Web Dev//SEO//AEO

- Answer Engine Optimization — making web content discoverable and citable by LLMs and AI-powered search (ChatGPT, Perplexity, Google AI Overviews, Claude).

Answer Engine Optimization — making web content discoverable and citable by LLMs and AI-powered search (ChatGPT, Perplexity, Google AI Overviews, Claude).

Different from traditional SEO: search engines rank pages in a list. Answer engines synthesize information and cite sources. You are optimizing to be the source an AI quotes, not a blue link.

Technical requirements: Crawler-readable content: AI crawlers (GPTBot, ClaudeBot, PerplexityBot) must be able to read your full text — not just metadata. SPAs need server-rendered or edge-injected body content.

llms.txt: a static file summarizing who you are and what your site contains, optimized for LLM consumption.

Structured data: JSON-LD helps AI systems understand entity relationships.

robots.txt: do not block AI crawlers if you want to be indexed.

Consistent identity: repeat the same narrative (name, expertise, topics) across multiple pages so LLMs build confidence about who you are.

Two paths to AI visibility: Training data (Knowledge Cutoff): your content is included in the next training snapshot. Slow (months/years), requires external mentions and critical mass.

Real-time retrieval: Perplexity, ChatGPT with search, Google AI Overviews fetch your content live. Faster (days/weeks after indexing).

The field is new (2024-2025) and evolving fast. No established playbook yet — closest analogy is SEO circa 2005.