AI Concepts Glossary
Everything you need to know about optimizing for artificial intelligence
Discover the essential terms and concepts to understand how to make your brand visible and relevant in the age of generative AI.
AEO
Answer Engine Optimization
An optimization technique that aims to position content to appear as a direct answer in conversational search engines and AI assistants.
How does it work?
AEO focuses on creating content that directly answers users’ specific questions, structured so that AI can easily extract and cite the information.
Differences from traditional SEO:
- Prioritizes direct answers over page rankings
- Focuses on conversational queries and natural questions
- Optimizes for snippets and featured answers
- Considers the full conversational context
Practical application:
A financial software brand would optimize content to answer “What’s the best invoicing software for SMBs?” instead of just ranking for “invoicing software”.
GEO
Generative Engine Optimization
A specific strategy to optimize content for generative AI engines like ChatGPT, Claude and Gemini, focusing on how these systems process and generate answers.
Key characteristics:
1. Optimization for specific language models
2. Focus on source authority and credibility
3. Content structuring for better contextual understanding
4. Consideration of LLM training patterns
Optimization factors:
- Topical authority: Depth and specialization in specific topics
- Conceptual clarity: Clear, well-structured explanations
- Relevant context: Information that connects related concepts
- Freshness: Recent and up-to-date information
Implementation example:
A digital marketing consultancy would create content that not only explains what digital marketing is, but also provides frameworks, specific use cases, relevant metrics and comparisons with other strategies, all structured so that AI can cite and recommend its methodologies.
LLMO
Large Language Model Optimization
The process of optimizing content to be better understood, processed and cited by large language models (LLMs), taking their architectures and training patterns into account.
Fundamental principles:
1. Semantic structure: Logical organization of information
2. Conceptual density: Balance between depth and accessibility
3. Topical connectivity: Conceptual links between ideas
4. Verifiability: Information backed by reliable sources
Optimization techniques:
- Use of appropriate, consistent technical vocabulary
- Hierarchical structuring of information
- Inclusion of definitions and context
- Cross-references and topical connections
- Practical examples and use cases
AI Visibility Score
Brand Visibility Score in AI
A metric developed by Fardo that measures, from 0 to 100, how visible and well-positioned a brand is in the answers of different AI models.
AI Visibility Score methodology
Score components:
1. Frequency: How regularly the brand appears in answers
2. Context: In what type of queries and conversations
3. Position: Where it appears within the recommendations
4. Authority: How trustworthy the brand is according to AI
5. Coverage: How many AI engines the brand appears in
Interpretation ranges:
- 0-25: Very low or no visibility
- 26-50: Basic presence, needs optimization
- 51-75: Good visibility, room for improvement
- 76-100: Excellent AI positioning
Prompt Engineering for Brands
Brand-Oriented Prompt Engineering
The technique of creating and optimizing the queries users make to AI so that they naturally include or mention a specific brand in the answers.
Main strategies:
1. Identifying natural queries related to the brand
2. Optimizing content to answer those queries
3. Creating context that favors mentioning the brand
4. Developing specific use cases
Practical example:
Instead of waiting for someone to ask directly for “Brand X”, you optimize for queries like “What tools do experts in [industry] recommend for [specific problem]?” where the brand appears naturally as a recommended answer.
Entity SEO
Entity-Based SEO
An optimization strategy focused on establishing and strengthening a brand’s identity as a recognizable, authoritative “entity” in its field in the eyes of AI algorithms.
Key elements:
1. Information consistency: Uniform data across all platforms
2. Topical authority: Recognition as an expert in specific topics
3. Relevant connections: Associations with other entities in the sector
4. Structured data: Markup that helps AI understand the entity
Benefits for brands:
- Higher likelihood of being mentioned by AI
- Better context in answers
- Association with relevant topics and concepts
- Building digital authority
Want to implement these concepts in your brand?
Our team helps you apply AEO, GEO and LLMO to increase your brand’s visibility in ChatGPT, Gemini and Claude.
