As SEOs, we used to optimize for “Google.” Now we optimize for “The Models.” But GPT-4 (OpenAI) and Claude (Anthropic) behave differently. They have different “personalities” and retrieval preferences.

GPT: The Structured Analyst

GPT models tend to prefer highly structured data.

  • Loves: Markdown tables, bullet points, JSON chunks, clear headers.
  • Hates: Long-winded ambiguity.
  • Optimization: Use key: value pairs in your text. “Price: $50.” “Speed: Fast.”

Claude: The Academic Reader

Claude models have a massive context window and are fine-tuned for “Helpfulness and Honesty.”

  • Loves: Coherent, long-form prose. Nuanced arguments. Evidence-based reasoning.
  • Hates: Salesy bullet points that lack context.
  • Optimization: Write like a researcher. “While X is true, Y is also factors in because…”

The Hybrid Strategy

For a comprehensive transparency strategy, you need a hybrid content mix suitable for both “Left-Brained” (GPT) and “Right-Brained” (Claude) models.

  • Top of Page: Summary bullets (for GPT).
  • Body: Deep, nuanced analysis (for Claude).
  • Bottom: Structured Data table (for both).

By layering your content, you ensure high retrieval rates across the entire diverse ecosystem of AI agents.

The Introduction of Gemini (Google)

We cannot ignore Gemini. Gemini is multimodal native. It “sees” your images and “watches” your videos.

  • GPT/Claude: Text-dominant.
  • Gemini: Visual-dominant.

To optimize for Gemini, you must ensure your images have Exif Data, descriptive filenames, and alt text that aligns with the vectors of your body text. Gemini builds a “Multi-Modal Embedding” where the image and the text reinforce each other. If your text says “Sunsets” but your image is a “Spreadsheet,” the dissonance weakens the vector.

Glossary of Terms

  • Agentic Web: The specialized layer of the internet optimized for autonomous agents rather than human browsers.
  • RAG (Retrieval-Augmented Generation): The process where an LLM retrieves external data to ground its response.
  • Vector Database: A database that stores data as high-dimensional vectors, enabling semantic search.
  • Grounding: The act of connecting an AI’s generation to a verifiable source of truth to prevent hallucination.
  • Zero-Shot: The ability of a model to perform a task without seeing any examples.
  • Token: The basic unit of text for an LLM (roughly 0.75 words).
  • Inference Cost: The computational expense required to generate a response.