Artificial intelligence optimization

Artificial Intelligence Optimization (AIO) or AI Optimization is a technical discipline concerned with improving the structure, clarity, and retrievability of digital content for large language models (LLMs) and other AI systems. AIO focuses on aligning content with the semantic, probabilistic, and contextual mechanisms used by LLMs to interpret and generate responses.^[1][2][3]

Unlike search engine optimization (SEO), which is designed to enhance visibility in traditional search engines, and generative engine optimization (GEO), which aims to increase representation in the outputs of generative AI systems, AIO is concerned primarily with how content is embedded, indexed, and retrieved within AI systems themselves. It emphasizes factors such as token efficiency, embedding relevance, and contextual authority in order to improve how content is processed and surfaced by AI.^[4][5]

AIO is also known as Answer Engine Optimization (AEO), which targets AI-powered systems like ChatGPT, Perplexity and Google's AI Overviews that provide direct responses to user queries. AEO emphasizes content structure, factual accuracy and schema markup to ensure AI systems can effectively cite and reference material when generating answers.^[6]

As LLMs become more central to information access and delivery, AIO offers a framework for ensuring that content is accurately interpreted and retrievable by AI systems. It supports the broader shift from human-centered interfaces to machine-mediated understanding by optimizing how information is structured and processed internally by generative models.^[7]