Prompt Indexing

Prompt Indexing is the AI SEO practice of systematically identifying, cataloging, and targeting the specific natural-language prompts that users enter into AI search systems like ChatGPT, Perplexity, and Google AI Overviews. Unlike traditional keyword research, which targets short queries entered into a search box, prompt indexing deals with longer, conversational, often multi-part questions that reflect how people actually interact with AI assistants.

The goal of prompt indexing is to build a comprehensive map of the "prompt space" around a topic — every question, request, and scenario a user might pose to an AI system when they are in the domain you want to own. For a project management software company, this might include prompts like "what's the best way to manage remote team tasks," "how should I structure a product roadmap," or "compare asana vs trello for small teams." Each of these represents a retrieval event where the company's content could be cited.

Prompt indexing requires different tools than traditional keyword research. AI-native query research tools, query fanout generators, and "People Also Ask" analysis can reveal the shape of the prompt space. The output is a list of specific prompts with associated content gap analysis — showing which prompts your existing content already answers well and which are unaddressed.

Once a prompt index is built, it serves as the editorial roadmap for a GEO content campaign. Content is created or optimized specifically to answer each indexed prompt directly, with the appropriate level of detail and structure to be selected by AI retrieval systems. Over time, the prompt index can be updated as AI user behavior evolves.

A travel booking platform uses a query fanout generator to identify 300 prompts users ask AI assistants about European travel, finds it has content answering only 60 of them, and uses the gap analysis to commission 240 new content pieces — each optimized for the specific prompt.