How We Get a Client’s Business into ChatGPT and Google AI Overview Answers
The exact engineering checklist we use to index client products, services, and brand entity graphs directly into LLM retrieval models.
Locallify Engineering
2026-07-02
5 min read
## How LLM Search Indexers Retrieve Answers
When a user prompts ChatGPT or Perplexity, the LLM executes a Real-Time Search (RAG) query. It parses the top 10 search results, extracts semantic facts, and evaluates entity confidence before generating its output.
The Locallify GEO Implementation Checklist
- **Semantic HTML5 Markup**: Use exact `<header>`, `<article>`, `<section>`, and `<table>` structures instead of unstructured nested `<div>` elements.
- **Schema Graph Cross-Referencing**: Link your `Organization` schema to social profiles (`sameAs`), official domain, and Wikipedia/Wikidata entities where available.
- **Structured Q&A Sections**: Embed explicit `FAQPage` schema with clear 1-to-2 sentence direct answers to common questions.
- **Sub-Second Page Performance**: Fast response times ensure LLM web crawlers (GPTBot, PerplexityBot) don't hit execution timeouts during real-time retrieval.
