Prompt cohorts and benchmarking
A cohort is a defined group of observations with comparable intent and conditions. A benchmark uses a documented task, sample and evaluation method to compare results.
How the concept fits together
- Step 1Define intent and cohort
- Step 2Record mode, language and period
- Step 3Repeat and retain raw outcomes
- Step 4Compare matched rates and limitations
What it means for GEO
Keep prompt intent, language, account context, mode and time window matched. Repeat observations and retain failures. Report study-specific uncertainty rather than making causal claims from a before-and-after change.
What not to infer
News retrieval accuracy, citation overlap and brand visibility have different denominators. Do not combine them into one leaderboard.
How it works
A cohort fixes the intent and observation conditions so comparisons have an interpretable scope. Separate category discovery, branded verification and technical support: they elicit different answers and have different opportunities for mentions and citations. Also record language, location, mode, account context, dates and the model or product label exposed by the interface. Outputs can vary between runs, and consumer products can change without revealing every underlying detail. A benchmark must document tasks, evaluation rules and sampling, not simply arrange a few answers into a leaderboard.
GEO example
Illustrative scenario, not observed assistant behavior or measured results.
Illustrative study design: keep one cohort of unbranded tool-discovery questions and a separate cohort of branded feature-verification questions. Observe both before and after updating documentation, using the same prompts and comparable settings. If discovery mentions increase but feature errors remain, report both outcomes. Adding easier prompts or switching from answers without search to search-enabled answers midway changes the comparison. An unchanged comparison cohort can help contextualize a change, but does not automatically turn this observation into a causal experiment.
What you can do
- Version the prompt set and freeze inclusion criteria, denominators and review rules before collecting answers.
- Repeat observations under recorded conditions and retain full outputs, citations, timestamps and failed attempts.
- Report results per comparable cohort instead of pooling unrelated intents, assistants or evaluation measures.
What to measure, and what it cannot prove
Show sample sizes and variation across repeated runs. If uncertainty estimates are used, account for repeated observations of the same prompts rather than assuming every answer is independent. Describe a before-and-after change as observational unless the design supports causal inference. Published GEO and citation-evaluation benchmarks provide methodological examples, not current guarantees for your prompt set.
Evidence and authoritative sources
- Aggarwal et al.: GEO (KDD 2024)
Introduces GEO-bench and study-specific visibility measures; historical experimental findings do not guarantee current product outcomes.
- Gao et al.: Enabling Large Language Models to Generate Text with Citations (EMNLP 2023)
Illustrates a documented benchmark with separate evaluation dimensions for generated answers and citations.
- OpenAI: Searching the web with ChatGPT
Documents contextual influences such as memory and location that should be recorded or controlled.
The sources establish the documented concepts and product behavior. The publishing checklists, scenarios and operational measurement rules are GeckoCheck educational interpretations, not provider-endorsed ranking instructions.
Source: Aggarwal et al.: GEO (KDD 2024) · Gao et al.: Enabling Large Language Models to Generate Text with Citations (EMNLP 2023) · OpenAI: Searching the web with ChatGPT
Period: Reviewed October 12, 2026. Living provider documentation; cited research published in 2023 and 2024 where applicable.
Sample: Conceptual explanation and illustrative scenario. No measured observations.
Limitation: GeckoCheck educational interpretation. Examples are fictional; formulas use declared operational definitions, not universal provider metrics. Source documentation does not guarantee citation or ranking outcomes.