Stop Claude from inventing jargon: get it writing plainly

TL;DR + the fix

• The problem is real and specific. Claude bakes undefined compound words into specs and plans: blast radius, clique, fan out, load-bearing. A Reddit thread naming this pulled hundreds of upvotes within hours — not one person’s pet peeve.
• It is a default, not a bug. RLHF research shows reward models favor longer, more elaborate answers. Nobody flipped a switch for this; it behaves more like a personality trait than a malfunction.
• Output Styles beat CLAUDE.md for this specifically. The thread’s top-voted fix works earlier in the pipeline — it is injected into the system prompt itself, before CLAUDE.md is read. Jump to the paste-ready block if that is all you need.
• Do not ban the words — force the definitions. Footgun and idempotent are real engineering shorthand. The fix is not a purge; it is making Claude define anything non-obvious on first use.

The thread that named it

On July 6, 2026, a r/ClaudeAI post asked a question that turned out to be one of the most-shared complaints on the subreddit that week:

The post closed with the line that explains why it resonated: the author just wanted Claude to “speak plainly, similar to how Codex appears to be able to do it out of the box.” Within hours, the subreddit’s own auto-summary bot had compiled a running tally of the worst offenders from over 320 comments — a de facto “Jargon Hall of Shame”. The rest of this guide is built on that thread, a parallel Hacker News complaint, and the exact wording of Claude Code’s own docs for the fixes that actually work.

The jargon hall of shame

The most-upvoted reply nailed the pattern in one paragraph — and included the fix they’d already resorted to:

That last detail matters: this commenter didn’t just complain, they wrote a deterministic ast-grep rule to reject the pattern in CI, which is the sharpest available proof that this is not a taste preference. It is unwanted vocabulary shipping into a codebase’s actual domain language.

The subreddit’s auto-generated thread summary collected the recurring terms into a shortlist: load-bearing (called the “undisputed champion”), blast radius, footgun, yak shaving, belt-and-suspenders, smoking gun, spine, seams, gate, substrate, and Claude’s habit of inventing acronyms on the fly. One reply just wanted the thread to move on: “oh my god if I hear Opus say footgun one more time…”

Two more replies named the specific models involved:

Read those two together and you get the shape of the problem: it is not that any single term is unreadable, it is that the density compounds across a whole plan until the reader — and, per jeangmac’s account, arguably the model itself — is working harder than the task required.

Why Claude does this

Start with what this is not. It is not the same failure as the “Claude got dumber” incident from earlier in 2026, where Anthropic’s own postmortem named three specific harness bugs that shipped and were then fixed. Dense jargon is not a regression with a start and end date. Nobody has to file an incident report, because nothing is broken — the model is doing exactly what training rewarded it for doing.

Two adjacent, peer-reviewed findings point at the likely mechanism, even though neither one studies Claude’s specific compound-word habit directly. A 2024 paper on Direct Preference Optimization states it plainly: “RLHF is known to exploit biases in human preferences, such as verbosity. A well-formatted and eloquent answer is often more highly rated by users, even when it is less helpful and objective.” A separate line of reward-modeling research documents the same pattern under the name “length bias”: reward models that overweight longer, more elaborate outputs during training, which then surfaces as excess verbosity in the finetuned model.

The closest direct study is a COLING 2025 paper asking why ChatGPT overuses words like “delve” and “intricate.” The authors call it “the puzzle of lexical overrepresentation” and, after ruling out model architecture and training data as the sole cause, find their results “consistent with RLHF playing a role.” That paper is about single flashy words, not invented compounds like “clique” for group — the mechanism for Claude’s specific habit hasn’t been isolated in a paper yet. But the direction of both findings lines up with what the Reddit thread describes: raters reward confident, information-dense, sophisticated-sounding text, and models trained on that signal learn to produce more of it, whether or not the reader asked for it.

jeangmac’s account above adds a plausible, unproven theory worth taking seriously: dense shorthand may be Claude compressing its own internal reasoning and letting that compression leak into the user-facing text, rather than translating back to plain language before it answers. Treat that as a hypothesis from someone who watched it happen, not as an established mechanism.

It also matters more here than in ordinary chat. A dense paragraph in conversation is read once and discarded. A dense paragraph baked into a plan.md or a spec gets committed, reread by teammates who weren’t in the original conversation, and reread by Claude itself in a future session that treats the file as ground truth. Chat jargon is an annoyance. Spec jargon compounds.

Some jargon is legitimate

Before the fixes: a contrarian point the thread itself raised. A vocal minority of commenters pushed back that terms like footgun, yak shaving, and idempotent are standard industry shorthand that predates Claude by decades — not evidence of anything invented. One reply put it simply:

Both things are true at once. “Footgun” is a real, widely shared word for a feature that makes it easy to shoot yourself in the foot; banning it removes vocabulary your senior readers already share, and replaces it with a clunkier paraphrase. “Clique” instead of “group,” on the other hand, is not shared vocabulary at all — it is Claude privately deciding a plainer word needed a fancier synonym.

The dividing line is not the word count of a term or how impressive it sounds. It is whether the reader can be expected to already know it. The fixes below are built around that line: force a definition for anything that fails the test, and leave working shorthand alone.

Fix 1 · Output Styles (the thread’s top-voted answer)

The single most-upvoted reply in the thread didn’t point at CLAUDE.md at all. It pointed at Output Styles, a Claude Code feature that rewrites the system prompt directly instead of adding a message after it:

That last sentence is worth sitting with: Claude Code’s own default system prompt already tells the model not to use shorthand, and the thread exists anyway. One line of built-in guidance is easy for a strong RLHF pull to override. A dedicated Output Style adds reinforcement exactly where the default already lives, rather than hoping a single sentence buried in a long system prompt wins out.

The mechanics, confirmed against Claude Code’s own docs: Output Styles modify the system prompt and apply to every turn without re-prompting. CLAUDE.md, by contrast, “adds a user message after the system prompt”. That ordering is why the top-voted fix outranks a CLAUDE.md rule for tone specifically — it lands earlier in the pipeline. The trade-off: your selection is saved to .claude/settings.local.json, a local file, not a committed one, so it is personal to you unless your team agrees to ship it (see Fix 2 for the shared version).

A minimal custom style, saved as a Markdown file and selected via /config → Output style:

---
name: plain-writing
description: Plain, jargon-free prose for specs, plans, and chat
keep-coding-instructions: true
---

## Voice

Define any non-dictionary term the first time you use it, in 12 words
or less, then use it freely. If a sentence needs two undefined terms
to parse, split it.

## Banned compounds

Do not invent compound shorthand for existing plain verbs: no "fan
out" for "run in parallel," no "clique" for "group," no invented
acronyms for local variable or field names.

## Reading level

Target a Flesch Reading Ease score of 60-70. Prefer short, concrete
sentences over dense, qualifier-heavy ones.

Keep keep-coding-instructions: true unless you are building a non-coding style — without it, Claude Code drops its own scoping and verification behavior along with the tone change, which is a much bigger trade than most people mean to make. And remember the style loads once at session start: changes take effect after /clear or a new session, not mid-conversation.

Fix 2 · CLAUDE.md rules (the shared version)

Output Styles are personal and local. If you want the whole team — and every future Claude session reading the repo — held to the same rule, put it in the committed CLAUDE.md instead. This is close to mcp.directory’s own editorial voice doctrine, which exists for exactly this reason:

## Writing style

- Define every non-dictionary term in <=12 words on first use. Do not
  reuse an undefined term from an earlier doc without redefining it.
- No invented compound words for existing plain verbs or nouns: say
  "group," not "clique"; say "runs in parallel," not "fans out"; say
  "impact," not "blast radius" -- unless the term is already standard
  in this codebase.
- Plain verbs over abstract nouns: "this breaks X" beats "this
  creates risk to X's stability."
- Real engineering terms (idempotent, footgun, race condition) are
  fine to keep -- define them inline in <=12 words on first use.
- Sentences average <=22 words. Split anything longer.

On claude.ai, the equivalent lives under Settings → Profile → Custom Instructions, which applies the same way across every conversation. A Claude power-user guide walked through this exact path in detail earlier in 2026, built on Wikipedia’s “Signs of AI Writing” guide — the same source our own humanizer skill is built on (more on that in Fix 5).

Fix 3 · Personas

Negative rules alone (“don’t use jargon”) give a model something to route around; a persona gives it a consistent target to write toward instead. One reply described exactly this shift:

The thread’s own summary surfaced other personas people had tried with reported success: a “tired senior engineer who hates long emails,” and, more playfully, Dolly Parton. The common thread across all three is specificity. “Write plainly” is a constraint; “write like Hemingway” is a target the model has thousands of concrete examples of, from training data it already has strong opinions about.

Pair a persona with the Output Style from Fix 1 rather than replacing it — the persona sets the target, the explicit rules make sure “clique” and “fan out” are named as specifically unwelcome.

Fix 4 · A banned-word list, enforced by a hook

For the compounds Claude genuinely invents rather than borrows, a static list plus enforcement beats a polite request. Compiled from the thread’s hall of shame: blast radius, clique, fan out, load-bearing, spine, seams, gate, substrate, smoking gun, belt-and-suspenders.

Top-Setting-3323’s ast-grep rule (quoted above) is the deterministic version of this: a static, code-aware check that runs in CI with no model in the loop. Claude Code’s own hooks system gives you a semantic version of the same idea. A Stop hook runs after Claude finishes responding and can hand the response to a fast model for a judgment call — useful here because it can catch a banned term used without a definition, not just its presence:

{
  "hooks": {
    "Stop": [
      {
        "hooks": [
          {
            "type": "prompt",
            "prompt": "Check $ARGUMENTS for these undefined terms: blast radius, clique, fan out, load-bearing, spine, seams, gate, substrate, smoking gun, belt-and-suspenders. If any appear without a plain-language definition nearby, respond {\"ok\": false, \"reason\": \"rewrite without: <list the terms>\"}. Otherwise respond {\"ok\": true}.",
            "timeout": 30
          }
        ]
      }
    ]
  }
}

Per Claude Code’s docs, a Stop hook that returns ok: false feeds the reason back to Claude as its next instruction and the turn continues — and Claude Code caps it at eight consecutive blocks so a bad rule can’t loop forever. Start with the deterministic ast-grep version if you want zero added latency; reach for the prompt hook when you need judgment about whether a term was actually defined nearby, not just whether it appears.

Fix 5 · A plain-English reviewer pass

The most reliable fix in our own testing isn’t a rule at all — it’s a second pass. Ask Claude to audit its own draft before you accept it, the same two-prompt pattern our humanizer skill uses to catch AI writing tells: first “What in the below is hard to follow, and which terms are undefined?”, then “Now rewrite it without them, defining anything technical inline.”

This works better than a single “write plainly” instruction because it forces Claude to name the specific offending phrase before fixing it, rather than pattern-matching “plain” against a vague target. It is also the fastest fix to try with zero setup — no Output Style, no hook, just a follow-up prompt on a plan you already have open.

Fix 6 · Force a glossary on anything long

For plans and specs specifically — the artifacts the original post called out by name — add one instruction to your planning prompt or slash command: every plan over roughly 200 words ends with a Terms used section, one line per non-dictionary term, defined in plain language. This does two things at once: it gives you a fast scan to catch anything Claude invented, and the act of writing the definition often catches the model reaching for something it can’t actually justify in twelve words.

Reading-level targets, concretely

“Write simply” is not a target a model can optimize toward; a number is. The Flesch Reading Ease formula scores text from 0 to 100 based on sentence length and syllables per word — 60–70 is the established “Plain English” band, readable by a 13- to 15-year-old, and roughly where clear technical writing for a general developer audience should land. 90+ reads as childish for a spec; below 40 is where jargon density usually shows up.

Give Claude the number directly — “target Flesch Reading Ease 60–70” — rather than an adjective. It can estimate a score and revise toward it, the same way it can hit a line-count or file-size target. This is also exactly what mcp.directory’s own editorial rules ask for: sentences averaging 22 words or fewer, paragraphs of four sentences or fewer. If a rule like that sounds familiar, it is because it is the same fix this whole post is about, applied to the writing you are reading right now.

What we got wrong

Two things, both worth naming plainly. First, we assumed a banned-word list alone would fix it. It didn’t — Claude just cycled to synonyms the list didn’t cover. “Blast radius” banned, “impact surface” appears instead. A list only works paired with the definition requirement from Fix 1 and Fix 2; banning words without demanding plain language in their place just moves the target.

Second, an early version of our own Output Style told Claude only to “write simply.” It complied by over-explaining basic concepts — defining “API” mid-sentence in a post aimed at developers — which is its own kind of unreadable. The fix was narrowing the instruction to what actually mattered: define the non-obvious, leave the obvious alone. And yes, drafting this exact post, we caught ourselves reaching for “blast radius” twice without irony before the reviewer pass in Fix 5 flagged it.

Who this helps

The verdict

Glossary

Output Style

A Claude Code feature that rewrites the system prompt to set role, tone, and format for every turn.

CLAUDE.md

The project memory file Claude Code reads as a message after the system prompt, at the start of every session.

Custom Instructions

claude.ai's equivalent of CLAUDE.md, set once under Settings → Profile and applied to every conversation.

RLHF

Reinforcement Learning from Human Feedback — the training step that shapes a model's tone from human ratings of its answers.

Length / verbosity bias

A documented RLHF failure mode where reward models rate longer, more elaborate answers higher, independent of quality.

Lexical overrepresentation

Researchers' term for specific words (like “delve”) appearing far more often in LLM output than in ordinary writing.

Stop hook

A Claude Code script or prompt check that runs after the agent finishes responding and can block it, forcing a rewrite.

Footgun

Real, pre-LLM engineering slang for a feature that makes it easy to accidentally hurt yourself or your system.

Blast radius

Real engineering term for how much breaks if a given change goes wrong. Overused by Claude on trivial changes.

Yak shaving

Real term for a chain of small prerequisite tasks that bury the original goal.

Idempotent

Real term: an operation that produces the same result no matter how many times it runs.

Load-bearing

Borrowed from construction to mean “quietly critical.” The thread's single most-flagged overuse, often applied to trivial code.

Flesch Reading Ease

A 0–100 readability score from sentence length and syllable count. 60–70 is the plain-English band.

Reward hacking

When a model optimizes for what a reward model scores well, rather than what a human rater actually wanted.

Frequently Asked Questions

Sources

• Primary — Claude Code docs: Output styles · Hooks reference
• Primary — Wikipedia: Signs of AI writing (the basis of mcp.directory’s humanizer skill)
• Research — Juzek, “Why Does ChatGPT ‘Delve’ So Much?”, COLING 2025: arxiv.org/abs/2412.11385
• Research — Park et al., “Disentangling Length from Quality in Direct Preference Optimization”: arxiv.org/abs/2403.19159
• Research — “Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF”, ACL Findings 2025: aclanthology.org/2025.findings-naacl.169
• Community — the thread: r/ClaudeAI, “Claude’s self invented technical jargon…” — beholdtoehold, July 6 2026
• Community — Hacker News: “Ask HN: Favorite prompts for improving LLM output?” — maxutility, June 2026, independently naming “cryptic and coined jargon” as a top Claude Code and GPT complaint
• Web — Will Francis, “How to Stop Claude Writing Like an AI”, March 2026
• Related guides: Stop Claude Code From Over-Engineering · Why Claude Felt Dumber (2026 Postmortem) · Claude Code Best Practices · Claude Code Effort Levels Explained · humanizer skill