This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 10 explores Relevance Engineering in practice, showing how to apply GEO principles to create content that is retrievable, extractable, and visible in AI-driven search.

We begin with semantic scoring and passage optimization, explaining how modern systems evaluate meaning at the passage level rather than relying on keyword density. The episode shows how embeddings represent content in vector space and why well-structured, semantically rich passages increase visibility in generative results.

We walk through seven practical ways to tune embeddings, including topic clustering, content architecture, structured data, internal linking, and intent alignment. The discussion then introduces simulation techniques like prompt injection and retrieval simulation, which allow teams to test how AI interprets and retrieves their content.

The chapter closes with a step-by-step Relevance Optimization Plan, covering audits for AI readability, latent intent research, content structuring, and iterative testing. Together, these practices provide a blueprint for aligning content with the way AI systems actually retrieve and assemble answers.

Read the full chapter at ipullrank.com/ai-search-manual