2nd/10_Wiki/Topics/AI_and_ML/Articulateness.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-articulateness	Articulateness	10_Wiki/Topics	verified	self	명료성 articulation prompt clarity plain language technical writing	none	B	0.85	applied	communication writing prompt-engineering technical-writing plain-language llm-collab		2026-05-10	pending	language applicable_to writing / communication Prompt Engineering Documentation PR Description Spec Writing

Articulateness

📌 한 줄 통찰

"매 thought 의 high-resolution output". 매 head 의 fuzzy idea 의 매 lossless 의 transmit. 매 AI 시대 의 가장 큰 leverage — 매 prompt 의 articulate = 매 output 의 quality. 매 vocabulary 의 X, 매 clarity 의 win.

📖 핵심

매 components

1. Vocabulary precision: 매 정확 단어 선택. 매 vague vs specific.
2. Structural clarity: 매 conclusion-first (BLUF, Bottom Line Up Front).
3. Nuance: 매 audience 의 calibrate.
4. Cohesion: 매 logical flow 의 transition.
5. Concision: 매 superfluous word 의 cut.

매 BLUF (Bottom Line Up Front)

• ❌ "Background, then build-up, then conclusion."
• ✅ "Conclusion. Then evidence."

→ 매 reader 의 시간 존중.

Plain Language (매 modern standard)

• 매 short sentence (15-20 word).
• 매 active voice ("X did Y" > "Y was done by X").
• 매 concrete > abstract.
• 매 jargon 의 explain.
• 매 multinational 의 translation 의 friendly.

→ US Plain Writing Act (2010), GOV.UK style guide.

매 prompt engineering 의 articulateness

1. Specific: 매 "write a poem" → "매 4-line haiku 의 autumn".
2. Constraint: 매 length, 매 style, 매 audience.
3. Example: 매 few-shot.
4. Role: 매 "act as senior backend engineer".
5. Output format: 매 JSON, 매 bullet, 매 markdown.

매 technical writing 의 hierarchy

1. README: 매 30-second pitch.
2. Tutorial: 매 narrative, hand-holding.
3. How-to: 매 task-oriented.
4. Reference: 매 exhaustive.
5. Explanation: 매 conceptual.

→ Diátaxis framework.

Anti-clarity 의 source

• 매 jargon 의 over-use.
• 매 passive voice.
• 매 nominalization ("perform an analysis" > "analyze").
• 매 abstract noun ("optimization", "leverage").
• 매 throat-clearing ("It is important to note that...").

💻 패턴

Prompt template (specific + constraint)

Role: Senior TypeScript backend engineer.
Task: Refactor this function for testability.
Constraints:
- Keep the public signature unchanged.
- Extract DB call to a repository interface.
- Return a Result<T, E> instead of throwing.
- Output: code only, no explanation.

Input:
{{code}}

→ 매 vague "make it better" 의 X.

PR description template

## Why
[1-2 sentence motivation]

## What changed
- bullet
- bullet

## How to verify
- [ ] step 1
- [ ] step 2

## Risk
[regression area / rollback plan]

## Out of scope
[what NOT done — prevent reviewer churn]

Plain language rewrite

❌ Original (50 words):
"It is recommended that users should consider implementing
appropriate validation mechanisms in order to ensure that 
input data is properly sanitized before being processed."

✅ Rewrite (10 words):
"Validate input before processing."

Articulateness 의 self-check

1. 매 첫 sentence 의 conclusion?
2. 매 sentence avg < 25 word?
3. 매 active voice 의 80%+?
4. 매 jargon 의 explain or replace?
5. 매 reader 의 next step 의 clear?

🤔 결정 기준

상황	우선
Spec / RFC	Structural clarity + precision
README	BLUF + plain language
PR description	Why + what + risk
LLM prompt	Specific + constraint + format
Slack / async	Concise + actionable
Customer-facing	Plain + nuance

기본값: BLUF + active + concise + concrete.

🔗 Graph

• 변형: Plain-Language · Technical-Writing
• 응용: Prompt_Engineering · PR-Template
• Adjacent: Vocabulary-Expansion

🤖 LLM 활용

언제: 매 prompt 의 craft. 매 doc / spec / PR write. 매 communication 의 review. 언제 X: 매 creative writing 의 nuance 의 sacrifice. 매 poetic context.

❌ 안티패턴

• Throat-clearing: 매 "It is important to note that..."
• Passive voice abuse: 매 actor 의 hide.
• Jargon dump: 매 audience 의 ignore.
• Burying lede: 매 conclusion 의 끝.
• Nominalization: "perform an analysis" 의 "analyze".
• Vague prompt: 매 "make it better" 의 LLM 에 unhelpful.

🧪 검증 / 중복

• Verified (Plain Writing Act, GOV.UK style, Diátaxis).
• 신뢰도 B.
• Related: Prompt_Engineering · Technical-Writing · Plain-Language.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — BLUF + Plain Language + Diátaxis + prompt template