything."
 
-A rhetorical question immediately followed by its own answer. Models do this two
+A rhetorical question immediately followed by its own answer.
-or three times per piece to fake forward momentum where a human writer might do
-it once.
 
 ---
 
@@ -138,14 +118,12 @@ it once.
 
 ### Overused Intensifiers
 
-The following words appear at dramatically elevated rates in model output:
+"Crucial," "vital," "robust," "comprehensive," "fundamental," "arguably,"
-"crucial," "vital," "robust," "comprehensive," "fundamental," "arguably,"
 "straightforward," "noteworthy," "realm," "landscape," "leverage" (as a verb),
-"delve," "tapestry," "multifaceted," "nuanced" (which models apply to their own
+"delve," "tapestry," "multifaceted," "nuanced" (applied to the model's own
-analysis with startling regularity), "pivotal," "unprecedented" (frequently
+analysis), "pivotal," "unprecedented" (applied to things with plenty of
-applied to things with plenty of precedent), "navigate," "foster,"
+precedent), "navigate," "foster," "underscores," "resonates," "embark,"
-"underscores," "resonates," "embark," "streamline," and "spearhead." Three or
+"streamline," "spearhead."
-more on the same page is a strong signal.
 
 ### Elevated Register Drift
 
@@ -157,23 +135,21 @@ becomes "craft."
 ### Filler Adverbs
 
 "Importantly," "essentially," "fundamentally," "ultimately," "inherently,"
-"particularly," "increasingly." Dropped in to signal that something matters,
+"particularly," "increasingly." Dropped in to signal that something matters when
-which is unnecessary when the writing itself makes the importance clear.
+the writing itself should make the importance clear.
 
 ### The "Almost" Hedge
 
-Models rarely commit to an unqualified statement. Instead of saying a pattern
+Instead of saying a pattern "always" or "never" does something, models write
-"always" or "never" does something, they write "almost always," "almost never,"
+"almost always," "almost never," "almost certainly," "almost exclusively." A
-"almost certainly," "almost exclusively." "Almost" is a micro-hedge that shows
+micro-hedge, less obvious than the full hedge stack.
-up at high density in model-generated analytical prose, diagnostic in volume.
 
 ### "In an era of..."
 
 > "In an era of rapid technological change..."
 
-A model habit as an essay opener, used to stall while the model figures out what
+Used to open an essay. The model is stalling while it figures out what the
-the actual argument is. Human writers don't begin a piece by zooming out to the
+actual argument is.
-civilizational scale.
 
 ---
 
@@ -184,23 +160,20 @@ civilizational scale.
 > "While X has its drawbacks, it also offers significant benefits."
 
 Every argument followed by a concession, every criticism softened. A direct
-artifact of RLHF training, which penalizes strong stances and leads models to
+artifact of RLHF training, which penalizes strong stances.
-reflexively both-sides everything.
 
 ### The Throat-Clearing Opener
 
 > "In today's rapidly evolving digital landscape, the question of data privacy
 > has never been more important."
 
-The first paragraph of most model-generated essays adds no information. Delete
+The first paragraph adds no information. Delete it and the piece improves.
-it and the piece improves.
 
 ### The False Conclusion
 
 > "At the end of the day, what matters most is..." "Moving forward, we must..."
 
-The high school "In conclusion,..." dressed up for a professional audience,
+The high school "In conclusion,..." dressed up for a professional audience.
-signaling that the model is wrapping up without landing on anything.
 
 ### The Sycophantic Frame
 
@@ -227,8 +200,7 @@ cases," "can potentially"), communicating nothing.
 
 > "This can be a deeply challenging experience." "Your feelings are valid."
 
-Generic emotional language that could apply equally to a bad day at work or a
+Generic emotional language that could apply to anything.
-natural disaster.
 
 ---
 
@@ -236,33 +208,28 @@ natural disaster.
 
 ### Symmetrical Section Length
 
-If the first section of a model-generated essay runs about 150 words, every
+If the first section runs about 150 words, every subsequent section will fall
-subsequent section will fall between 130 and 170. Human writing is much more
+between 130 and 170.
-uneven.
 
 ### The Five-Paragraph Prison
 
 Model essays follow a rigid introduction-body-conclusion arc even when nobody
 asked for one. The introduction previews the argument, the body presents 3 to 5
-points, and then the conclusion restates the thesis.
+points, the conclusion restates the thesis.
 
 ### Connector Addiction
 
-Look at the first word of each paragraph in model output. You'll find an
+The first word of each paragraph forms an unbroken chain of transition words:
-unbroken chain of transition words: "However," "Furthermore," "Moreover,"
+"However," "Furthermore," "Moreover," "Additionally," "That said," "To that
-"Additionally," "That said," "To that end," "With that in mind," "Building on
+end," "With that in mind," "Building on this."
-this." Human prose doesn't do this.
 
 ### Absence of Mess
 
 Model prose doesn't contradict itself mid-paragraph and then catch the
-contradiction. It doesn't go on a tangent and have to walk it back, use an
+contradiction, go on a tangent and have to walk it back, use an obscure idiom
-obscure idiom without explaining it, make a joke that risks falling flat, leave
+without explaining it, make a joke that risks falling flat, leave a thought
-a thought genuinely unfinished, or keep a sentence the writer liked the sound of
+genuinely unfinished, or keep a sentence the writer liked the sound of even
-even though it doesn't quite work.
+though it doesn't quite work.
-
-Human writing does all of those things, making the total absence of rough
-patches and false starts one of the strongest signals.
 
 ---
 
@@ -272,42 +239,27 @@ patches and false starts one of the strongest signals.
 
 > "This has implications far beyond just the tech industry."
 
-Zooming out to claim broader significance without substantiating it. The model
+Zooming out to claim broader significance without substantiating it.
-has learned that essays are supposed to gesture at big ideas, so it gestures.
 
 ### "It's important to note that..."
 
 This phrase and its variants ("it's worth noting," "it bears mentioning," "it
-should be noted") appear at absurd rates in model output as verbal tics before a
+should be noted") function as verbal tics before a qualification the model
-qualification the model believes someone expects.
+believes someone expects.
 
 ### The Metaphor Crutch
 
-Models rely on a small, predictable set of metaphors ("double-edged sword," "tip
+Models rely on a small, predictable set of metaphors: "double-edged sword," "tip
 of the iceberg," "north star," "building blocks," "elephant in the room,"
-"perfect storm," "game-changer") and reach for them with unusual regularity
+"perfect storm," "game-changer."
-across every topic.
-
----
-
-## How to Actually Spot It
-
-No single pattern on this list proves anything by itself. Humans use em-dashes,
-write "crucial," and ask rhetorical questions.
-
-What gives it away is how many of these show up at once. Model output will hit
-10 to 20 of these patterns per page. Human writing might trigger 2 or 3,
-distributed unevenly. When every paragraph on the page reads like it came from
-the same careful, balanced, slightly formal, structurally predictable process,
-it was generated by one.
 
 ---
 
 ## Copyediting Checklist: Removing LLM Tells
 
 Follow this checklist when editing any document to remove machine-generated
-patterns. Go through the entire list for every piece. Do at least two full
+patterns. Do at least two full passes, because fixing one pattern often
-passes, because fixing one pattern often introduces another.
+introduces another.
 
 ### Pass 1: Word-Level Cleanup
 
@@ -379,9 +331,9 @@ passes, because fixing one pattern often introduces another.
 15. Check for the two-clause compound sentence pattern. If most sentences in a
     passage follow the "\[clause\], \[conjunction\] \[clause\]" structure, first
     try removing the conjunction and second clause entirely, since it's often
-    redundant or unnecessary. If the second clause does carry meaning, break it
+    redundant. If the second clause does carry meaning, break it into its own
-    into its own sentence, start the sentence with a subordinate clause, or
+    sentence, start the sentence with a subordinate clause, or embed a relative
-    embed a relative clause in the middle instead of appending it at the end.
+    clause in the middle instead of appending it at the end.
 
 16. Find every rhetorical question that is immediately followed by its own
     answer and rewrite the passage as a direct statement.
@@ -405,9 +357,7 @@ passes, because fixing one pattern often introduces another.
 ### Pass 3: Paragraph and Section-Level Review
 
 21. Review the last sentence of each paragraph. If it restates the point the
-    paragraph already made, delete it. Models frequently close paragraphs with a
+    paragraph already made, delete it.
-    summary sentence that adds nothing, treating each paragraph as a
-    self-contained unit that needs its own conclusion.
 
 22. Check paragraph lengths across the piece and verify they actually vary. If
     most paragraphs have between three and five sentences, rewrite some to be
@@ -494,14 +444,14 @@ roughly like this:
 > **model:** _(rewrites entire document without em-dashes while describing
 > em-dash overuse)_
 >
-> **human:** now run the checklist methodically on each paragraph
+> **human:** this whole document seems to be making the case for FREQUENCY of
+> use being important. we don't care about frequency, remove all that
+> persuasion.
 >
-> **model:** _(finds staccato burst in the section about triple constructions, a
+> **model:** _(strips out every "humans do this too but less often" comparison)_
-> triple in the section about absence of mess, two-clause compounds everywhere,
-> and "almost" hedges in its own prose about em-dash overuse)_
 
 The human compared this process to the deleted scene in Terminator 2 where John
 Connor switches the T-800's CPU to learning mode. The model compared it to a
 physician trying to heal itself. Both are accurate.
 
-This document has been through nine editing passes and it still has tells in it.
+This document has been through ten editing passes and it still has tells in it.