Recommendations synthesize per-engine retrieval behavior from the RAG and recency research: citation depth, freshness windows, and structure/schema preferences. Directional guidance, not engine documentation.
How this works
The major engines do not retrieve or cite the same way. Claude cites deeply (~13 sources) and rewards long-form methodology; Perplexity is near real-time and rewards fresh data; ChatGPT favors broad consensus and comparison structures; Google AI Overviews are schema-driven. Pick your targets and content type to get the structure, schema and refresh cadence each engine rewards.
How to use it
- Select your target AI engines.
- Pick your content type.
- Apply the per-engine structure, schema and freshness recommendations.
Frequently asked questions
Why optimise differently for each AI engine?
ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews retrieve and cite differently, different index sources, schema weighting, freshness sensitivity and citation depth. The matrix gives the structure and cadence each one rewards for your content type.
Which engines should I prioritise?
Weight the engines your buyers actually use. For most B2B, Google's AI surfaces and ChatGPT carry the most citation volume, while Perplexity rewards clean question-and-answer formatting and recency. The tool tailors its recommendations to the engines and content type you pick.
Is the AI Platform Optimization Matrix free to use?
Yes. The AI Platform Optimization Matrix is completely free, with no sign-up, no usage limits and no watermark on the output.
Is my data private?
Yes. The AI Platform Optimization Matrix runs entirely in your browser. Nothing you paste or enter is uploaded, stored or sent to any server.
Related tools and reading
Keep going with the playbooks and tools behind this one.