Most marketers reading "AI search keyword research" do not know where to start. The category is two years old. The terminology is unsettled (AEO, GEO, AI search optimisation, LLMO — same thing, four names). The tool space is fragmented — no single product covers the full workflow, the way Ahrefs or Semrush covers traditional research. The result is a lot of "I should be doing this" and not much "this is what I am doing on Tuesday morning".
Here is the concrete playbook. Seven steps, executed in order, mostly using tools that already exist. The total time investment for a single topic is two to four hours the first time you run it, dropping to twenty minutes per topic once you have built the muscle memory. Cost in API fees: $0.20-0.50 per topic if you use paid sources, $0 if you stick to free.
This is the version that produces a content brief at the end. Not "10 questions to write about" — a real brief with H2 structure, citation-worthy data points, schema type, and the predicted citation outcome.
Step 1 — Pick the seed, not the keyword
Traditional keyword research starts with a keyword. AI search keyword research starts with a topic — usually a 3-5 word phrase that captures a real customer concern. "running shoes for flat feet", not "best running shoes flat feet 2026". The seed should be specific enough to have an answer, broad enough to have multiple sub-questions.
The seeds that work best are buyer-journey moments. "Choosing between WordPress and Webflow", "moving a 50k-page site without losing SEO", "what to look for in a wholesale jewelry supplier". Question-adjacent, decision-adjacent. Not single-keyword, not branded.
Step 2 — Harvest questions from five sources in parallel
Five places, in order of signal-to-noise.
DataForSEO People Also Ask + autosuggest. The canonical Google "real questions" signal. Cost: about $0.05 for one seed expansion. Free alternative: AlsoAsked free tier (3 searches/day).
Reddit search via the Reddit API. Free, deeply useful for buyer questions in their natural form. Search the subs that match your topic — r/SEO, r/marketing, r/buildapcsales, etc. Top 30 threads by upvotes, last 12 months.
Stack Exchange API. Free, best for technical or how-to topics. Filter by accepted-answer threads to see the questions people actually got answered.
Quora — manual or via SERP scrape. Quality has dropped since 2020 but still useful for B2C and lifestyle topics. SerpAPI costs $20/mo entry.
Tavily semantic search or Claude with web_search. Surfaces questions in long-form articles, podcasts, YouTube descriptions that the structured PAA misses. Cost: $0.005-0.01 per search.
Combine the five outputs into one list. Expect 80-150 raw questions for a typical seed.
Step 3 — Cluster with an LLM, not by hand
A list of 100 questions is unactionable. You need 8-15 question clusters where each cluster represents a distinct sub-topic. Doing this by hand takes an hour and is inconsistent. Doing it with Claude or GPT-4 takes 30 seconds and is more consistent.
The prompt template that works: "Group the following 120 questions about [topic] into 8-15 semantic clusters. For each cluster, give it a short label, list the constituent questions, and identify the canonical question that summarises the cluster best." Pipe the output into a markdown table or Google Sheet.
Cost: about $0.02 in Claude API calls per topic.
Step 4 — Score each cluster with three numbers
For each canonical question (one per cluster), pull three metrics from DataForSEO.
Search volume — how many monthly searches the canonical question gets. Categories below 50 are still worth covering if they cluster around buyer-decision moments; categories above 1000 are worth prioritising.
Keyword difficulty (KD) — Ahrefs/Semrush-equivalent score from 0-100. Below 30 is realistic for DR-15 sites; 30-50 is achievable in 6-12 months of authority building; above 50 is hard for non-enterprise sites.
AI Overview presence — yes or no, currently surfacing in Google AI Overview for that question. DataForSEO returns this directly. Yes-flagged questions are higher priority because that is where AI is intercepting traffic.
Step 5 — Query the AI engines for the top 10 clusters
For your top 10 clusters by combined score, query each AI engine and capture the answer plus cited sources.
ChatGPT — easiest via the OpenAI API with GPT-4o-mini. Cost: about $0.001 per query. Returns the answer; you have to infer cited sources from the prose (often imperfect).
Perplexity — the cleanest source citations. Use the Perplexity API. Cost: about $0.005 per query. Returns answer plus a list of cited URLs with no inference required.
Claude with web_search — Anthropic API with the web_search tool enabled. Cost: about $0.01 per query. Returns answer plus inline source citations. Particularly good at surfacing recent and technical sources.
Google AI Overview — DataForSEO captures this in their SERP responses. No additional cost beyond the SERP query.
For each of the four engines, save the answer text and the cited domains.
Step 6 — Build the citation gap matrix
You now have, for each top-10 cluster, four AI-engine answers and four lists of cited domains. Combine into one matrix.
Rows: clusters. Columns: cited domains across all four engines. Values: count of citations across the four engines.
Sort the columns by total citation count descending. The top 10 columns are the domains currently winning AI search visibility for your topic. If your domain is not in the top 10, that is your competitive gap.
For each cluster, identify the domain that has the highest citation count and read the page they are being cited from. The patterns you find — H2 structure, citation density, schema usage, the specific data points being cited — become the template for your own content.
Step 7 — Generate the content brief
For each of the top 5-10 clusters by score, produce a content brief that includes: page title, URL slug, primary keyword, secondary keywords, target word count, H2 structure (one H2 per sub-question in the cluster), citation-worthy data points to include, schema markup type, internal links to add, and a predicted citation outcome (which AI engines this content is targeted at).
LLM-generated briefs save 30-60 minutes per brief over hand-written. The right prompt template specifies the structure above explicitly.
What the workflow looks like in practice
Hands-on, the 7 steps run as: 30 minutes harvesting (parallel API calls), 10 minutes clustering (LLM), 20 minutes scoring (DataForSEO bulk endpoints), 30 minutes querying engines (parallel), 30 minutes citation gap analysis (matrix work), 60 minutes brief generation (LLM-assisted). Three hours for one topic the first time. Twenty minutes per topic by the tenth time.
Or, if you do not want to run all of it manually, the free tool at /tools/ai-search-keyword-research/ runs steps 1-6 in 30 seconds end-to-end and produces the brief output that step 7 needs. The tool is fed by the same APIs (DataForSEO, Reddit, Perplexity, Claude) under the hood.
Frequently asked questions
Can I do AI search keyword research with only free tools?
Mostly yes, partially no. The free stack is: AlsoAsked free tier + Reddit API + Stack Exchange API + ChatGPT free tier (manual queries) + Perplexity free tier (manual queries). You lose three things: DataForSEO search volume + KD per question, programmatic Perplexity/Claude queries (manual is slower), and brief generation automation. For five topics, the free stack is fine. For ongoing research at scale, paid APIs save hours.
How often should I refresh AI search keyword research for a topic?
AI engine retrieval changes faster than Google SERPs. For active topics, refresh every 4-8 weeks. For evergreen topics, every 90 days. The signal that triggers a refresh is when the AI Overview answer for your top cluster changes meaningfully — that means the engines have updated their retrieval set.
Should I do AI search keyword research before or after traditional SEO research?
In parallel. The two workflows share data sources (DataForSEO, autosuggest) and can be run as one combined process. Most teams that do both at separate times duplicate work; the integrated workflow is the future state.
What does a content brief look like at the end of this?
Three pages, structured. Page 1: meta — title, slug, primary/secondary keywords, target word count. Page 2: structure — H2-level outline with one H2 per sub-question, suggested H3s, citation-worthy data points to include, named entities, schema type. Page 3: citation strategy — which AI engines this content is targeted at, which competitor patterns it is borrowing from, predicted citation outcome.
What is the biggest mistake teams make doing this?
Treating it as a keyword exercise. The output is not a list of keywords. The output is a list of content briefs that have predicted citation outcomes, written for passage extraction, structured for retrieval. Teams that produce keyword lists at the end of this workflow have done one third of the work.
What to do next
If you want the bigger-picture explainer first, read the pillar post on AI search keyword research in 2026.
If you want to run the workflow on your own topic, the free tool at /tools/ai-search-keyword-research/ runs all 7 steps in about 30 seconds.
If you want the comparison between AEO, GEO, and traditional SEO keyword research as disciplines, that is the next article in this cluster.
Or book a 30-min call if you want to talk through what this looks like applied to your specific brief.