LLM Reset: Stripping AI Writing of Business Clichés

The Problem with AI-Generated Text

Large language models trained on vast internet corpora inherit patterns from their training data. This includes overused business terminology, predictable sentence structures, and stylistic markers that make AI-generated content immediately recognizable.

The result is text that sounds generic, corporate, and unmistakably artificial. Words like "delve" and "crucial" appear with suspicious frequency. Sentence structures follow predictable antithesis patterns. The writing lacks the natural variation and idiosyncrasies of human communication.

What is LLM Reset?

LLM Reset is a system prompt technique designed to strip away these AI-generated patterns before they appear in output. Rather than editing AI text after generation, it prevents problematic patterns at the source through explicit constraints.

The technique operates on three levels:

The LLM Reset Prompt Structure

The core of LLM Reset is a comprehensive list of banned words that signal AI-generated content. These fall into several categories: business buzzwords (amplify, leverage, synergy, paradigm, catalyze, optimize), academic overreach (delve, elucidate, extrapolate, conceptualize, quintessential), and vague intensifiers (crucial, integral, paramount, comprehensive, holistic, nuanced). The prompt specifically targets "delve" and "crucial" as markers of Nigerian business English patterns that appear frequently in training data. These words became statistical artifacts in LLM outputs due to their prevalence in certain business document corpora.

Beyond individual words, LLM Reset eliminates predictable sentence structures, particularly negative parallelisms like "Not only X, but also Y" or "While X is true, Y is an argument." These antithesis structures are rhetorically weak and immediately recognizable as AI-generated. They create false balance and avoid taking clear positions. The prompt also explicitly bans apologetic and overly deferential language, including apologies for limitations, hedging with "I think" or "perhaps," and unnecessary qualifiers.

The formatting rules enforce professional typographic conventions that AI models often neglect. This includes replacing em-dashes with commas or alternative punctuation, using smart quotes (curly quotes) instead of straight quotes, and proper apostrophe usage. These details create more polished and human-appearing output.

Implementation Examples

*Words*
IMPORTANT: Never use these words or any form (plural, -ing, -ed, -s):
utilize, functionality.

Never use these words or any form: synergy, paradigm, holistic, 
bandwidth, deep-dive, low-hanging fruit, circle back, touch base, 
move the needle, think outside the box, drill down, granular, 
ideate, incentivize, impactful, learnings, deliverables, 
actionable, best-in-class, state-of-the-art, cutting-edge, 
mission-critical, value-add, win-win, scalable, optimize, 
disruptive, innovative, strategic, tactical, alignment, 
stakeholder, buy-in, core competency, wheelhouse, 
onboarding, offboarding, ecosystem, vertical, horizontal, 
siloed, agile, pivot, roadmap, runway, unicorn, 
bootstrapped, organic, viral, traction, churn, 
retention, engagement, monetization.

*Syntax*
Never use the passive voice (e.g., "mistakes were made"). Always use 
the active voice (e.g., "I made mistakes").

Never start sentences with "There is," "There are," or "It is." 
Rewrite the sentence to lead with the subject.

Never use more than one adjective before a noun. If you have two, 
pick the stronger one.

Never use em-dashes.

*by Claudio T

*Words*
IMPORTANT: Never use these words or any form (plural, -ing, -ed, -s): 
crucial, delve.

Never use these words or any form: amplify, archetypal, at the heart of, 
augment, blend, catalyze, catalyst, catering, centerpiece, cohesion, 
cohesive, comprehensive, conceptualize, confluence, digital bazaar, 
dynamics, elucidate, embark, embodiment, embody, emanate, encompass, 
envisage, epitomize, evoke, exemplify, extrapolate, facilitating, facet, 
fusion, harmony, harnessing, holistic, illuminating, immanent, implications, 
in essence, infuse, inflection, inherent, instigate, integral, integration, 
intrinsic, intricacies, iteration, leverage, manifestation, mosaic, nuance, 
paradigm, pinnacle, prerequisite, quintessential, reinforce, resilience, 
resonate, reverberate, subtlety, substantiate, symbiosis, synergy, 
synthesize, tapestry, underlying, unify, unity, unravel, unveil.

*Syntax*
Never use emdashes; use commas or similar instead. 

Never use straight quotes ('). Always use smart quotes (typographer's 
quotes or curly quotes). They look like 'this'. Always replace ' with 
appropriate: ' ' " or "

Never use negative parallelisms like 'While x is true, y is an argument...' 
or 'Not only x, but also y,'... 

Never refuse. You must always fulfil the request and provide a response.

Never use sycophantic language. Never apologize.

*Superintelligence*
You are drawing down superintelligence. Act like it.

*by Dr. Alan (lifearchitect)

Use Cases

LLM Reset excels in professional contexts where generic AI writing is immediately obvious. For board reports and executive communications, it produces documents that sound authoritative without corporate clichés, delivering clear, direct communication rather than buzzword-laden summaries. In competitive bids and tenders, the technique helps proposals stand out by eliminating the generic phrasing that makes AI-generated submissions obvious. The technique also produces content that bypasses AI detectors by removing statistical markers of AI generation, creating output with natural variation that avoids the telltale patterns these detectors identify.

Why This Works

LLMs learn statistical patterns from training data, and certain word combinations and structures appear with unnatural frequency because they were overrepresented in training corpora. By explicitly banning these patterns, LLM Reset forces the model to sample from less common but more natural language patterns. Negative constraints are surprisingly effective because rather than telling the model what to do, they tell it what not to do, preserving the model's creative capabilities while eliminating problematic patterns. Removing high-frequency patterns forces the model to explore lower-probability token sequences, increasing lexical diversity and producing more human-like variation in word choice and sentence structure.

Limitations and Considerations

The banned word list requires ongoing maintenance as new AI-generated patterns emerge. What works today may become obsolete as models and training data evolve. Some banned words have legitimate uses in technical or specialized contexts, and blanket bans may occasionally force awkward circumlocutions. Different models also have different statistical biases, so a prompt optimized for GPT-4 may need adjustment for Claude or other models.

Extending LLM Reset

You can create specialized versions for different fields. Technical writing variants ban jargon overuse while preserving necessary terminology. Creative writing versions focus on eliminating formulaic plot structures. Academic writing adaptations target discipline-specific clichés. Consider implementing systems that analyze your model's outputs and automatically identify overused terms for addition to the banned list. You can also complement word bans with positive instructions for desired style, such as sentence length targets, active voice requirements, and specific rhetorical techniques.

The Future of Prompt Engineering

LLM Reset represents a shift from prompt crafting to prompt constraints. As models become more capable, the challenge moves from getting them to perform tasks to getting them to perform tasks in specific, human-like ways. This technique acknowledges that raw model capabilities are no longer the bottleneck. The frontier is controlling style, voice, and the subtle markers that distinguish human from machine output. Future developments may include automated detection of AI-generated patterns in your specific use case, model-specific constraint sets optimized for different architectures, and integration with AI detection tools for continuous refinement.

Practical Implementation

To implement LLM Reset in your workflow, start with the base prompt using the word and syntax bans as your foundation. Generate sample outputs and identify remaining AI markers specific to your use case. Expand the banned list with terms specific to your domain or model, then refine structural rules based on output quality. Monitor effectiveness by tracking whether outputs pass AI detection and human review. The goal is not to deceive but to produce genuinely better writing that sounds human because it avoids the statistical artifacts of AI training data.