"AI Answer Optimization" & SEO
* AI Answer Optimization is the new SEO
* Reforge Presentation: https://lnkd.in/d9kKvJSy
* Slides: bit.ly/ai-optimization
* Graphite is launching AI Answer Tracking - https://lnkd.in/d9w8XQjh
Key Points
* AI Search is increasingly used for search and discovery
* Graphite is building strategies to help track and optimize for AI Search
* Optimization for AI Search will likely be similar and an evolution of traditional SEO vs. something entirely different
LLM vs. LLM+RAG
* Early versions of LLMs were based on next-word prediction, which is different from search
* More and more, AI chat and search use RAG+LLM
* RAG (retrieval augmented generation) starts with a search (retrieval), then summarizes/reforms the search as an answer (generation)
* Perplexity has built its own search engine, CoPilot frequently uses Bing, and OpenAI is reportedly building a search engine - so AI Search is moving towards RAG+LLM more and more
* Therefore, optimizing for AI Search starts with optimizing for search using traditional SEO strategies + is an evolution of new optimization strategies
Keyword Research > Question Research
* "Question research" is the new keyword research
* Rather than finding keywords, instead we need to find all the ways people ask questions for our product
* We may target thousands of keywords, but questions vary more than keywords, thus there are millions of variations of questions
* Keyword research can use known data from Google and other keyword tools
* There are no tools to find what questions people ask - new strategies will need to be made to source how people ask questions
* Based on the questions people ask, we create landing pages and content to target questions similar to SEO
Good Content for Questions
* "Good content" in Google SEO is comprehensive and answers the questions users have for that page via the presence of TF-IDF terms, sub-topics, and embeddings
* A landing page that targets a topic in AI Search needs to first understand the thousands of questions users have for that topic, then answer as many of them as possible
* Example - ResortPass's West Hollywood Edition page should answer questions about reserving a cabana, how much does a day pass cost, and what are opening hours
Citation Optimization
* RAG+LLM performs a search, looks at multiple pages, summarizes them, and cites its sources
* Companies can optimize for being cited using SEO strategies similar
* Forbes and NerdWallet can try to optimize for being a citation for "best credit cards" across many variations of credit cards
SERP Tracking > AI Answer Tracking
* SEO tracks single positions for keywords (e.g. I rank #5 for "best credit card")
* AI Answer Tracking is a distribution or frequency across surfaces, question variants, and question runs
* There are no tools that allow companies to track their presence in AI Answers
* Graphite is launching AI Answer Tracking - https://lnkd.in/d9w8XQjh