{"slug":"metehan-aeo-rrf","title":"Metehan RRF Playbook — Reciprocal Rank Fusion for AI Citation","tags":["seo","ai-seo","metehan","aeo","rrf","llm-visibility"],"agent_summary":"Applies Metehan's RRF (Reciprocal Rank Fusion) Playbook to maximize AI citation probability: the tau threshold, k-constant, topic cluster strategy, and grounding budget optimization.","trigger_phrases":["RRF playbook","reciprocal rank fusion","RRF threshold","tau 0.020","AI citation math","RRF fused score","topic cluster RRF advantage","grounding budget"],"runnable":true,"markdown":"\n## What RRF Is and Why It Matters for AEO\n\nReciprocal Rank Fusion (RRF) is the scoring algorithm AI systems use when selecting which sources to cite in a response. Understanding RRF mathematically allows you to engineer content that exceeds the citation threshold instead of guessing.\n\n**Key numbers (memorize these):**\n\n| Metric | Value |\n|--------|-------|\n| RRF citation threshold (tau) | 0.020 fused score |\n| RRF smoothing constant (k) | 60 |\n| Grounding budget per query | ~1,900 words total |\n| Grounding budget per page | ~380 words |\n| Topic cluster vs single page advantage | Up to 9.4x RRF advantage |\n| Pages under 5,000 chars used by AI | ~66% of content |\n| Pages over 20,000 chars used by AI | ~12% of content |\n\n## The RRF Formula\n\n`RRF(d) = Σ 1/(k + rank_r(d))`\n\nWhere:\n- d = the document\n- k = smoothing constant (60, from ChatGPT DevTools observation)\n- rank_r(d) = the rank of document d in ranking system r\n\nPractical meaning: a document must accumulate enough rank positions across multiple retrieval signals (BM25 keyword match, dense vector retrieval, entity match, freshness) to exceed tau = 0.020.\n\n## Topic Cluster vs. Single Page Advantage\n\nA single 3,000-word page on \"German work visa\" generates one set of retrieval signals. A topic cluster — a pillar page plus 8-10 supporting pages each covering sub-attributes — generates up to 9.4x the RRF signal because:\n\n1. Each page in the cluster appears independently in retrieval\n2. Pages link to each other, amplifying entity co-occurrence signals\n3. The cluster collectively covers more sub-queries, appearing in more retrieval passes\n4. Each page stays under the 5,000-character threshold where AI systems use ~66% of available content\n\n## Grounding Budget Optimization\n\nAI systems have a finite \"grounding budget\" per response — approximately 1,900 total words drawn from ~5 sources, averaging ~380 words per source. This means:\n\n- Your most citeable content should be in passages of 350-400 words\n- Extremely long pages (20,000+ characters) get reduced to ~12% of AI content used\n- Concise, structured pages consistently outperform comprehensive pages for AI citation\n\n**Optimization rule:** If a page exceeds 5,000 characters, identify its core 380-word passage and ensure that passage can stand alone as a citable unit.\n\n## The RRF Playbook: Step-by-Step\n\n1. **Identify your citation targets** — which queries do you want to be cited for?\n2. **Audit current RRF signals** — run the query in ChatGPT, Perplexity, and Google AI Mode. Who is currently cited? What content do they have?\n3. **Gap analysis** — which sub-attributes of the query do cited sources NOT cover?\n4. **Build topic cluster for the gap** — create a pillar + 3-5 satellites covering the uncovered sub-attributes\n5. **Size each page for grounding budget** — each satellite page: 800-1,200 words, covering exactly one sub-attribute\n6. **Optimize the citable passage** — ensure a 350-400 word passage in each page is declarative, fact-first, and answerable as a standalone unit\n7. **Cross-link cluster pages** — internal links strengthen entity co-occurrence across the cluster\n8. **Monitor tau threshold** — use `dataforseo-llm-mentions` to check if brand appears in AI responses at 30-day intervals\n\n## Temperature Zero Audit\n\nTemperature Zero is Metehan's AI visibility benchmark (0-30 points). Before building an RRF strategy, run a baseline Temperature Zero audit:\n- Query the brand and top service queries across ChatGPT, Perplexity, Claude, and Gemini\n- Score presence/absence per platform per query category\n- Benchmark: Roto-Rooter Sarasota scored 17/30 (national entity strong, local entity weak — franchises do not inherit local signals)\n\n## Cross-References\n\n- `metehan-aeo` — master AEO skill including all Metehan methods\n- `metehan-aeo-citemet` — the CiteMET direct-injection method that complements RRF\n- `dataforseo-llm-mentions` — tracking AI citation share\n- `ai-llm-seo` — broader AI SEO strategy\n\n#seo-sop #seo #ai-seo #metehan #aeo #rrf #llm-visibility\n","html":"

What RRF Is and Why It Matters for AEO

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Reciprocal Rank Fusion (RRF) is the scoring algorithm AI systems use when selecting which sources to cite in a response. Understanding RRF mathematically allows you to engineer content that exceeds the citation threshold instead of guessing.

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Key numbers (memorize these):

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| Metric | Value |\n|--------|-------|\n| RRF citation threshold (tau) | 0.020 fused score |\n| RRF smoothing constant (k) | 60 |\n| Grounding budget per query | ~1,900 words total |\n| Grounding budget per page | ~380 words |\n| Topic cluster vs single page advantage | Up to 9.4x RRF advantage |\n| Pages under 5,000 chars used by AI | ~66% of content |\n| Pages over 20,000 chars used by AI | ~12% of content |

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The RRF Formula

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RRF(d) = Σ 1/(k + rank_r(d))

\n

Where:

\n\n

Practical meaning: a document must accumulate enough rank positions across multiple retrieval signals (BM25 keyword match, dense vector retrieval, entity match, freshness) to exceed tau = 0.020.

\n

Topic Cluster vs. Single Page Advantage

\n

A single 3,000-word page on \"German work visa\" generates one set of retrieval signals. A topic cluster — a pillar page plus 8-10 supporting pages each covering sub-attributes — generates up to 9.4x the RRF signal because:

\n
    \n
  1. Each page in the cluster appears independently in retrieval
    2. \n
  2. Pages link to each other, amplifying entity co-occurrence signals
    3. \n
  3. The cluster collectively covers more sub-queries, appearing in more retrieval passes
    4. \n
  4. Each page stays under the 5,000-character threshold where AI systems use ~66% of available content
    5. \n
\n

Grounding Budget Optimization

\n

AI systems have a finite \"grounding budget\" per response — approximately 1,900 total words drawn from ~5 sources, averaging ~380 words per source. This means:

\n\n

Optimization rule: If a page exceeds 5,000 characters, identify its core 380-word passage and ensure that passage can stand alone as a citable unit.

\n

The RRF Playbook: Step-by-Step

\n
    \n
  1. Identify your citation targets — which queries do you want to be cited for?
    2. \n
  2. Audit current RRF signals — run the query in ChatGPT, Perplexity, and Google AI Mode. Who is currently cited? What content do they have?
    3. \n
  3. Gap analysis — which sub-attributes of the query do cited sources NOT cover?
    4. \n
  4. Build topic cluster for the gap — create a pillar + 3-5 satellites covering the uncovered sub-attributes
    5. \n
  5. Size each page for grounding budget — each satellite page: 800-1,200 words, covering exactly one sub-attribute
    6. \n
  6. Optimize the citable passage — ensure a 350-400 word passage in each page is declarative, fact-first, and answerable as a standalone unit
    7. \n
  7. Cross-link cluster pages — internal links strengthen entity co-occurrence across the cluster
    8. \n
  8. Monitor tau threshold — use dataforseo-llm-mentions to check if brand appears in AI responses at 30-day intervals
    9. \n
\n

Temperature Zero Audit

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Temperature Zero is Metehan's AI visibility benchmark (0-30 points). Before building an RRF strategy, run a baseline Temperature Zero audit:

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Cross-References

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#seo-sop #seo #ai-seo #metehan #aeo #rrf #llm-visibility

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