Your content either works in vector space or it doesn't. Here's how we make it work.
01
📝
RAG Content Structuring
Structure content with clear semantic breaks every 300-500 words using H2/H3 headers. Each section must be independently retrievable and semantically complete. LLMs chunk your content into blocks for retrieval—if your structure is broken, you're invisible to AI search.
📊
300-500 Words
Semantic Chunk Size
02
🎯
Entity Density & Technical Precision
Pack content with specific technical terms, model names, measurements, and methodologies. Aim for 20-30 technical entities per 2,000-word article for strong vector signals. Generic fluff creates weak embeddings—technical precision creates citations.
🔢
20-30 Entities
Per 2,000 Words Minimum
03
✅
Citation Signal Architecture
Use first-person data, specific percentages, named tools/methodologies, and actual results with numbers. "I analyzed 200+ sources" beats vague claims every time. LLMs cite content that demonstrates real expertise through specifics, not generic advice.
📈
Real Expertise
Demonstrated, Not Claimed
04
📏
1,500-2,500 Word Sweet Spot
Target this range for 95% retrieval rate. Under 800 words is too thin (20% retrieval), over 5,000 gets diluted (53% retrieval). The sweet spot exists because semantic density stays high while focus doesn't get spread too thin across chunks.
📊
95% Retrieval
At Optimal Word Count
05
🔮
Vector Embedding Optimization
Content gets converted into high-dimensional vectors (768-1,536 dimensions) for similarity matching. Optimize by increasing entity density, using precise terminology, and maintaining consistent vocabulary. Technical precision creates strong vector signals that get retrieved.
🎯
768-1,536 Dims
Vector Space Optimization
06
🧩
Semantic Chunking Strategy
Design content so each 400-500 word chunk contains one complete concept with context and closure. Prevent chunks that start mid-thought or mix multiple topics. Bad chunking kills retrieval even if your content is excellent.
✂️
1 Concept
Per Chunk, Complete
07
💬
Answer Completeness
Create answers complete enough that users don't need follow-ups. Anticipate obvious next questions and answer them in the same content. If your answer is incomplete, users ask follow-up questions and your site doesn't get mentioned again.
🎯
Zero Follow-Ups
Complete First Answer
08
🔬
Authority Through Data
Build content with proprietary research, original data analysis, case study results with specific metrics, and methodology transparency. Demonstrated expertise beats claimed expertise. "We tested 47 products over 6 months" wins citations.
📊
Real Data
Original Research Wins
09
🧪
AI Search Visibility Testing
Test content across ChatGPT, Perplexity, Claude, and Gemini. Document what gets cited and why. Track citation patterns, analyze competitive gaps, and score retrieval probability. This is the only way to know if optimization actually works.
🔍
4+ Platforms
Continuous Testing
Ready for AI Search Dominance?
Stop optimizing for 2019 Google. Start getting cited by ChatGPT, Perplexity, and Claude.