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From SEO to AEO for Optimizing LLM Output in E-commerce Applications

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  • Maria Cristina Enache

    (Dunarea de Jos University of Galati, Romania)

Abstract

This paper proposes a methodological framework for transitioning from traditional Search Engine Optimization (SEO) to a new paradigm—Answer Engine Optimization (AEO)—driven by Large Language Models (LLMs) and multimodal Generative AI systems. The study contrasts the architectural philosophies of leading LLMs, such as GPT-4o and Claude, illustrating how Custom Instructions, Constitutional AI, and long-context design affect enterprise-grade deployment. It introduces a structured approach to product description generation and content optimization in e-commerce, emphasizing Semantic Clarity, Schema.org markup, and Extraction-Readiness as prerequisites for LLM citation and high-quality output. The framework ultimately positions LLM-optimized content as a structured, machine-interpretable data object, offering a foundation for next-generation e-commerce systems that integrate conversational commerce, multimodal diagnostics, and automated technical support.

Suggested Citation

  • Maria Cristina Enache, 2025. "From SEO to AEO for Optimizing LLM Output in E-commerce Applications," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 91-96.
    • Handle: RePEc:ddj:fseeai:y:2025:i:3:p:91-96
    DOI: https://doi.org/10.35219/eai15840409551
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