Testing Your Content Across All AI Engines
Key Takeaways
- • Test all 4 major platforms — Claude, GPT, Gemini, Perplexity minimum
- • Use consistent queries — Same questions across all models
- • Track citation rates — Measure how often your content is cited
- • Benchmark against competitors — Compare performance relatively
- • Iterate based on variance — Focus on platforms with lowest scores
Cross-model testing validates that your content performs consistently across all AI engines. Without systematic testing, you are optimizing blind—you might excel on one platform while failing on others without knowing it.
The testing methodology involves querying each AI engine with consistent prompts related to your content topics, measuring citation rates, and iterating based on variance analysis. This data-driven approach replaces guesswork with evidence.
According to Search Engine Journal and Ahrefs research, content that performs well on one AI platform often has 30-40% variance on others. Systematic testing identifies and addresses these gaps.
Testing Methodology #
Step 1: Define Test Queries #
- Primary queries — Direct questions your content answers
- Related queries — Adjacent topics where you should appear
- Competitive queries — Questions where competitors currently win
- Long-tail queries — Specific variations of main topics
Step 2: Execute Across Platforms #
- Claude — Test via claude.ai or API
- ChatGPT — Test via chat.openai.com with browsing enabled
- Gemini — Test via gemini.google.com
- Perplexity — Test via perplexity.ai
Step 3: Measure and Record #
- Citation rate — Was your content cited? (Yes/No)
- Citation position — Where in the response? (1st, 2nd, etc.)
- Citation quality — How much of your content was used?
- Competitor citations — Who else was cited?
| Metric | Target | Action if Below Target |
|---|---|---|
| Cross-model citation rate | >60% | Improve universal content |
| Platform variance | <20% | Add model-specific tuning |
| Citation position | Top 3 | Improve authority signals |
| Competitor gap | Parity | Analyze competitor content |
Iteration Strategy #
- 1Identify variance — Find platforms where you underperform
- 2Analyze gaps — What do competitors do better there?
- 3Apply fixes — Model-specific or universal improvements
- 4Retest — Verify improvements
- 5Repeat — Continue until variance <20%
Related Articles #
Frequently Asked Questions #
How often should I test?
Test major content pieces monthly and after significant updates. For high-priority pages, consider weekly testing. AI models update frequently, so ongoing testing catches performance changes early.
What is acceptable platform variance?
Variance below 20% indicates well-balanced content. 20-40% variance suggests model-specific tuning opportunities. Above 40% variance indicates fundamental content issues that need universal improvement first.
Can I automate cross-model testing?
Yes. Seenos automates cross-model testing, running consistent queries across all platforms and tracking citation rates over time. This enables continuous monitoring without manual effort.
Related Articles #
- Cross-Model GEO Hub
- Universal Content Strategy
- Model-Specific Tuning
- Citation Convergence
- GEO A/B Testing Guide
- Track GEO Performance
- AI Visibility Scores
- What is GEO?
Product Manager at Seenos.ai. Pioneer in AEO research since 2024, exploring the convergence of SEO and GEO (Generative Engine Optimization). Led multiple AI-powered content optimization projects that achieved 300%+ citation increases in ChatGPT and Perplexity.