# LLM Content Strategy — AI-First Content Framework

## Overview

A content strategy specifically designed to build brand authority in AI-generated answers. Focuses on creating content that AI models trust, cite, and accurately represent.

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## Topical Authority Mapping

### Methodology

1. **Define core topics** (3-5 primary topics the brand should own in AI answers)
2. **Map subtopics** for each core topic (10-20 subtopics per core)
3. **Audit existing coverage** (which subtopics have quality content vs gaps)
4. **Check AI citation status** (which subtopics generate AI answers citing brand content)
5. **Prioritize gaps** (high-value subtopics where brand has no coverage)

### Authority Score Per Topic

For each core topic, score:

| Signal | Weight | Scoring |
|--------|--------|---------|
| Content depth (word count, detail level) | 20% | 0-10 |
| Content freshness (last updated) | 15% | 0-10 |
| Unique data/insights | 20% | 0-10 |
| Expert authorship | 15% | 0-10 |
| External citations (backlinks, media mentions) | 15% | 0-10 |
| Schema markup coverage | 10% | 0-10 |
| AI citation rate | 5% | 0-10 |

**Authority Score** = Weighted average × 10 (0-100 scale)

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## Content Gap Analysis for AI Visibility

### Gap Types

1. **Coverage gap**: Topic exists but brand has no content on it
2. **Depth gap**: Content exists but is too shallow vs what AI cites
3. **Freshness gap**: Content is outdated (AI prefers recent, dated sources)
4. **Authority gap**: Content lacks expert signals (no author credentials, no data)
5. **Structure gap**: Content isn't formatted for AI parsing (no schema, poor headings)
6. **Entity gap**: Content doesn't connect to the brand's knowledge graph entity

### Prioritization Matrix

Score each gap: **Impact** (how much AI visibility would improve) × **Effort** (how hard to fix)

| Priority | Impact | Effort | Action |
|----------|--------|--------|--------|
| P1 — Quick wins | High | Low | Fix immediately (schema, freshness, formatting) |
| P2 — Strategic | High | High | Plan content creation (new pillar content) |
| P3 — Opportunistic | Low | Low | Batch with other updates |
| P4 — Defer | Low | High | Backlog unless resources allow |

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## LLM-Optimized Content Templates

### Template 1: Definitional Content

Best for: "What is [X]?" queries

```markdown
# What Is [Term]?

[Term] is [one-sentence definition]. [Expanded explanation in 2-3 sentences].

## Key Characteristics
- [Characteristic 1]: [Brief explanation]
- [Characteristic 2]: [Brief explanation]
- [Characteristic 3]: [Brief explanation]

## How [Term] Works
[Clear, step-by-step explanation]

## [Term] vs [Related Concept]
[Comparison table or brief differentiation]

## Examples of [Term]
[2-3 real-world examples with specifics]
```

### Template 2: Comparative Content

Best for: "[A] vs [B]" queries

```markdown
# [Option A] vs [Option B]: [Year] Comparison

## Quick Comparison

| Feature | Option A | Option B |
|---------|----------|----------|
| Best for | [use case] | [use case] |
| Price | [range] | [range] |
| Key strength | [strength] | [strength] |

## Detailed Comparison
[Section-by-section analysis]

## Verdict
[Clear recommendation with reasoning]
```

### Template 3: Procedural Content

Best for: "How to [do X]" queries

```markdown
# How to [Achieve Outcome] (Step-by-Step Guide)

[One paragraph summary of what this guide covers and who it's for]

## Prerequisites
- [Prerequisite 1]
- [Prerequisite 2]

## Step 1: [Action]
[2-3 paragraphs with specific instructions]

## Step 2: [Action]
[Continue for each step]

## Common Mistakes to Avoid
- [Mistake 1]: [Why it's a problem and what to do instead]
```

### Template 4: Statistical/Data Content

Best for: "[topic] statistics" and benchmark queries

```markdown
# [Topic] Statistics and Benchmarks ([Year])

## Key Statistics
- **[Stat 1]**: [Number] ([Source, Year])
- **[Stat 2]**: [Number] ([Source, Year])

## Benchmarks by [Category]

| Category | Metric 1 | Metric 2 | Source |
|----------|----------|----------|--------|
| [Cat A]  | [value]  | [value]  | [source] |
```

---

## 90-Day AI Authority Building Plan

### Days 1-30: Foundation

- [ ] Complete AI visibility audit (baseline scores)
- [ ] Fix all entity consistency issues
- [ ] Implement Organization, Article, FAQ schema on key pages
- [ ] Update or create Wikidata entry
- [ ] Identify top 5 content gaps for priority queries
- [ ] Publish 2-3 definitional/comparative pieces on gap topics
- [ ] Ensure all content has expert author attribution

### Days 31-60: Building

- [ ] Publish 4-6 pillar content pieces on core topics
- [ ] Create original research or data content (survey, analysis, benchmark)
- [ ] Implement HowTo schema on procedural content
- [ ] Build internal linking between topic cluster pages
- [ ] Secure 2-3 external citations (guest posts, press mentions)
- [ ] Re-test priority queries on all AI platforms (measure improvement)

### Days 61-90: Scaling

- [ ] Fill remaining content gaps across all core topics
- [ ] Publish thought leadership content (unique frameworks, predictions)
- [ ] Expand schema markup to all eligible pages
- [ ] Build FAQ content targeting AI platform questions
- [ ] Conduct second full AI visibility audit
- [ ] Document score changes and identify next priority actions
- [ ] Set up ongoing monthly monitoring cadence
