LLM Optimization in Plain English
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LLM optimization is not ChatGPT SEO.
There is no SERP. There is no ranking. There is no algorithm release you can track.
What it is, in under 600 words.
The two layers
Large language models generate answers from:
Training data, what they learned during pretraining. Frozen until the next version.
Retrieval, what they pull in real time from Google, web search, or APIs.
LLM optimization builds presence in both layers.
The three pillars
Entity establishment. Wikipedia. Wikitia. IQ Wiki. Schema.org Organization markup. Consistent NAP. Crunchbase. LinkedIn Company. The model has to know you are a real, discrete entity.
Citation seeding. Forbes. Entrepreneur. Bloomberg. Inc. Business Insider. Reddit. LinkedIn. Medium. Podcast transcripts. YouTube videos. The model has to see you mentioned by credible sources.
Technical optimization. Schema markup. Clean HTML. FAQ sections. Structured data. Open robots.txt. Your owned content has to be parseable.
Skip one and the other two cap out.
Source weighting (cheat sheet)
Very high: Wikipedia, Forbes, Bloomberg, industry leaders
High: Reddit, LinkedIn, Entrepreneur, Inc, Business Insider
Medium: YouTube, Substack, Medium, Quora
Low: Directory listings, wire service republications, niche marketing blogs
Weights shift across ChatGPT, Claude, Gemini, Grok, and Perplexity. Rank order stays stable.
The content format that gets cited
Leads with a 40-60 word self-contained answer
Declarative language, no hedging verbs
Specific numbers, dates, and attribution
Clean heading structure
FAQ schema
Zero marketing jargon
Writes like journalism. Not like marketing.
The 90-day arc
Month 1: Foundation. Audits, baseline measurement, first outreach.
Month 2: Activation. Publications land, Reddit and LinkedIn seeding, thought leadership publishing.
Month 3: Compound. 4-8 additional publications, wikis live, prompt bank reruns show 3-5x lift.
Months 4-6: Acceleration. Citations surface on queries nobody specifically targeted.
How to measure
Run a fixed prompt bank of 20-40 queries against ChatGPT, Claude, Gemini, Grok, and Perplexity. Monthly. Log citations, source attribution, and context. Track month-over-month deltas.
Without measurement, the program feels like throwing rocks into fog. With it, ROI becomes clear.
Common mistakes
Treating it as SEO (overlap exists but tactics differ)
Buying AI visibility tools without a content program
Blocking LLM crawlers in robots.txt
Measuring at 2 weeks (too early)
Waiting 90 days without interim checks (too late)
The pull-quote
Nobody gets cited in ChatGPT because they gamed a system. They get cited because they built enough credible presence across the web that the model has no reason not to mention them.
Authority building, not hacking.
Want the full framework? Read the complete LLM optimization guide.
Instant Press Co., AI visibility for brands that want to be found.
