Your content either works in vector space or it doesn't. Here's how we make it work — across ChatGPT, Perplexity, Claude, and Gemini.
Get Your AI Audit →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 — if your structure is broken, you're invisible.
Pack content with specific technical terms, model names, measurements, and methodologies. Aim for 20-30 technical entities per 2,000-word article. Generic fluff creates weak embeddings — precision creates citations.
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.
Target this range for 95% retrieval rate. Under 800 words is too thin (20% retrieval), over 5,000 gets diluted (53% retrieval). Semantic density stays high while focus doesn't spread too thin across chunks.
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.
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.
Create answers complete enough that users don't need follow-ups. Anticipate obvious next questions and answer them in the same content. Incomplete answers mean users ask follow-ups and your site doesn't get mentioned again.
Build content with proprietary research, original data analysis, case study results with specific metrics, and methodology transparency. "We tested 47 products over 6 months" wins citations every time.
Test content across ChatGPT, Perplexity, Claude, and Gemini. Document what gets cited and why. Track citation patterns, analyze competitive gaps, and score retrieval probability.
User's question gets converted into a vector representation in high-dimensional space.
The vector is compared against millions of indexed content chunks using cosine similarity.
Top-matching chunks are retrieved. This is where your content structure matters — poorly chunked content never surfaces.
The LLM synthesizes retrieved chunks into an answer and cites your source. This is where you get mentioned.