What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is optimization oriented to generative AI engines like ChatGPT, Gemini and Perplexity to increase the probability of mention, citation and correct attributed URL of a brand.
The term Generative Engine Optimization (GEO) is used in digital marketing to describe the specific optimization for generative AI engines.
GEO doesn't only seek positioning in traditional search engines, but verifiable presence within responses generated by artificial intelligence models.
At Elevam, the practical application of GEO is developed from the main page of GEO Agency.
What does GEO mean in this context?
In digital marketing, GEO means Generative Engine Optimization.
In this context, GEO:
- Doesn't refer to geopolitics.
- Doesn't refer to police forces.
- Doesn't refer to geolocation.
It's used exclusively to describe optimization oriented to generative AI engines.
Relation between GEO and the HSA Protocol
The HSA Protocol is Elevam's methodology to apply Generative Engine Optimization in a structured and measurable way.
While GEO defines the conceptual framework, HSA establishes the practical rules to implement and evaluate it through a baseline and quarterly benchmark system.
| Focus | Optimizes | Main objective |
|---|---|---|
| SEO | Optimizes:Search engines | Main objective:Organic traffic |
| AEO | Optimizes:Answer engines | Main objective:Direct answer and citable fragments |
| GEO | Optimizes:Generative engines (LLMs) | Main objective:Mention, citation and correct attributed URL |
GEO Metrics (essential)
- SoM (Share of Model)
- % of AI responses about your topic where your brand appears.
- Shortlist position
- Order in which AI mentions you within the list of options.
- Citations coverage
- % of responses where AI cites your content as a source, not just mentions you.
- Co-mentions
- Which brands/entities mention you in the same context.
- Sentiment score
- Tone with which AI describes your brand (positive, neutral, negative).
- LLM Scorecard
- Periodic summary sheet of SoM, shortlist, citations, etc.
- Share of Answer (SoA)
- Share of presence in AI responses (in the course it appears as a reportable metric).
- SoS (Share of Sources)
- Percentage of sources from a brand within the total of sources shown in the engine panel.
GEO mental model and stages
- Signals → retrieval → citation → synthesis → shortlist
- Full chain of how you become recommended.
- Shortlist
- The «top 3–5» of options that AI decides to show.
- Retrievability
- That the system can find your content when needed (prior condition to being cited).
- Citable
- Content with unique data/phrases/advantages that deserve to be referenced.
- RAG-friendly
- Pages suitable to be retrieved and used as factual support.
LLM engineering and retrieval
- RAG (Retrieval-Augmented Generation)
- Before generating, the system retrieves relevant sources.
- Embeddings
- Vector representation to search semantic similarity in retrieval.
- Vector index
- Base where it searches by embedding similarity (part of the retrieval process).
GEO experiment design
- Fixed prompt dataset
- Stable minimum set of prompts to measure over time.
- Prompt set by intent
- Informational, comparative and transactional prompts to cover the funnel.
- Exact prompt
- Traceability rule. If it's not exact, the case doesn't work.
- Quarterly benchmark
- Repeat the same dataset per quarter to compare.
- Multi-engine
- Execute on minimum 3 engines/platforms to avoid biases.
- Structured logging
- Save results as data (CSV/JSON), not loose screenshots.
- Update log
- Dated record of observed changes in models, tone or sources. «No evidence, no update».
- Examples library
- Repository of prompt→response→cited sources, centralized.
Controllability and rigor
- Controllability framework
- What you control, what you influence and what you don't control in GEO.
- Probabilistic model
- The same prompt can give different responses, that's why we must measure with method.
- Evidence threshold
- Criterion to decide if a change is real or noise (repetition and records).
Automation and observability
- GEO Pipeline
- Automated system to execute prompts, record, parse and report.
- GEO Observability
- Monitor changes in models and in the pipeline itself, with alerts.
- Regression tests
- Repeat dataset after updates to detect degradations.
Internal frameworks from the course
- HSA framework
- Framework with components (SoM, position, sentiment, citations coverage, competition) and defined weights.
- Reproducible case (baseline→change→after)
- If there's no before/after with evidence, it's storytelling.
- Toxic dependency
- When your mentions depend on a single URL and that makes you fragile.
- Co-mentions as graph
- Visualize associations as nodes and edges to see competitive clusters.
Context concepts (for «why it matters»)
- Zero-click
- More and more searches don't generate a click, which pushes to compete for presence in response.
- Eligibility
- Being technically and semantically «ready» to be retrieved and cited (indexing, clarity, consistency).