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
-work as tells is that LLM output packs fifteen of them into a paragraph.
+A catalog of structural, lexical, and rhetorical patterns found in LLM-generated
+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
-than human writers, substituting them for commas, semicolons, parentheses,
-colons, and periods. A human writer might use one or two in a piece. Models
-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.
+Even outside the "not X but Y" pivot, models substitute em-dashes for commas,
+semicolons, parentheses, colons, and periods. The em-dash can replace any other
+punctuation mark, and models default to it for that reason.
 
 ### 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
-maintain.
+two, never four) with strict grammatical parallelism.
 
 ### 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
-sentence for emphasis occasionally, but stacking three or four at matching
-length creates a mechanical regularity.
+Runs of very short sentences at the same cadence and matching length.
 
 ### The Two-Clause Compound Sentence
 
-Possibly the most pervasive tell, and easy to miss because each instance looks
-like normal English. The model produces sentence after sentence where an
-independent clause is followed by a comma, a conjunction ("and," "but," "which,"
+An independent clause, 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
-same two-part structure, the rhythm becomes monotonous.
+embed their complexity in the middle.
 
 ### 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
-sentence followed by one that runs eight or nine) because they follow the shape
-of an idea.
+every subsequent paragraph will too.
 
 ### The Dramatic Fragment
 
-Sentence fragments used as standalone paragraphs for emphasis, like "Full stop."
-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.
+Sentence fragments used as standalone paragraphs for emphasis.
+
+> "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
-one of these and the piece reads better.
+zero information. The actual point is always in the next paragraph.
 
 ### 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
-changing the argument.
+Parenthetical asides inserted to perform nuance without ever changing the
+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
-paragraph" already means. Models optimize for clarity at the expense of
-concision, producing prose that feels padded. If you can cut the last third of a
-sentence without losing any meaning, the last third shouldn't be there.
+This sentence could end at "paragraph." The words after it repeat what "per
+paragraph" already means. If you can cut the last third of a sentence without
+losing 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
-or three times per piece to fake forward momentum where a human writer might do
-it once.
+A rhetorical question immediately followed by its own answer.
 
 ---
 
@@ -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
-analysis with startling regularity), "pivotal," "unprecedented" (frequently
-applied to things with plenty of precedent), "navigate," "foster,"
-"underscores," "resonates," "embark," "streamline," and "spearhead." Three or
-more on the same page is a strong signal.
+"delve," "tapestry," "multifaceted," "nuanced" (applied to the model's own
+analysis), "pivotal," "unprecedented" (applied to things with plenty of
+precedent), "navigate," "foster," "underscores," "resonates," "embark,"
+"streamline," "spearhead."
 
 ### 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,
-which is unnecessary when the writing itself makes the importance clear.
+"particularly," "increasingly." Dropped in to signal that something matters when
+the writing itself should make the importance clear.
 
 ### The "Almost" Hedge
 
-Models rarely commit to an unqualified statement. Instead of saying a pattern
-"always" or "never" does something, they write "almost always," "almost never,"
-"almost certainly," "almost exclusively." "Almost" is a micro-hedge that shows
-up at high density in model-generated analytical prose, diagnostic in volume.
+Instead of saying a pattern "always" or "never" does something, models write
+"almost always," "almost never," "almost certainly," "almost exclusively." A
+micro-hedge, less obvious than the full hedge stack.
 
 ### "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
-the actual argument is. Human writers don't begin a piece by zooming out to the
-civilizational scale.
+Used to open an essay. The model is stalling while it figures out what the
+actual argument is.
 
 ---
 
@@ -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
-reflexively both-sides everything.
+artifact of RLHF training, which penalizes strong stances.
 
 ### 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
-it and the piece improves.
+The first paragraph adds no information. Delete 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,
-signaling that the model is wrapping up without landing on anything.
+The high school "In conclusion,..." dressed up for a professional audience.
 
 ### 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
-natural disaster.
+Generic emotional language that could apply to anything.
 
 ---
 
@@ -236,33 +208,28 @@ natural disaster.
 
 ### Symmetrical Section Length
 
-If the first section of a model-generated essay runs about 150 words, every
-subsequent section will fall between 130 and 170. Human writing is much more
-uneven.
+If the first section runs about 150 words, every subsequent section will fall
+between 130 and 170.
 
 ### 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
-unbroken chain of transition words: "However," "Furthermore," "Moreover,"
-"Additionally," "That said," "To that end," "With that in mind," "Building on
-this." Human prose doesn't do this.
+The first word of each paragraph forms an unbroken chain of transition words:
+"However," "Furthermore," "Moreover," "Additionally," "That said," "To that
+end," "With that in mind," "Building on 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
-obscure idiom without explaining it, make a joke that risks falling flat, leave
-a thought genuinely unfinished, or keep a sentence the writer liked the sound of
-even 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.
+contradiction, go on a tangent and have to walk it back, use an obscure idiom
+without explaining it, make a joke that risks falling flat, leave a thought
+genuinely unfinished, or keep a sentence the writer liked the sound of even
+though it doesn't quite work.
 
 ---
 
@@ -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
-has learned that essays are supposed to gesture at big ideas, so it gestures.
+Zooming out to claim broader significance without substantiating it.
 
 ### "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
-qualification the model believes someone expects.
+should be noted") function as verbal tics before a qualification the model
+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
-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.
+"perfect storm," "game-changer."
 
 ---
 
 ## 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
-passes, because fixing one pattern often introduces another.
+patterns. Do at least two full passes, because fixing one pattern often
+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
-    into its own sentence, start the sentence with a subordinate clause, or
-    embed a relative clause in the middle instead of appending it at the end.
+    redundant. If the second clause does carry meaning, break it into its own
+    sentence, start the sentence with a subordinate clause, or embed a relative
+    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
-    summary sentence that adds nothing, treating each paragraph as a
-    self-contained unit that needs its own conclusion.
+    paragraph already made, delete it.
 
 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
-> triple in the section about absence of mess, two-clause compounds everywhere,
-> and "almost" hedges in its own prose about em-dash overuse)_
+> **model:** _(strips out every "humans do this too but less often" comparison)_
 
 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.