How to check LLM search visibility

Beta

Use Boki's LLM search visibility agent to predict how well AI systems will cite your content.

Overview

The LLM search visibility agent scores your content against a specific question. It predicts whether an LLM (like ChatGPT, Claude, or Perplexity) would cite your content when answering that question.

This is not SEO rank. Traditional SEO measures search engine ranking. LLM visibility measures whether AI systems reference your content in their generated responses — GEO (Generative Engine Optimization).

Prerequisites

• An article with substantial content
• At least 50 words of text to score
• A specific question to score against

Steps

1. Open your article in the editor
2. Select the text you want scored (minimum 50 words)
3. Click the sparkle icon that appears, or right-click and select "Ask Boki"
4. Choose LLM visibility from the options
5. Enter the question you want to score against
6. Wait for the analysis

What the visibility score measures

Question alignment: Does your content directly answer the question?

Comprehensiveness: Does your content cover the topic thoroughly enough to be a primary source?

Authority signals: Does your content include elements that LLMs weight highly — clear definitions, structured information, cited sources?

Differentiation: Does your content offer something not found in the top-ranking sources?

AI Search Readiness vs observed visibility

Boki distinguishes between two types of AI visibility:

AI Search Readiness is Boki's assessment of how ready your content is to be cited by AI search engines. This score is based on:

• Source-backed fact coverage
• Answer-first intent alignment
• Content structure and authority signals

Observed Google AI visibility reflects what Google's AI features actually show in search results. This is based on real-world observation of AI search behavior.

These are shown separately because readiness (what you can control) and actual visibility (what's happening in the wild) measure different things. High readiness doesn't guarantee visibility, and low readiness doesn't mean you're invisible — but improving readiness gives you the best chance.

Score states

When an assessment cannot be made, Boki shows an explicit state instead of a placeholder score:

This helps you distinguish between content that scores poorly and content that couldn't be scored at all.

Example

You select a section about API rate limiting and enter the question:

"How do I implement rate limiting in a REST API?"

The LLM visibility agent might respond:

• Score: 72/100
• Strengths: Clear step-by-step implementation, code examples, covers common edge cases
• Gaps: Missing comparison of rate limiting algorithms (token bucket vs sliding window), no mention of distributed rate limiting
• Suggestion: Add a section comparing approaches — LLMs often cite content that presents multiple options

Understanding the score

Result

After checking LLM visibility, you'll have:

• A score indicating citation likelihood
• Specific gaps compared to competing content
• Actionable suggestions for improvement

LLM Search results persistence

When you run LLM visibility checks, the results persist for your session:

• Results survive page reloads and closing/reopening the sidebar
• Each article's results are stored independently
• You can clear results for a single article or for your entire session

This means you can run a check, navigate away, and return to see your results without re-running the analysis.

Tips

Test multiple questions: The same content scores differently against different questions. Test the questions your audience actually asks.

Focus on specific questions: "What is API rate limiting?" scores differently than "How do I implement rate limiting in Node.js?"

Iterate and re-score: Make improvements, then check the score again.

Related

• LLM visibility explained — What LLM visibility means and why it matters
• Technical review — Check accuracy before checking visibility
• Agent reference — LLM visibility specifications