Cornerstone · 7 chapters

The Search Agents Guide to
Answer Engine Optimization

A field guide to optimizing for answer engines in 2026. Seven chapters covering the technical layer, reverse engineering LLM responses, content that gets cited, and the off-platform surfaces models lean on. Written for SEOs and people who ship.

Want a strong base first? Read the Guide to SEO before this one. AEO compounds on top of an SEO foundation, not in place of it.

/01 Technical /02 Reverse engineering LLM responses /03 Content /04 Reddit /05 YouTube /06 Social media /07 Monitoring and growing
  1. /01

    Technical Coming soon

    The bots reading your site for AI answers are not Googlebot. Different fetchers, different rules, different things they care about. Get the access layer right or nothing else compounds.

    • Letting the right AI crawlers in, and which to block
    • Structured data that LLMs actually parse
    • llms.txt and the new well-known files
    • Rendering so models can read past the shell
  2. /02

    Reverse engineering LLM responses Coming soon

    Before optimizing for ChatGPT, Perplexity, or Gemini, learn what they already return for your space. The citation set is the brief.

    • Pulling apart what each model cites
    • Source-set sampling at scale
    • Prompt batteries that map the surface
    • Reading when answers shift, and why
  3. /03

    Content Coming soon

    Writing for AI answer engines is not writing shorter. It is writing in pieces a model can lift cleanly, with facts dense enough to be worth lifting.

    • Answer-first structure that survives summarization
    • Factual density without filler
    • Citations as the trust layer
    • Original data as the moat
  4. /04

    Reddit Coming soon

    Reddit is one of the most cited domains across major LLMs. Showing up there is not optional if your category lives in those answers.

    • Why Reddit dominates AI citation sets
    • Building a profile that gets read, not flagged
    • Picking subs that actually carry weight
    • Posting without burning the account
  5. /05

    YouTube Coming soon

    Video transcripts feed AI answers more often than people expect. A YouTube channel can outrank your own blog inside ChatGPT for the queries you care about most.

    • Transcripts that get retrieved
    • Descriptions and chapters as retrieval signals
    • Comments and engagement, and what they teach the model
    • When a video should replace a blog post
  6. /06

    Social media Coming soon

    LinkedIn, X, and the rest are not just distribution. They are how models learn what your brand is, what it is for, and who vouches for it.

    • LinkedIn for authority signals
    • X for retrieval timeliness
    • Brand mentions across platforms
    • What social mentions teach the model about you
  7. /07

    Monitoring and growing Coming soon

    Rankings are not the output anymore. Share of voice across ChatGPT, Perplexity, and Gemini is. Track what already gets cited, then scale what works.

    • Share of voice across the major answer engines
    • Prompt batteries you can rerun weekly
    • What to do when a citation drops
    • Scaling the pages that already get picked up