The Deslop Stack Fallacy: Why Style Rules Hurt Thinking Quality | Kondasamy Jayaraman (Samy)

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On this page<br>On this page Developer Twitter found a new silver bullet: the instant deslop system prompt.

Stop banning single words like “delve” or “testament” one at a time. Instead, paste an entire writing standard into your global CLAUDE.md or AGENTS.md. Stack Orwell’s 1946 rules, ASD-STE100 Simplified Technical English, ISO 24495-1, and GovUK style guidelines into one prompt block. Every commit message, README, and PR description ships in clean, human prose.

The promise is seductive. The reality is different. Forcing an LLM to follow strict writing constraints while it solves a complex problem creates severe cognitive interference. It destroys thinking quality.

Here’s what actually happens when you deslop at reasoning time.

What the Twitter Advice Actually Says

The advice circulating on X right now sounds like this:

“STOP telling Claude Code/Codex ‘no em dashes’. STOP telling Claude Code/Codex ‘stop saying delve’. STOP telling Claude Code/Codex ‘don’t sound like AI’. You never gave it a writing system. ”

“Orwell wrote the fix 80 years ago. Six rules, 1946. Paste them into your global CLAUDE.md / AGENTS.md and every session picks them up: never use a long word where a short one will do, if it is possible to cut a word out cut it out, never use the passive where you can use the active.”

“Experimenting with a rare addition to my global CLAUDE.md: ‘Always talk in ASD-STE100 Simplified Technical English.’”

“I go for the three prong deslop stack: ASD-STE100 + Orwell + GovUK house style.”

The pattern is clear: take a historical writing standard, paste it into your system prompt, and let it run during generation. It sounds reasonable. To understand why it fails, you have to understand what these standards are and why humans created them.

What the Style Rules Actually Are

These frameworks were built for human editors working under specific constraints. They were never designed to run inside a reasoning loop.

ASD-STE100 (Simplified Technical English): In 1986, the European Association of Aerospace Industries created STE for aircraft maintenance manuals. Technicians across the world read these manuals in English as a second or third language. A misread procedure could crash a plane. STE solves this by restricting English to a controlled dictionary of about 1,000 approved words. Words can only function as one part of speech. “Close” is approved only as a verb, never as an adjective. Sentences cap at 20 words for instructions. Passive voice is forbidden.

Orwell’s Six Rules (1946): Orwell wrote Politics and the English Language to fight political propaganda and evasive jargon. His rules emphasize cutting spare words, using active verbs, and choosing short words over long ones. He was combatting dead metaphors that concealed uncomfortable truths.

GovUK, ISO 24495-1, W3C COGA: These standards were built for public services. They ensure citizens of all literacy levels can pay taxes, apply for benefits, or navigate healthcare forms without getting stuck in bureaucratic prose. ISO 24495-1 formalized plain language principles internationally. W3C COGA extended them for cognitive accessibility.

Why developers imported them: LLM outputs were filled with hyperbole, inflated symbolism, and passive academic framing. Developers saw STE, Orwell, and GovUK as a fix. If aerospace technicians used STE to prevent plane crashes, why not force LLMs to write in STE from the start?

The mistake is assuming that a rule system designed for human post-editing works as an inline constraint for an AI reasoning engine. Aerospace writers draft technical procedures, then edit them against STE. When you force an LLM to generate in STE during its reasoning pass, you force it to solve the problem and satisfy rigid lexical constraints at the same millisecond.

The Data: Anand’s Experimentation

I came across a blog from Anand. Anand tested this tradeoff empirically. He ran six complex reasoning tasks through ChatGPT (GPT 5.6 Sol) under two conditions: a standard prompt, and the same prompt with "Answer in ASD-STE100" appended.

The six tasks covered deep analytical work: designing non-saturating agent benchmarks, diagnosing support ticket surges, evaluating startup runway under doubled churn, testing AI decision quality, evaluating benchmark beliefs, and designing adversarial agent systems.

The Evaluation Protocol

To prevent bias toward longer or more sophisticated answers, Anand used a blind, position-swapped rubric. Every response pair was evaluated twice, swapping the order (A, B) and (B, A) to eliminate position bias. The rubric judged six criteria on content only: Correctness, Key Drivers, Mechanism, Caveats, Calibration, and Actionability. Plainly worded answers and technical answers that made the same point were scored as equal. The rubric explicitly instructed the evaluator to ignore...