Learn how modern discovery works.
Plain-language guides to technical SEO, AI visibility, structured content, and the search systems that decide how your site is found.
- 01Start with the concepts behind GEO and AI visibility
- 02Understand how crawlers and answer engines read a page
- 03Move into the technical checks used in a complete audit
- 01
Core Web Vitals in plain English
Core Web Vitals are three metrics for loading, visual stability, and responsiveness - LCP, CLS, and INP. Here's what each means and the exact thresholds Sight grades against.
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How AI crawlers read your site
AI answer engines mostly fetch your raw HTML without running JavaScript. If your content only appears after a client-side render, they see an empty shell. Here's what that means.
Read the guide - 03
llms.txt explained: what it is and who reads it
llms.txt is a plain-markdown file at your site root that gives AI assistants a curated map of your most important pages. Here's the format and the honest state of adoption.
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The meta tags that actually matter
Title, meta description, canonical, and Open Graph tags do most of the work in your HTML head. Here are the length rules and conventions Sight checks, with the exact thresholds.
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How to prioritize your SEO fixes
A long list of issues is only useful if you know what to do first. Here's how to sequence fixes by severity and effort - the same ordering Sight uses to rank your report.
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Robots.txt and sitemaps: a practical guide
robots.txt controls which crawlers can fetch what; your sitemap lists the pages you want found. Here's how to configure both for search engines and AI bots without accidentally blocking yourself.
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An introduction to structured data (JSON-LD)
Structured data is machine-readable markup that states what a page is about in a typed, unambiguous way. Here are the JSON-LD types worth having and how to test them.
Read the guide - 08
What is GEO (Generative Engine Optimization)?
GEO is the practice of making your website visible and citable to AI answer engines. Here's how it works, how it differs from SEO, and where to start.
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A learning path from concept to implementation
The guides are written so a search lead can understand the decision while an implementer can still find the technical detail required to act.
Every topic starts with the decision it supports, then connects that decision to the crawler response, page signal, model answer, or measurement that can verify it. The sequence is intentional: establish how discovery works, learn which technical and content signals are observable, and only then build a monitoring program around prompts, competitors, markets, alerts, and reports. Readers can follow the complete path or open one guide when a specific implementation question appears.
Begin with the discovery model
Start with the difference between ranking in a list of links and being cited or recommended inside a generated answer. The introductory guides define GEO, AI visibility, prompts, citations, source authority, and the relationship between conventional SEO and answer engines.
Understand how systems read the site
Move into crawler access, robots directives, canonical URLs, structured data, internal linking, content hierarchy, performance, and llms.txt. Each topic explains the signal, the limitation of the signal, and the evidence an audit should retain before recommending a change.
Turn the evidence into a repeatable program
Use the advanced guides to design prompt groups, competitor sets, market coverage, monitoring cadence, alerts, and stakeholder reports. The goal is a stable operating method that lets the team compare periods and explain changes without relying on isolated screenshots.