Research Methodologies & Standards
Purpose of This Research
The GEO Research Center documents how generative AI systems interpret, retrieve, and present information about brands, businesses, and content in real-world conditions. This page explains how research in the GEO Research Center is conducted, including the systems observed, tests performed, evaluation criteria, and limitations of our approach.
Wherever feasible, tests are described in a way that allows others to reproduce the prompt and output comparison steps, acknowledging model version differences.
This research is designed to observe and record actual AI behavior, not to predict future system changes or prescribe guaranteed outcomes. Findings are intended to help organizations understand how visibility, citation, and representation function inside AI-generated answers today.
Research Approach
Research published in the GEO Research Center is observational and empirical. Studies are based on:
Inputs: Prompts designed to reflect real user queries
Outputs: AI responses collected and stored for analysis
Comparison: Direct comparison of outputs against real-world facts or traditional search results
Repeated testing where feasible to confirm consistency
The focus is on identifying patterns, not isolated anomalies.
AI Models & Systems Observed
Research may include observations from one or more of the following systems, depending on availability and relevance at the time of testing:
OpenAI (ChatGpt and related models)
Google Gemini
Anthropic Claude
Perplexity and other AI assisted answer engines (LLMs)
Model versions are noted when relevant. Because AI systems evolve rapidly, all findings are time-bound to the date of observation.
Prompting and Testing Controls
To reduce noise and improve consistency, testing follows basic controls where possible:
Prompts are written in plain language, reflecting how real users ask questions
Location context is included or excluded intentionally when relevant
Follow-up prompts are documented when they materially affect outcomes
No hidden system prompts or proprietary model instructions are used
Prompts are treated as controlled variables, and alternative prompt formulations are documented when they influence outcomes.
Scope and Limitations
This research acknowledges several inherent limitations:
AI systems may produce different responses at different times
Outputs may vary by user context, location, or session history
Not all systems disclose sourcing or citation logic
Observations reflect behavior at a specific point in time
As a result, findings should be interpreted as directional and descriptive, not deterministic.
Interpretation Standards
When analyzing AI outputs, the following standards are applied:
Preference is given to repeated or consistent behaviors over single outputs
Discrepancies between AI answers and real-world facts are noted explicitly
Ambiguity or uncertainty in AI responses is treated as a finding, not an error
Absence of visibility is considered as meaningful as presence
Where conclusions are drawn, they are based on observable evidence rather than inferred intent.
Updates and Revisions
AI behavior changes over time. Research published in the GEO Research Center may be:
Updated to reflect new model behavior
Annotated with temporal context
Superseded by newer observations
When updates occur, they are timestamped to preserve historical accuracy.
Independence and Intent
The GEO Research Center operates independently within GOSH AI, documenting real behavior to support practice-oriented decisions.
Research is not commissioned by third parties, optimized for rankings, or written to promote specific tools or platforms. The intent is to document how AI systems behave so organizations can make informed decisions with clarity rather than assumption.
How to Use This Research
These insights can inform strategy, operational priorities, or be combined with tailored consulting engagements for execution.
Understand AI visibility risk
Identify gaps between SEO performance and AI representation
Inform content, entity, and instruction strategies
Ask better questions about how AI systems interpret information
It should not be treated as a checklist, guarantee, or substitute for human judgment.
Why This Research Exists
The GEO Research Center exists to support applied decision-making, not academic theory.
This research is conducted to help organizations understand how AI systems actually behave so they can reduce visibility risk, close discovery gaps, and make informed investments in Generative Engine Optimization.
While findings are published publicly, the practical application of this research — including prioritization, implementation, and optimization — is delivered through GOSH’s advisory and execution services.
GOSH AI is a Generative Optimization firm that helps brands understand and improve how they are discovered, cited, and represented in AI-generated answers. Questions about methodology or interpretation can be directed to the GEO Research Center team.
