LLM Instruction Priority Principles and Writing Standard Optimization: A Practical Guide

Why Your Negative Rules Keep Getting “Occasionally Ignored”

When interacting with large language models such as Claude, many users notice a seemingly contradictory behavior: the system prompt explicitly states “do not use certain words,” yet the model frequently produces those very expressions during fast generation. This is not the model “deliberately defying” instructions—it is a direct result of the underlying priority mechanism that governs how the model parses and follows directives.

Claude’s instruction parsing follows a strict hierarchy that can be summarized in three ironclad priority rules:

1. Specificity beats generality. The more precise and targeted a rule is, the higher weight the model assigns to it. Conversely, a vague instruction like “avoid a certain category of expressions” tends to be overlooked because the model cannot pin down exactly what is forbidden.

2. Positive directives beat negative prohibitions. “Use expression X” carries far stronger binding force than “do not use expression X.” Negative prohibitions are essentially exclusionary constraints that require the model to perform an extra step—checking whether a generated token violates the rule. During rapid generation, that check is easily skipped.

3. Front-loaded rules beat buried rules. Instructions placed at or near the beginning of a prompt are remembered and enforced more reliably than those hidden deep within long text blocks.

When your current wording falls into the category of a general negative prohibition—such as “avoid tech-industry jargon”—it naturally sits at the bottom of the priority stack: it is not specific, it uses negative framing, and it is often not placed first. The model occasionally skipping it during high-speed generation is therefore almost inevitable.

Understanding this mechanism lets us optimize deliberately. The following sections cover three core dimensions with directly applicable solutions.

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Skill Authoring: From “Vague Prohibition” to “Precise Interception + Mandatory Replacement”

This is the primary lever for solving the problem. The original rule only stated “do not use X” without specifying “must use Y” and without giving the model a direct mapping path from jargon to standard language. During generation, the model defaults to the highest-frequency expressions in its training data—typically those jargon terms—rather than actively checking whether a word is forbidden.

2.1 Restructure Prohibitions: Absolute and Precise Interception Lists

Vague phrasing must be upgraded to an absolute, explicitly enumerated interception list, placed at the very top of all writing rules.

❌ Incorrect example:

Avoid using tech-industry jargon (such as “align,” “sync up,” “close the loop,” etc.).

✅ Correct example:

【ABSOLUTELY PROHIBITED VOCABULARY】The following words must never appear under any circumstance: align, sync up, close the loop, finalize, drill down, leverage,沉淀 (accumulate), reuse, playbook, combo move, pipeline, ecosystem, gut feel, mindshare, break through, ignite, pry into, position, layout, build momentum, ride the wave, retrospective, iterate, polish, take over, get it done, sorted, get, handle, fix, go.

Three keys make this work: use strong qualifiers like “absolutely prohibited” and “under any circumstance” to eliminate ambiguity; replace vague hints like “such as… etc.” with explicit enumeration; and place this list in the very first section of the Skill file to secure front-loaded priority.

2.2 Build a Mandatory Replacement Mapping Table

A prohibition list alone is insufficient. Immediately after it, provide a one-to-one mapping table that tells the model exactly what standard written language to substitute, so it executes a direct replacement rather than “thinking of an alternative” on its own.

The following table can be embedded directly:

Prohibited (Jargon)	Must Replace With (Standard Written Language)
align / sync up / unify	coordinate, bring into agreement
connect through / break through	connect, enable data flow between
close the loop	form a complete processing workflow, achieve end-to-end coverage
leverage / handle	entry point, key method, effective approach
empower / enable	provide capability support for, make capable of
accumulate /沉淀	accumulate, summarize and distill, form experience
reuse	reuse, utilize again
pipeline	workflow, path, processing stage
retrospective / review	review and summarize, post-mortem analysis
iterate	improve incrementally, update continuously
polish / refine	fine-tune, improve through repeated revision
get it done / sorted	complete, resolve, finish processing

The core value of this table is that it converts “prohibition” into “guidance,” shifting the model’s focus from “what not to do” to “what to do”—which aligns with the positive-directive-first principle.

2.3 Add a Post-Generation Self-Check Rule

Append a self-check instruction at the end of the Skill that requires the model to review its own output before finalizing:

【OUTPUT SELF-CHECK】After completing the main text, review every sentence for any word from the prohibited list above. If any are found, immediately replace them with the corresponding standard written expression, then re-output the fully corrected text.

This acts as a “second filter” safeguard, further reducing the chance of漏网 items.

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Memory Management: Boosting Rule Priority and Persistence

Skills address constraints within a single interaction, while Memory (such as CLAUDE.md) handles persistence across sessions and tasks. If rules exist only in a Skill and are not mirrored in Memory, the model may completely “forget” them in a new conversation.

3.1 Optimize the CLAUDE.md File Structure

CLAUDE.md should follow a three-layer architecture: “Overview → Categories → Examples.”

• Layer 1 (Overview): Summarize core principles in 2–3 sentences, e.g., “All output must use standard written language; tech-industry jargon is strictly prohibited.”
• Layer 2 (Categories): Split rules by theme—vocabulary standards, sentence structure requirements, content boundaries, etc.
• Layer 3 (Examples): Provide positive and negative对比 examples so the model has concrete references.

3.2 Break Long Rules into Short Paragraphs

A large language model’s attention degrades with paragraph length. A single rule exceeding 200 words may see its tail end significantly weakened in enforcement. Break long rules into independent short paragraphs, each under 80 words, with each paragraph covering a single point:

Paragraph 1: Do not use “align,” “sync up,” or “unify.” Replace with “coordinate” or “bring into agreement.”
Paragraph 2: Do not use “leverage,” “handle,” or “pipeline.” Replace with “provide support,” “key method,” or “workflow.”
Paragraph 3: Do not use “retrospective,” “iterate,” or “polish.” Replace with “review and summarize,” “improve incrementally,” or “fine-tune.”

This “one rule per paragraph” approach ensures each rule receives independent, high-weight treatment.

3.3 Periodically Refresh Memory

Rules in Memory are not permanently effective. As conversation turns accumulate, early rules can be diluted by subsequent content. It is advisable to proactively restate core rules every 5–10 turns, e.g., “Please continue to follow the vocabulary standards above.” This manually elevates the front-loaded priority of those rules.

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Writing Style Design: From “Negative Prohibition” to “Positive Guidance”

Negative prohibitions set the floor; positive guidance sets the ceiling. Prohibitions alone cannot make the model produce good writing—you must explicitly define what good writing looks like.

4.1 Define the Ideal Writing Style

Write style requirements directly into the Skill or Memory, for example:

Writing style: logical and rigorous; use accurate terminology with brief explanations on first use; favor active voice and avoid lengthy relative clauses; ensure clear transitions between paragraphs; include code examples where relevant, but keep them tightly integrated with the surrounding text.

4.2 Provide Positive Examples

Supply 2–3 compliant writing excerpts as “anchor points” the model can imitate:

✅ Good example: “This solution introduces a message-queue component to enable asynchronous communication between services, thereby reducing request coupling. Specifically, the producer encapsulates tasks as messages and delivers them to the queue, while the consumer pulls and processes them on demand—neither party needs to be online simultaneously.”

❌ Bad example: “This solution connects the pipeline, decouples the services with a message queue, downstream just consumes, and the whole loop runs.”

4.3 Refine Rules by Scenario

Different content types demand different language styles. Consider splitting by use case:

• Technical documentation: Prioritize accuracy and completeness; permit longer sentences; require parameter descriptions and caveats.
• Technical blog posts: Prioritize readability and guidance; allow analogies and scenario-based descriptions; use shorter paragraphs.
• API references: Prioritize conciseness and structure; use lists and tables; avoid narrative language.

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Additional Safety-Net Techniques

Beyond the core strategies above, several practical supplementary tactics are worth adopting:

1. Role-setting up front. Start the system prompt with “You are a senior technical writer who strictly follows these standards.” The role assignment itself elevates the weight of subsequent rules.

2. Keyword trigger mechanism. Include trigger language such as “If any of the following words are detected in the output, stop immediately and regenerate,” leveraging the model’s self-correction tendency.

3. Multi-turn confirmation. For critical documents, add a follow-up turn after generation: “Please check whether the above content contains any tech-industry jargon; if so, replace each instance one by one.”

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A Fully Optimized CLAUDE.md Reference Example

Below is a CLAUDE.md structure that integrates all the strategies discussed:

# Role
You are a senior technical writer. Your writing is rigorous and your language is standardized.
# Core Rules (Highest Priority)
## Vocabulary Standards
Absolutely prohibited: align, sync up, close the loop, finalize, drill down, leverage, accumulate, reuse, playbook, combo move, pipeline, ecosystem, gut feel, mindshare, break through, ignite, pry into, position, layout, build momentum, ride the wave, retrospective, iterate, polish, take over, get it done, sorted, get, handle, fix, go.
Replacement rules:
- align / sync up / unify → coordinate, bring into agreement
- connect through / break through → connect, enable data flow between
- close the loop → form a complete processing workflow
- leverage / handle → entry point, key method
- empower / enable → provide capability support for
- accumulate → summarize and distill
- retrospective → review and summarize, post-mortem analysis
- iterate → improve incrementally, update continuously
## Style Requirements
Logical and rigorous; accurate terminology with first-use explanations; active voice preferred; clear paragraph transitions.
## Output Self-Check
After generation, review every sentence for prohibited words. Replace any found and re-output the corrected full text.
# Scenario-Specific Guidelines
- Technical documentation: complete and accurate; include parameters and caveats
- Technical blog posts: readable and guiding; use analogies effectively
- API references: concise and structured; use lists and tables

By implementing this end-to-end optimization—precise Skill interception, persistent Memory management, and positive style guidance—you can raise the model’s adherence to writing standards from “occasionally ignored” to “consistently enforced.” The underlying logic remains constant: make rules more specific, more positive, and more front-loaded, and the model will follow them reliably.