Generative Engine Optimization with Python

Chapter 3. Content for AI

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This will be the 3rd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

Using the topics identified in Chapter 2, the goal of this chapter is to create the research that will help create the data-driven content for AI search visibility, on any given topic.

Content from a Generative Engine Optimization (GEO) perspective is predominantly text in the form of articles that each have their own URL, which will be our focus.

Even with the knowledge of your audience’s desired topics, the major challenge facing organisations is knowing how to create the content deemed good enough by Large Language Models (LLMs) to be visible for their targeted prompts in AI search.

This chapter will provide the context and data pipelines for creating content for AI search:

• Research for data Driven Content: Delivering the data AI Search wants

• Information Gain Measurement: Measuring the added value of the data driven content to AI

Research For Data Driven Content

Our approach for creating research for data driven content will be to leverage Reddit which is ...