Keyword research for AI search is the practice of identifying the conversational queries, questions, and prompt patterns people type into AI assistants like ChatGPT, Perplexity, and Google AI Mode, then mapping the entities and subtopics your content must cover to be retrieved and cited in those answers, instead of chasing keyword strings ranked by search volume.
The shift matters because the query itself has changed shape. A searcher who once typed “crm small business” now asks an assistant “I run a 6-person agency, we live in email, which CRM should we pick and why?” Same need, completely different research problem. Classic keyword tools cannot see that prompt, and classic volume metrics cannot prioritize it. This guide covers how people actually query AI assistants, how to mine questions and prompt patterns, why entity coverage now beats keyword volume, and a repeatable workflow. For the hands-on tool walkthrough, pair it with how to use the SEO Keyword Research tool.
Image: A split view showing a classic keyword list with volume columns on one side and a set of full conversational prompts typed into an AI assistant on the other, connected by an entity map
What is keyword research for AI search?
Classic keyword research answers one question: which strings do enough people type into Google to justify a page? You pull volumes, judge difficulty, and assign one primary keyword per URL. That model assumes a search box, a results page, and a click. AI search removes all three. An assistant reads the question, retrieves a handful of sources, composes one answer, and cites two or three of them. Nobody scrolls a list, and the “query” is often a paragraph.
So the research unit changes. Instead of a keyword with a volume attached, you research three connected layers. First, intents: the underlying jobs your buyers ask assistants to do, such as choosing a tool, diagnosing a problem, or building a plan. Second, prompt patterns: the recurring shapes those intents take when typed into a chat box, with their constraints and follow-ups. Third, entities: the concepts, products, and subtopics an assistant expects a credible source to cover before it will cite you. This is the same conceptual move that drives generative engine optimization and answer engine optimization, applied to the research phase rather than the writing phase.
None of this makes classic research worthless. Google still drives enormous traffic, and the language people use in keywords is evidence of the same intents. The mistake is stopping there. Industry analyses report that Google AI Overviews now appear in roughly 45% of searches, which means even inside Google, a large share of queries resolve to a composed answer rather than a plain list of links. Research that only produces a keyword list is preparing you for the shrinking half of search.
How do people search differently in ChatGPT and Perplexity?
Watch someone use an assistant and the difference from a search box is immediate. Queries get longer and more conversational, because there is no penalty for typing a full sentence and a real payoff for adding context. They carry constraints: budget, team size, industry, stack, region. They ask for judgment, not documents: “which should I pick,” “is this worth it,” “what would you do.” And they arrive in threads, where the third message in a conversation is the one that names your category.
A few recurring prompt patterns cover most commercial intent. Recommendation prompts: “best X for Y, given Z.” Comparison prompts: “X vs Y for my situation.” Diagnosis prompts: “why is my A doing B and how do I fix it.” Process prompts: “walk me through doing X step by step.” Validation prompts: “is X still worth doing in 2026.” Each pattern is a family of thousands of phrasings that resolve to the same intent, which is exactly why researching individual strings breaks down and researching patterns works.
The retrieval side differs too. Perplexity searches the live web for almost every answer. ChatGPT mixes what its model already believes about your category with live retrieval through its search crawler, which OpenAI documents separately from its training crawler in its bots documentation. Google grounds AI Overviews and AI Mode in its index. Practical consequence: the same prompt can produce different sources on each engine, so your research has to test prompts per engine, not assume one result. The mechanics of that selection are covered in how AI search engines decide what to cite.
How is AI search query research different from classic keyword research?
The two disciplines share a starting point, understanding how your audience expresses a need, and then diverge on almost everything else. The table below maps the differences that change how you actually work.
| Dimension | Classic keyword research | AI search query research |
|---|---|---|
| Unit of research | Keyword strings, two to four words | Intents, prompt patterns, and entities |
| Query shape | Short, context-stripped fragments | Full questions with constraints, often multi-turn |
| Primary data source | Volume and difficulty databases | Question mining, customer language, live prompt testing |
| Prioritization signal | Search volume and keyword difficulty | Intent value and entity coverage gaps |
| Success metric | Rankings and organic clicks | Citations, mentions, and share of answers |
| Content output | One page per primary keyword | Entity clusters that answer whole prompt families |
Treat the table as an expansion, not a replacement. The overlap is real: a well-built page that answers a prompt family directly also tends to rank for the keyword fragments inside it, and the foundations Google describes in its Search Essentials still gate everything, because content that cannot be crawled and indexed cannot be retrieved by anyone. The full relationship between the two disciplines is mapped in SEO vs GEO: the complete comparison.
How do you mine the questions and prompts your buyers actually type?
There is no Ahrefs for prompts. No tool exports the queries people type into ChatGPT, so prompt research is reconstructive: you gather the questions your market asks in observable places, then expand them into the conversational forms an assistant would receive. The raw material is closer than it looks.
- Customer-facing conversations. Sales calls, support tickets, and onboarding questions are prompts in their natural habitat, complete with the constraints and vocabulary keyword tools strip out. The questions a prospect asks on a demo call are the questions they asked an assistant the night before.
- Communities and forums. Reddit threads, niche Slack and Discord groups, and industry forums show full-sentence questions with upvotes as a crude demand signal. The phrasing people use when asking peers is close to the phrasing they use with an AI.
- Search suggestion surfaces. People Also Ask boxes, autocomplete, and related searches are Google’s own question mining, free to harvest. They skew short, so treat them as seeds to expand, not finished prompts.
- The assistants themselves. Ask ChatGPT and Perplexity what questions people in your category commonly ask, then run the plausible ones and study which sources get cited. The engine is both the research subject and a research tool.
Then expand. Take each mined question and multiply it across the modifiers that change the answer: audience (“for a solo consultant,” “for an enterprise team”), constraint (“free,” “without engineers”), and stage (“getting started,” “switching from X”). The free SEO Keyword Research tool generates question and long-tail variants from a seed topic, and the Query Optimizer rewrites raw keywords into the conversational forms assistants actually receive. Classify the results by intent with the Keyword Intent Analyzer so recommendation and comparison prompts, the ones closest to money, rise to the top of the list.
Why does entity coverage beat keyword volume in AI search?
Volume data fails for AI search for a structural reason: prompt phrasings are so varied that demand never concentrates on measurable strings. A thousand people can ask the same question a thousand different ways, and a keyword database records zeros across all of them. Prioritizing by volume systematically hides the conversational queries where AI answers happen. What the engines reward instead is coverage of meaning.
Retrieval systems embed both the prompt and candidate content as vectors and match by semantic closeness, not term overlap. A site with strong pages across every entity a topic implies, the concepts, comparisons, use cases, and adjacent products, lands near the relevant prompts in vector space no matter how they are phrased. A site with three high-volume keyword pages and nothing else gets retrieved for those three phrasings and misses the long conversational tail entirely. This is the entity model of authority, which what is entity SEO unpacks in full, applied as a research prioritization rule: map the entity space first, then fill the gaps, and let volume break ties rather than lead.
The evidence that content-level signals move AI visibility is real. The Princeton-led generative engine optimization study, first published in November 2023 and presented at KDD 2024, tested optimization tactics across thousands of queries and found that adding citations, quotations, and statistics lifted visibility in generative engine answers by up to roughly 40%. Those tactics work per page; entity coverage decides whether your pages enter the retrieval pool at all. Classic authority proxies like Moz Domain Authority still help you triage which competitors are beatable, but they measure links, not the entity coverage that retrieval actually keys on. Map the entities and subtopics your core topic implies with the free Entity Cluster tool before you commit a quarter of content to a keyword list.
Image: An entity map for a topic with covered nodes filled in and gap nodes highlighted, beside a prompt list grouped into recommendation, comparison, and how-to families
What does a keyword research workflow for AI search look like?
Here is the end-to-end workflow, from raw intent to a content plan built for both rankings and citations. It runs in six steps and repeats quarterly.
Step 1: Define the intents you can win
List the jobs your buyers hire an assistant for: choosing tools in your category, diagnosing the problems you solve, planning the projects you enable. Be ruthless about relevance. An intent you cannot serve with genuine expertise produces content an assistant has no reason to cite over the incumbents.
Step 2: Mine questions and expand them into prompt families
Run the mining pass from the section above: customer conversations, communities, People Also Ask, and the assistants themselves. Expand seeds into conversational variants with the SEO Keyword Research tool and the Query Optimizer, then group phrasings that deserve the same answer into one prompt family. The family, not the phrasing, is what a page targets.
Step 3: Map the entity space and find your gaps
Feed your core topic into the Entity Cluster tool to get the entities and subtopics an authority is expected to cover, then audit your site against the map. Every uncovered entity is a hole in your retrievability. Prioritize gaps that sit inside your highest-value prompt families first.
Step 4: Test prompts live and study the winners
Run your top prompt families through ChatGPT, Perplexity, and Google’s AI surfaces. Record which sources get cited, what format wins (list, table, direct recommendation), and how answers differ per engine. Check the intent behind the classic-search versions with the SERP Intent tool so one page can serve both the ranking and the citation. This step tells you the answer shape to write before you write it.
Step 5: Build pages that answer prompt families directly
Structure each page so an assistant can lift the answer: a direct definition or recommendation up front, headings phrased as the questions in the family, and an FAQ block generated with the FAQ Generator for the long-tail phrasings one page can absorb. Google’s own guidance on AI features is blunt that there is no special markup for AI surfaces, just clear, extractable content. Verify each page with the AEO Ready Checker and a GEO Audit before calling it done. If pages keep getting skipped despite good coverage, why your content is not cited by ChatGPT walks the usual causes.
Step 6: Track a prompt set and re-run quarterly
Freeze 20 to 50 prompts that represent your highest-value families into a tracked set, and score your presence in the answers on a schedule with the free AI Visibility Score. Note who gets cited beside you, watch assistant referral traffic in analytics, and re-run the whole workflow each quarter: prompt patterns drift as models and user habits change, and a six-month-old prompt list goes stale the same way a keyword list does.
Frequently Asked Questions
What is keyword research for AI search?
Keyword research for AI search is the practice of identifying the conversational queries, questions, and prompt patterns people type into AI assistants like ChatGPT, Perplexity, and Google AI Mode, then mapping the entities and subtopics your content must cover to be retrieved and cited in those answers, instead of chasing keyword strings ranked by search volume.
What is the difference between a keyword and a prompt?
A keyword is a short string typed into a search box, usually two to four words, stripped of context. A prompt is a full request typed into an AI assistant: it carries a situation, constraints, and an expected format, often across several sentences. Keywords ask an engine to find pages. Prompts ask an assistant to produce an answer, which it assembles from sources it retrieves and trusts.
How do I find the prompts people type into ChatGPT and Perplexity?
Mine questions from sales calls, support tickets, community threads, People Also Ask boxes, and autocomplete, then expand each into full conversational phrasings with modifiers like budget, industry, and use case. Interview customers about how they actually ask assistants for help, and test candidate prompts in ChatGPT and Perplexity yourself to see which sources get cited and what answer shape wins.
Does keyword search volume still matter for AI search?
Volume still matters for classic rankings, but it is a weak guide for AI search because prompt phrasings are so varied that most individual prompts show zero volume in keyword tools. What matters instead is whether a prompt maps to an intent you can serve and to entities you cover. Many high-value prompts, especially comparison and recommendation prompts, are invisible in volume data.
What is entity coverage in keyword research?
Entity coverage means having strong content for every entity and subtopic a topic implies: the products, concepts, use cases, and comparisons an assistant expects an authority to address. AI systems retrieve by meaning, so a site that covers the full entity map of a topic gets pulled into answers across many prompt phrasings, while a site with scattered keyword pages gets skipped.
How do I measure whether prompt targeting is working?
Build a tracked prompt set of 20 to 50 prompts your buyers actually use, run them through ChatGPT, Perplexity, and Gemini on a schedule, and record whether you are mentioned or cited and beside which competitors. Score it repeatably with an AI visibility tool, and watch referral traffic from assistant domains in your analytics. Rising citation share on your prompt set is the success metric.