AI Search Isn't Keywords, It's Topics

Forget keywords. The way users interact with AI search engines like ChatGPT or Google's AI Mode fundamentally differs from traditional search. Instead of discrete queries, AI systems dissect questions into clusters of related sub-queries, generating responses based on a broad understanding of a topic and its constituent prompts. This shift demands a new strategy for content creators looking to capture attention in the AI era. As highlighted in a recent analysis from Similarweb, understanding these AI keyword research tools means moving beyond search volume to grasp topic relevance and prompt-level engagement.

The core unit of research is no longer a keyword but a topic, and more specifically, the precise prompts within that topic that users are asking. This necessitates a structured approach to identify what's truly worth targeting. The process involves six key steps, designed to be data-driven and eliminate guesswork.

The Six-Step AI Prompt Research Process

The journey begins with identifying trending topics and narrowing them down to specific, representative prompts. These prompts are then tracked, analyzed for competitor gaps, and ranked based on their potential for content creation. This methodical approach ensures that efforts are focused on areas with genuine audience interest and measurable opportunity.

Step 1: Pinpoint Your Focus Topic

Start by selecting a relevant category within an AI research tool, such as Similarweb's AI Search Intelligence suite, which offers capabilities for AI Search Intelligence suite. Filtering by category is crucial, as it contextualizes scores relative to other topics within that specific scope, preventing broader trends from obscuring niche opportunities.

Step 2: Evaluate Topic Viability

Examine the AI Interest Score, a 0-100 index reflecting a topic's prominence in AI-generated answers relative to the category's most discussed topic. Crucially, pair this score with month-over-month (MoM) change and trend line analysis. A topic with a slightly lower score but significant MoM growth may represent a more promising emerging opportunity than a cooling, high-scoring topic.

Step 3: Extract Representative Prompts

Expand selected topics to reveal a summary of user questions and a list of representative prompts. These are the actual phrases AI systems encounter, forming the basis for subsequent analysis and diverging significantly from traditional keyword search volumes.

Step 4: Implement a Prompt Tracker

Load these representative prompts into a dedicated tracker to monitor visibility, sentiment, and citations over time. This moves beyond a static snapshot to a dynamic view essential for assessing content performance.

Step 5: Analyze Competitor Gaps

This critical step translates topic scores into actionable content decisions. Analyze citation gaps, identifying prompts where competitors are cited and your brand is absent. Filter further by mention gaps and status to pinpoint specific areas of opportunity, prioritizing service and post-purchase prompts if that's where gaps emerge.

Direction matters more than raw numbers; monitor the MoM change within citation gaps to identify growing opportunities. Zero-visibility prompts, where no brand has yet consolidated presence, represent a distinct type of opportunity, an open field rather than a direct competition.

Step 6: Establish a Review Cadence

AI interest and citation gaps are fluid. Implement a weekly or monthly review process to track momentum and direction of change. A shrinking citation gap after content publication serves as a key success metric.

Scaling this process involves broadening the topic net within categories, prioritizing topics with strong MoM growth, and running competitor gap analysis separately for distinct brand-competitor pairings. When managing multiple topics, prioritize prompts based on a combination of MoM change, citation gap size, and strategic goal alignment.

Researching keywords in AI search is fundamentally about understanding topics and prompts, not traditional keywords. By analyzing metrics like AI Interest Scores and competitor citation gaps, and tracking performance over time, content creators can make informed decisions about what to produce. This systematic approach, as detailed in resources covering Generative Engine Optimization, provides a clear path to success in the evolving AI landscape.