AI Search & LLM Optimization

ChatGPT, Perplexity, and Claude don't search like Google. They use RAG systems that chunk your content into ~500-word semantic blocks and retrieve the most relevant chunks. If your content isn't chunked properly, you're invisible. We structure content with clear semantic breaks every 300-500 words using H2/H3 headers, making each section independently retrievable. Every chunk needs to be semantically complete and useful standalone. Wall-of-text content gets murdered by chunking algorithms.

AI search uses vector embeddings to find content. Your content gets converted into high-dimensional vectors (usually 768 or 1,536 dimensions) and compared using cosine similarity against query embeddings. We optimize for embedding space proximity by increasing entity density (20+ specific technical terms per article), using precise terminology instead of vague language, and maintaining consistent vocabulary throughout. Generic fluff creates weak vector signals; technical precision creates strong ones.

LLMs chunk content at natural boundaries - paragraphs, headers, lists. Bad structure creates chunks that start mid-thought or mix multiple topics. We design content so each chunk contains one complete concept, starts with context, and ends with closure. Analyze chunk boundaries to prevent semantic fragmentation. Each 400-500 word section should answer one specific question completely.

LLMs weight technical entities heavily in retrieval. Content with specific model names (GPT-4, BERT, Claude), technical terms (cosine similarity, vector dimensions), precise measurements (1,536-dimensional vectors), and named methodologies gets retrieved more often. We analyze top-cited content for entity patterns and match that density. Aim for 20-30 technical entities per 2,000-word article minimum.

LLMs cite content that demonstrates actual expertise through specifics, not generic advice. We engineer citation signals: first-person data ("I analyzed 200+ sources"), specific percentages and measurements ("68% of sources were over 2,000 words"), named tools and methodologies ("Using Screaming Frog to crawl..."), actual results with numbers ("Traffic increased 260%"), technical precision throughout. Vague claims like "studies show" or "experts recommend" get ignored.

AI models recognize demonstrated expertise over claimed expertise. We build content with proprietary research, original data analysis, case study results with specific metrics, before/after comparisons with numbers, methodology transparency (how you tested), and tool-specific implementations. "We tested 47 products over 6 months" beats "this is the best product" every time for citations.

Content that's likely in LLM training data gets cited more. High-quality sources include: technical documentation, academic papers, established brand blogs, government sources, peer-reviewed research. We analyze your domain's training data likelihood and build authority signals that match high-retrieval domains. If your domain isn't recognized, focus on content quality that earns citations despite lower domain recognition.

LLMs extract answers more easily from well-structured content. We format for easy extraction: direct answer paragraphs (2-3 sentences answering the question directly), bulleted lists for scannable info, numbered steps for processes, comparison tables for data, code blocks for technical examples. Make it trivially easy for the model to extract the answer and it's more likely to cite you.

AI search queries are longer and more conversational than traditional Google searches. "How do I optimize content for ChatGPT citations" instead of "ChatGPT SEO". We target natural language queries, question-based keywords, and full-sentence search patterns. Analyze People Also Ask boxes and forum questions to find actual conversational queries people use with AI tools.

AI search conversations have context from previous queries. Someone asks "What is RAG?" then "How do I optimize for it?" Your content needs to work both as standalone answer and as part of conversation flow. We structure content to answer both the direct question and related follow-up questions in the same article.

If your answer is incomplete, users ask follow-up questions and your site doesn't get mentioned again. We create answers complete enough that follow-ups aren't needed. Anticipate obvious follow-up questions and answer them in the same content. "What is X?" should also cover "How does X work?" and "When should I use X?"

Data shows 1,500-2,500 word articles get retrieved 95% of the time. Under 800 words is too thin (20% retrieval rate). Over 5,000 words gets diluted across too many chunks (53% retrieval rate). We target the sweet spot where semantic density is high but focus isn't diluted. Three to five major sections, each covering one subtopic completely.

Long paragraphs (7+ sentences) create poor semantic chunks. We keep paragraphs to 2-4 sentences average. Each paragraph covers one complete thought. Never more than 4 sentences without a structural break (header, list, code block, or new paragraph). This creates clean chunk boundaries that RAG systems handle well.

Lists make content easier for both humans and LLMs to parse. We embed 2-3 lists per article in context, not just throwing random bullets. Bulleted lists for related items, numbered lists for sequential steps, nested lists for hierarchical information. Lists break up text and create natural semantic boundaries.

For technical content, code examples and practical demonstrations massively increase citation rates. We include 2-5 code blocks or specific examples per technical article. Real implementations, not pseudo-code. Actual tool commands, not abstract descriptions. Concrete examples beat abstract explanations for LLM retrieval.

ChatGPT pulls from web search results and its training data. Optimize for both: create content structured for web search retrieval (follows traditional SEO) plus high-quality technical content likely to be in future training datasets. Focus on authoritative, well-cited content that other sites reference. Being in other people's citations increases your chances of being in the training data.

Perplexity shows all sources prominently and users can see what got cited. Focus on being the most authoritative source on specific subtopics rather than trying to rank for everything. Deep, technical content on niche topics performs better than shallow content on broad topics. Perplexity rewards specialized expertise over generalist content.

Claude uses Constitutional AI and tends to cite academic and technical sources more heavily. Focus on research-backed claims, citations to authoritative sources, measured language without hype, and technical accuracy. Claude is more conservative with citations; only clearly authoritative content gets referenced.

SGE combines traditional search with AI-generated answers. Optimize for both traditional ranking signals AND citation-worthy content. SGE pulls heavily from top 3-5 traditional search results, so traditional SEO still matters. But within those results, citation-worthy content wins.

Test your content visibility across AI platforms. Run target queries through ChatGPT, Perplexity, Claude, and Gemini. Document which sources get cited, why they got cited, what made them citation-worthy. Compare your content against cited sources to identify gaps. This is the only way to know if your optimization actually works.

Track citation patterns across queries. Which of your pages get cited most often? What do they have in common? Entity density? Word count? Structure? Technical depth? Find the patterns in what works and replicate them across other content. Build a citation profile understanding for your niche.

Your competitors are getting cited and you're not. Why? We analyze their cited content for entity density, technical precision, content structure, demonstration of expertise, and unique data. Identify what they're doing that you're not. Close the citation gap systematically.

Score your content on retrieval probability factors: word count (1,500-2,500 = high score), header frequency (H2 every 350-400 words = high score), entity density (20+ mentions = high score), technical examples (3+ = high score), demonstrated expertise (data/results = high score). Anything scoring under 60% needs rewriting.