Blog topic
Generative Engine Optimization
Practical guidance on AI search discovery, crawler policies and evidence you can inspect.
Guides to GEO, crawler access and website evidence. Learn what can be checked directly, what is optional, and what requires an observed citation or visit.
Understand the concept before changing the site
Generative engine optimization describes work intended to help useful content appear accurately in AI-generated answers. It is not a guaranteed result or a separate requirement to publish special files. Start with the GEO evidence guide to distinguish access, retrieval, citations and business outcomes.
Diagnose access and content delivery
The AI visibility diagnostic helps you inspect the response a reader or crawler receives. A successful request is useful evidence, but it does not establish whether a service indexed the page or selected it for an answer.
Use robots.txt policy examples to separate search access from training choices. Prepare rules with the robots.txt generator, then test the deployed file. A crawler policy does not secure confidential information.
Decide whether an optional index is useful
The llms.txt example and validation guide explains how to select maintained pages, write summaries and test links. The llms.txt generator can prepare the file. Use it for a known consumer or workflow, not as a substitute for useful pages or a promise of citations.
Record outcomes separately
Keep a log of the question, service, date, cited URL and evidence location. Label tests where you supplied the URL. Compare recorded referrals and relevant conversions with their attribution limits, and preserve unknowns rather than turning them into zero.
The AI visibility capability page explains the product context. For recurring website work, the content refresh workflow provides a scoped change and verification process. Do not interpret publication itself as proof of improved visibility.
October 8, 2026
robots.txt Examples: Control Search and AI Crawler Access
Adapt these robots.txt examples for search and AI crawlers, understand rule precedence, and test access without confusing crawl rules with indexing or security.
October 8, 2026
llms.txt Example: Build and Validate a Useful Website Map
Build a concise llms.txt file with a copyable example, source-selection rules, and validation steps. Understand its limits and how it differs from a sitemap.
September 3, 2026
Lovable SEO: An Audit Framework for AI-Generated Sites
A reproducible audit for any AI-generated site, separating what Lovable’s own documentation verifies from what only a test on your own project can answer.
August 28, 2026
Can AI Assistants Actually Read Your Website? How to Check
Check whether crawlers can reach your website, read the served content and identify your business. Separate technical access from observed AI citations.
June 30, 2026
Generative Engine Optimization: A Practical Guide to Evidence
Understand GEO, search crawling and useful content. Learn what to verify, what llms.txt does not do, and how to measure citations without promising results.
Frequently asked questions
- Does llms.txt guarantee citations?
- No. It is an optional proposal for systems that consume it, not a ranking or citation guarantee.
- Are search crawlers and training crawlers the same?
- Not necessarily. Read each operator’s current documentation and decide the policy for each purpose.
- What should a visibility report include?
- The question, service, observation date, cited URL and evidence, with prompted tests and recorded referrals separated.
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