Search Engine Optimization is about getting ranked in Google. Answer Engine Optimization is about getting cited when someone asks ChatGPT, Claude, or Perplexity a question. The underlying content needs are similar, but how you get there is different.
How LLMs decide what to cite
Language models pull from training data and, in the case of retrieval- augmented generation (RAG), from real-time crawls. In both cases, they need to have read your page — which means your content needs to be in readable text form, not locked behind JavaScript.
A few things correlate with being cited in AI answers: factual accuracy and specificity, page authority signals (links from other sites), clean structured data, and content that directly answers the question being asked. Fluffy marketing copy doesn't get cited. Specific product specs, detailed how-tos, and clear factual statements do.
The JavaScript problem for AEO
GPTBot and ClaudeBot don't run JavaScript. If your storefront delivers product content through client-side rendering, none of that content exists in the eyes of the LLMs that might cite you. This is worse than a traditional SEO problem because there's no delayed rendering, no second wave crawl — the bot gets what it gets on the first request, period.
AI search is growing. Roughly 25% of traditional search traffic is expected to shift to AI-powered answer engines by 2027. Brands that appear in AI Overviews get 35% more organic clicks than those that don't. Missing from those answers because your content is JS-rendered is a compounding problem.
Practical steps
- Make your content readable without JavaScript (pre-rendering, SSR, or SSG)
- Add JSON-LD structured data for your content type (Product, Article, FAQ, etc.)
- Write specific, factual content that directly answers questions
- Serve clean Markdown to LLM crawlers instead of HTML cluttered with navigation and scripts
- Keep content fresh — AI retrieval systems favor recently crawled pages for time-sensitive topics
AEO isn't a separate track from technical SEO. It's the same fundamentals — readable content, good structure, clean markup — applied to a new set of crawlers with different requirements.