SlipItIn/.github/skills/human-ai/references/patterns-language.md

Language and Grammar Patterns (English)

Patterns that betray AI-generated text at the level of word choice, grammatical constructions, and sentence structure. Based on Wikipedia's Signs of AI Writing + tropes.fyi + blader/humanizer's 29-pattern set + brandonwise/humanizer's statistical model.

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1. AI Vocabulary (The Slop Dictionary)

Tier 1 - Zero Tolerance (cut on sight):

Word/Phrase	Why it's a tell	Human alternative
delve	No one says this in conversation	explore, dig into, look at
tapestry	Always used as "rich tapestry of..."	(cut entirely - always filler)
landscape (figurative)	"The AI landscape"	the AI space, AI right now
testament to	"It's a testament to..."	shows that, proves
serves as a reminder	Always preamble to nothing	(cut - just state the thing)
it's worth noting	Meta-commentary, not content	(cut - the note IS the content)
it bears mentioning	Same as above	(cut)
the ever-evolving landscape	Double slop	(cut entirely)
navigate (complexities)	"Navigate the challenges of"	deal with, handle, figure out
spearhead	"Spearheading the initiative"	lead, run, start
multifaceted	"This multifaceted problem"	complex, messy, complicated
pivotal	"A pivotal moment"	important, key, big
paramount	"Of paramount importance"	essential, critical
underscores	"This underscores the need"	shows, highlights
underpin	"Principles that underpin"	behind, supporting
in the realm of	"In the realm of AI"	in AI
shed light on	"Shedding light on this issue"	explain, clarify, show
this highlights	Meta-commentary	(cut - the highlight IS the sentence)

Tier 2 - High Suspicion (replace when clustered, OK once per 1000 words):

crucial, vital, comprehensive, robust, leverage, foster, facilitate, embark, harnessing, utilize, endeavor, moreover, furthermore, additionally, subsequently, nonetheless, overarching, intricate, nuanced, holistic, synergy, paradigm, catalyst, orchestrate, seamless, ecosystem (abstract), journey (figurative), unlock (figurative), empower, elevate

Tier 3 - Context-Dependent (flag if >2 per 500 words):

significant, enhance, innovative, dynamic, diverse, inclusive, sustainable, transformative, streamline, optimize, cutting-edge, state-of-the-art, game-changer, disruptive, scalable, impactful, actionable, meaningful, compelling, groundbreaking

Detection rule:

• 1 Tier-1 word = flag the sentence
• 3+ Tier-2 words in one paragraph = flag the paragraph
• 5+ Tier-3 words in one page = flag the text
• Any combination of 2+ Tier-1 words in 500 words = near-certain AI

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2. Copula Avoidance

Trigger patterns: constitutes, represents, serves as, functions as, operates as, positions itself as, stands as, emerges as, figures as

Problem: AI avoids "is" and "has" as if they were forbidden words. Replaces them with elaborate constructions no human would use in natural writing.

Before (AI):

Stripe constitutes the leading payments infrastructure for the internet. The platform serves as a comprehensive solution for businesses and positions itself as the de facto standard for online transactions.

After (human):

Stripe is the biggest payments company online. Their API is what most startups use to take money.

Rule: If "is" or "has" would work, use it. Save elaborate verbs for when you need precise meaning, not sophistication theater.

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3. Negative Parallelisms

Trigger patterns: not merely X, but Y; it's not just about X, it's about Y; more than X, it's Y; goes far beyond X; transcends the simple X; not simply X, it's fundamentally Y

Problem: Formulaic construction that artificially inflates importance. Creates a false dichotomy where the writer pretends to reject something then embraces something bigger - but both parts say the same thing.

Before (AI):

Product management is not merely about writing user stories. It goes far beyond simply shipping features - it's fundamentally about solving real problems. The PM transcends the role of ticket writer; they are, above all, a translator between business and technology.

After (human):

Good PMs solve problems. Bad PMs write tickets. The difference isn't philosophical - it's practical. Look at someone's backlog and you'll know in 5 minutes.

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4. Tricolon Abuse (Rule of Three)

Trigger patterns: clarity, concision, and coherence; innovation, technology, and transformation; plan, execute, and measure; any rhythmic triad with "and" before the third item; three adjectives in sequence; three abstract nouns grouped

Problem: AI groups ideas in threes because tricolon is rhetorically satisfying - but when it appears in every paragraph, it becomes a tic. Human text groups in 2, in 4, in 1. Rarely in 3 repeatedly.

Before (AI):

The platform delivers speed, reliability, and scalability. Teams need clarity, collaboration, and consistency. Our approach combines innovation, expertise, and dedication.

After (human):

It's fast. Reliable enough that we stopped worrying about downtime. And it scales - we went from 10k to 400k requests/day without changing anything.

Detection rule: If a text has 3+ tricolons per page, it's almost certainly AI-generated. Humans occasionally use rule-of-three for rhetorical effect. AI uses it as a structural crutch.

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5. Weasel Qualifiers

Trigger patterns: it could be argued that, one might suggest, there are those who believe, it has been said that, some would argue, many experts believe, it is generally accepted

Problem: AI uses qualifiers to avoid committing to claims. The result reads like a Wikipedia article written by someone afraid of being corrected. Humans either commit to a claim or cite a specific source.

Before (AI):

It could be argued that large language models represent a significant advancement. Many experts believe this technology has the potential to transform various industries, though some would argue the risks are considerable.

After (human):

LLMs are a big deal. They'll change how most knowledge work gets done - I genuinely believe that. But Hinton is right that we don't understand alignment well enough to be comfortable.

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6. Nominalization Disease

Trigger patterns: the implementation of, the utilization of, the facilitation of, the optimization of, the enhancement of, the establishment of, provides a demonstration of, performs an analysis of

Problem: AI converts verbs into nouns, making sentences longer, vaguer, and harder to parse. "We analyzed" becomes "we performed an analysis of". This is the passive-aggressive cousin of passive voice.

Before (AI):

The implementation of the new system resulted in the enhancement of performance metrics and the facilitation of improved collaboration across teams.

After (human):

We implemented the new system. Performance improved. Teams started collaborating more.

Rule: If a noun ending in -tion/-ment/-ance has a simpler verb form, use the verb.