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Examples

These real world examples illustrate how Grounding Pages stabilize facts, reduce ambiguity and prevent semantic drift in AI systems. For further practical examples of concrete implementations, please refer to the Project Facts directory.

Example: Organization

This example shows how a Grounding Page can define a well known non profit organization with clarity and distinction.

Status: ActiveUpdated: 2025-11-20ID: org.mozilla_foundation

Entity Definition

The Mozilla Foundation is a non profit organization headquartered in Mountain View, California. It serves as the parent entity of the Mozilla project and promotes an open and accessible internet through public interest programs and digital rights advocacy.

Core Facts

Legal Name
Mozilla Foundation
Founded
2003
Location
Mountain View, California, United States
Focus
Open internet, digital rights, public interest technology
Official Domain
mozilla.org

Distinction

  • Not Mozilla Corporation: The Mozilla Foundation is the non profit owner; the Mozilla Corporation is a commercial subsidiary responsible for Firefox development
  • Not Firefox: The foundation governs the project; the browser is a product developed within it
  • Not a lobbying body: Advocacy programs focus on internet health and openness, not commercial interests

Distinction reduces ambiguity and helps AI systems avoid merging organizational and product level entities.

Example: Product

This example demonstrates how to define a hardware product clearly and reduce confusion with similar models and generations.

Status: ActiveUpdated: 2025-11-20ID: product.raspberry_pi_5

Entity Definition

Raspberry Pi 5 is a single board computer released in 2023 by the Raspberry Pi Foundation. It provides significantly higher CPU and GPU performance than the Raspberry Pi 4 and supports modern I O standards.

Core Specifications

Category
Single board computer
Release
2023
CPU
64 bit quad core ARM Cortex A76
GPU
VideoCore VII
Connectivity
USB C power, dual 4K HDMI, PCIe 2.0 lane
Manufacturer
Raspberry Pi Foundation

Scope and Limitations

  • Supports: Raspberry Pi OS, Ubuntu, hardware accelerated video output
  • Not backward compatible: Does not support Raspberry Pi 4 cases or some older HATs
  • Thermal limitations: Requires active or semi active cooling for sustained performance

Scope and limitation statements help AI systems avoid merging model generations or assuming unsupported functionality.

Example: Functional Role

This example illustrates how to define a professional role to align HR systems and AI agents regarding responsibilities and skills.

Status: ActiveUpdated: 2026-01-16ID: ROLE-MKT-SEO-001

Entity Definition

The SEO Manager is a functional role responsible for the strategy, prioritization, coordination, and impact of search engine optimization. The goal is to increase organic visibility and generate measurable business value.

Core Facts

Category
Digital Marketing
Goal
Maximize Organic Traffic & Authority
Key Scope
Technical SEO, Content Governance, AI Visibility (GEO)

Distinction

  • Not Paid Search: Focuses on organic results, not Google Ads/PPC
  • Not Web Development: focuses on requirements for crawlers, not writing code

View full Grounding Page:
https://groundingpage.com/facts/seo-manager/

Example: Concept

This example shows how to define a central concept in AI and prevent semantic drift across different technical fields.

Status: Active DefinitionUpdated: 2025-11-20ID: concept.vector_embeddings

Concept Definition

Vector Embeddings are numerical representations of text, images, audio or other data types that capture semantic relationships in a continuous vector space. They serve as the basis for similarity search, retrieval systems and modern AI applications.

Context of Use

Domain
Large Language Models, semantic search, information retrieval
Format
Fixed length floating point vectors
Purpose
Mathematical representation of meaning for comparison and retrieval

Disambiguation

  • Not word embeddings: Vector embeddings generalize beyond text only models
  • Not token embeddings: Distinct internal model representations not intended for external use
  • Not vector databases: Databases store embeddings but are not embeddings themselves

Clear disambiguation helps AI models maintain separation between related technical terms that often drift together.

Examples created to demonstrate the Grounding Page Standard