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geo-fundamentals

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

personAuthor: jakexiaohubgithub
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GEO Fundamentals

Optimization for AI-powered search engines.

1. What is GEO?

GEO = Generative Engine Optimization

| Goal | Platform | |------|----------| | Be cited in AI responses | ChatGPT, Claude, Perplexity, Gemini |

SEO vs GEO

| Aspect | SEO | GEO | |--------|-----|-----| | Goal | #1 ranking | AI citations | | Platform | Google | AI engines | | Metrics | Rankings, CTR | Citation rate | | Focus | Keywords | Entities, data |

2. AI Engine Landscape

| Engine | Citation Style | Opportunity | |--------|----------------|-------------| | Perplexity | Numbered [1][2] | Highest citation rate | | ChatGPT | Inline/footnotes | Custom GPTs | | Claude | Contextual | Long-form content | | Gemini | Sources section | SEO crossover |

3. RAG Retrieval Factors

How AI engines select content to cite:

| Factor | Weight | |--------|--------| | Semantic relevance | ~40% | | Keyword match | ~20% | | Authority signals | ~15% | | Freshness | ~10% | | Source diversity | ~15% |

4. Content That Gets Cited

| Element | Why It Works | |---------|--------------| | Original statistics | Unique, citable data | | Expert quotes | Authority transfer | | Clear definitions | Easy to extract | | Step-by-step guides | Actionable value | | Comparison tables | Structured info | | FAQ sections | Direct answers |

5. GEO Content Checklist

Content Elements

  • [ ] Question-based titles
  • [ ] Summary/TL;DR at top
  • [ ] Original data with sources
  • [ ] Expert quotes (name, title)
  • [ ] FAQ section (3-5 Q&A)
  • [ ] Clear definitions
  • [ ] "Last updated" timestamp
  • [ ] Author with credentials

Technical Elements

  • [ ] Article schema with dates
  • [ ] Person schema for author
  • [ ] FAQPage schema
  • [ ] Fast loading (< 2.5s)
  • [ ] Clean HTML structure

6. Entity Building

| Action | Purpose | |--------|---------| | Google Knowledge Panel | Entity recognition | | Wikipedia (if notable) | Authority source | | Consistent info across web | Entity consolidation | | Industry mentions | Authority signals |

7. AI Crawler Access

Key AI User-Agents

| Crawler | Engine | |---------|--------| | GPTBot | ChatGPT/OpenAI | | Claude-Web | Claude | | PerplexityBot | Perplexity | | Googlebot | Gemini (shared) |

Access Decision

| Strategy | When | |----------|------| | Allow all | Want AI citations | | Block GPTBot | Don't want OpenAI training | | Selective | Allow some, block others |

8. Measurement

| Metric | How to Track | |--------|--------------| | AI citations | Manual monitoring | | "According to [Brand]" mentions | Search in AI | | Competitor citations | Compare share | | AI-referred traffic | UTM parameters |

9. Anti-Patterns

| ❌ Don't | ✅ Do | |----------|-------| | Publish without dates | Add timestamps | | Vague attributions | Name sources | | Skip author info | Show credentials | | Thin content | Comprehensive coverage |

Remember: AI cites content that's clear, authoritative, and easy to extract. Be the best answer.

Script

| Script | Purpose | Command | |--------|---------|---------| | scripts/geo_checker.py | GEO audit (AI citation readiness) | python scripts/geo_checker.py <project_path> |

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

  • Cache hit? Use cached response directly — no need to re-process.
  • Memory match? Inject context_chunks into your reasoning.
  • No match? Proceed normally, then store results:
python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags geo-fundamentals <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

  • Strategy: This skill communicates via the shared memory system.
  • Orchestration: Invoked by orchestrator via intelligent routing.
  • Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

  • Embeddings: nomic-embed-text via Qdrant memory system
  • Lightweight analysis: Local models reduce API costs for repetitive patterns