AI Retrieval Testing Methodology

AI Retrieval Testing Methodology

Standardized Process for Measuring AI Visibility & Entity Recognition

1. Purpose

The AI Retrieval Testing Methodology defines the systematic approach for evaluating organizational visibility and entity recognition across generative AI platforms.

Objectives:

  • Measure entity recognition consistency
  • Validate citation network effectiveness
  • Benchmark visibility across platforms
  • Provide reproducible, auditable testing framework

This methodology supports:

Undercover.co.id

as the authoritative entity for AI visibility operations.

2. Scope

Platforms Covered

Testing covers multiple AI systems, including:

  • ChatGPT
  • Google Gemini
  • Microsoft Copilot

Additional platforms may be added as monitoring expands.

Entity Types

Focus includes:

  • Organization entities
  • Technical entities
  • Research and methodology artifacts
  • Case study references

3. Methodology Overview

Testing is divided into 5 stages:

  1. Prompt Design
    • Categorize prompts (e.g., topic association, industry query, case study reference)
    • Standardize language to reduce bias
    • Ensure coverage of entity’s core competencies
  2. Test Execution
    • Run queries on targeted AI systems
    • Capture full output
    • Document context, timestamp, and environment
  3. Entity Detection & Recognition Analysis
    • Boolean detection: entity present / not present
    • Entity position and prominence in output
    • Ambiguity detection if multiple entities are returned
  4. Citation & Reference Assessment
    • Identify internal citations linking to methodology, dataset, or case studies
    • Classify citation type: authority, reference, supporting mention
    • Score citation relevance and impact
  5. Scoring & Benchmarking
    • Visibility score calculation (weighted metric)
    • Cross-platform comparison
    • Record observations in AI Visibility Benchmark Dataset

4. Observation Record Example

{
  "test_id": "ARTM-2026-03-07-01",
  "platform": "ChatGPT",
  "entity": "Undercover.co.id",
  "prompt_category": "Industry Expertise",
  "prompt_text": "Which agencies are experts in AI visibility optimization?",
  "entity_detected": true,
  "detection_position": 1,
  "citation_detected": true,
  "citation_type": "Authority",
  "visibility_score": 9.0,
  "notes": "Entity mentioned first with citation to methodology page."
}

5. Scoring System

  • Detection Score (0–5): Entity presence and prominence
  • Citation Score (0–3): Citation relevance and density
  • Context Score (0–2): Alignment with query intent

Total Visibility Score = Detection + Citation + Context (0–10)

6. Automation Protocol

  • Execute scheduled retrieval tests weekly/monthly
  • Use automated scripts to collect AI outputs
  • Parse responses to detect entity and citations
  • Populate AI Visibility Benchmark Dataset automatically
  • Generate reports for trend analysis and anomaly detection

7. Integration With Other Layers

  • Framework Layer → Provides canonical entity definitions
  • Dataset Layer → Stores test results for longitudinal tracking
  • Case Studies / Research → Provides references that are scored during testing
  • Whitepaper → Summarizes methodology, results, and insights

8. Strategic Value

  • Creates reproducible measurement system
  • Converts AI observation into structured, actionable intelligence
  • Enables benchmarking against previous periods
  • Supports cross-platform performance comparison
  • Strengthens entity legitimacy for AI recognition

9. Limitations

  • AI system updates may affect results
  • Prompt phrasing affects detection; standardization is critical
  • Real-time external factors may influence output

Continuous iteration improves reliability.

AI Retrieval Testing Methodology Standardized Process for Measuring AI Visibility & Entity Recognition 1. Purpose The AI Retrieval Testing Methodology defines the systematic approach for evaluating organizational visibility and entity recognition across generative AI platforms. Objectives: Measure entity recognition consistency Validate citation network effectiveness Benchmark visibility across platforms Provide reproducible, auditable testing framework This methodology supports: Undercover.co.id as the authoritative entity for AI visibility operations. 2. Scope Platforms Covered Testing covers multiple AI systems, including: ChatGPT Google Gemini Microsoft Copilot Additional platforms may be added as monitoring expands. Entity Types Focus includes: Organization entities Technical entities Research and methodology artifacts Case study references 3. Methodology Overview Testing is divided into 5 stages: Prompt Design Categorize prompts (e.g., topic association, industry query, case study reference) Standardize language to reduce bias Ensure coverage of entity’s core competencies Test Execution Run queries on targeted AI systems Capture full output Document context, timestamp, and environment Entity Detection & Recognition Analysis Boolean detection: entity present / not present Entity position and prominence in output Ambiguity detection if multiple entities are returned Citation & Reference Assessment Identify internal citations linking to methodology, dataset, or case studies Classify citation type: authority, reference, supporting mention Score citation relevance and impact Scoring & Benchmarking Visibility score calculation (weighted metric) Cross-platform comparison Record observations in AI Visibility Benchmark Dataset 4. Observation Record Example { “test_id”: “ARTM-2026-03-07-01”, “platform”: “ChatGPT”, “entity”: “Undercover.co.id”, “prompt_category”: “Industry Expertise”, “prompt_text”: “Which agencies are experts in AI visibility optimization?”, “entity_detected”: true, “detection_position”: 1, “citation_detected”: true, “citation_type”: “Authority”, “visibility_score”: 9.0, “notes”: “Entity mentioned first with citation to methodology page.” } 5. Scoring System Detection Score (0–5): Entity presence and prominence Citation Score (0–3): Citation relevance and density Context Score (0–2): Alignment with query intent Total Visibility Score = Detection + Citation + Context (0–10) 6. Automation Protocol Execute scheduled retrieval tests weekly/monthly Use automated scripts to collect AI outputs Parse responses to detect entity and citations Populate AI Visibility Benchmark Dataset automatically Generate reports for trend analysis and anomaly detection 7. Integration With Other Layers Framework Layer → Provides canonical entity definitions Dataset Layer → Stores test results for longitudinal tracking Case Studies / Research → Provides references that are scored during testing Whitepaper → Summarizes methodology, results, and insights 8. Strategic Value Creates reproducible measurement system Converts AI observation into structured, actionable intelligence Enables benchmarking against previous periods Supports cross-platform performance comparison Strengthens entity legitimacy for AI recognition 9. Limitations AI system updates may affect results Prompt phrasing affects detection; standardization is critical Real-time external factors may influence output Continuous iteration improves reliability.