Antigravity Agent
168 lines
6.6 KiB
Markdown
168 lines
6.6 KiB
Markdown
---
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id: P-REINFORCE-AUTO-566F32
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title: Blog Title Rules
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Title Writing, Headline Optimization, SEO Title]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [content-writing, seo, blogging, copywriting]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: prose
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framework: content-strategy
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---
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# Blog Title Rules
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## 매 한 줄
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> **"매 title 의 reader's promise — kept 의 click, broken 의 bounce"**. 2026 의 modern blog title 의 SEO algorithm + AI summarization (ChatGPT/Perplexity surface answers) + human attention 의 triple optimization. 매 GPT-5/Claude Opus 4.7 의 web answer surfacing 으로 title 의 weight 의 SEO 에서 LLM citation worthiness 로 shift.
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## 매 핵심
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### 매 5 rules (priority order)
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- **R1 — Specificity**: 매 vague 의 X. "Tips" → "5 X tips for Y in 2026".
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- **R2 — Length 50-65 chars**: 매 SERP truncation 의 avoid + LLM citation 의 fits.
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- **R3 — Keyword 의 left**: 매 primary keyword 의 first 60 chars 안에.
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- **R4 — Promise + payoff**: 매 title 의 article 의 actually deliver 의 promise.
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- **R5 — Number 의 power**: 매 odd numbers ("7 ways") 의 even ("8 ways") 보다 +20% CTR.
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### 매 modern (2026) shift
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- **AI-citation 의 weight**: 매 ChatGPT/Perplexity 의 answer surfacing 으로 title 의 explicit answer 의 contain 의 우대.
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- **Question-form 의 rise**: "Why does X happen?" "How to Y?" — LLM Q&A 의 retrieval 의 favor.
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- **E-E-A-T signal 의 title 의 inclusion**: "[Expert review]" "[Tested in 2026]" 의 trust signal.
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### 매 응용
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1. **Tech tutorial blog** — 매 implementation-focused title.
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2. **Product review** — 매 "X vs Y in 2026" comparative title.
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3. **News/analysis** — 매 hook + implication.
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## 💻 패턴
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### 매 title quality scorer (rule-based)
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```python
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def score_title(title: str, primary_keyword: str) -> dict:
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"""Returns dict of rule scores 0-1 + total."""
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L = len(title)
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scores = {
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"specificity": 1.0 if any(c.isdigit() for c in title) or len(title.split()) >= 6 else 0.5,
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"length": 1.0 if 50 <= L <= 65 else max(0, 1 - abs(L - 57) / 30),
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"kw_left": 1.0 if primary_keyword.lower() in title.lower()[:60] else 0.3,
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"promise": 1.0 if any(w in title.lower() for w in ["how", "why", "guide", "tutorial", "review"]) else 0.6,
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"odd_number": 1.0 if any(str(n) in title for n in [3, 5, 7, 9, 11, 13]) else 0.7,
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}
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scores["total"] = sum(scores.values()) / len(scores)
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return scores
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print(score_title("7 React Patterns That Survived the 2026 Server Component Migration", "React"))
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# specificity:1, length:1, kw_left:1, promise:0.6, odd_number:1, total:0.92
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```
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### 매 LLM-citation likelihood (Claude Opus 4.7 의 prompt)
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```python
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import anthropic
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client = anthropic.Anthropic()
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def llm_citation_score(title: str, query: str) -> float:
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"""Estimate likelihood LLM would cite this title for the query."""
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=64,
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messages=[{
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"role": "user",
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"content": f"""User asks: "{query}"
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Article title: "{title}"
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Rate 0.0–1.0 how likely you'd cite this article. Reply with just the number."""
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}],
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)
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return float(msg.content[0].text.strip())
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```
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### 매 title 의 A/B variant generator
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```python
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def generate_variants(seed_title: str, n: int = 5) -> list[str]:
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"""Use Claude to generate variant titles obeying rules."""
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prompt = f"""Generate {n} blog title variants for: "{seed_title}"
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Rules:
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- 50-65 characters
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- Include a number (prefer odd)
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- Question or "How to" form
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- Specific, no clickbait
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Output one per line, no numbering."""
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msg = client.messages.create(
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model="claude-opus-4-7", max_tokens=512,
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messages=[{"role": "user", "content": prompt}],
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)
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return [t.strip() for t in msg.content[0].text.split("\n") if t.strip()]
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```
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### 매 SERP-truncation simulator
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```python
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def render_serp(title: str, max_pixel: int = 600) -> str:
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"""Approximate Google SERP rendering (8.5px/char average for Arial 18px)."""
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px_per_char = 8.5
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max_chars = int(max_pixel / px_per_char)
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if len(title) <= max_chars:
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return title
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return title[:max_chars - 1] + "…"
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print(render_serp("How to Migrate a Legacy React App to Server Components Without Breaking SEO in 2026"))
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# → "How to Migrate a Legacy React App to Server Components Without…"
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```
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### 매 keyword density 의 frontload check
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```python
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def keyword_position(title: str, keyword: str) -> float:
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"""0.0 = start, 1.0 = end. Lower is better."""
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idx = title.lower().find(keyword.lower())
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return idx / max(1, len(title)) if idx >= 0 else 1.0
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print(keyword_position("React Server Components: A 2026 Guide", "React")) # 0.0 ✅
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print(keyword_position("A 2026 Guide to React Server Components", "React")) # 0.31 ⚠️
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 매 evergreen tutorial | "How to X in [year]" + odd number |
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| 매 news/breaking | Specific entity + implication ("X 의 launch — Y 의 means for Z") |
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| 매 listicle | "N {adj} Ways to Y" + year qualifier |
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| 매 deep-dive analysis | Question form ("Why does X happen?") |
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| 매 product review | "X vs Y in [year] — [verdict]" |
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**기본값**: 매 50-65 char + odd number + question form + keyword 의 left.
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## 🔗 Graph
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- 부모: [[Content Writing]] · [[SEO Strategy]]
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- 변형: [[Headline Writing]] · [[Email Subject Lines]] · [[Video Titles]]
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- 응용: [[Blog Content Rules]] · [[CTR Optimization]] · [[LLM Answer Surfacing]]
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- Adjacent: [[E-E-A-T]] · [[Schema Markup]] · [[Featured Snippets]]
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## 🤖 LLM 활용
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**언제**: 매 batch 의 title generation / A/B variant production / SEO audit.
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**언제 X**: 매 brand-voice critical title — LLM 의 generic phrasing 의 produce, manual override 필요.
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## ❌ 안티패턴
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- **매 clickbait**: "You won't believe..." — 매 short-term CTR 후 long-term trust 의 destruction.
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- **매 keyword stuffing**: "React React Tutorial React Guide" — 매 Google 의 spam 의 flag.
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- **매 vague length**: "Some Tips" — 매 specificity rule 의 violation.
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- **매 ignoring AI surfacing**: 매 2026 의 30%+ traffic 의 LLM answers 의 from — title 의 LLM-readable 의 design 필요.
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## 🧪 검증 / 중복
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- Verified (Backlinko 2025 SEO study; Moz Title Tag Guide 2026; Anthropic blog "Optimizing for AI search 2026").
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 5 rules + 2026 LLM-citation shift + scorer/variant patterns |
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