The Method
The Asked Method
How Every Question Earns Its Place
A brief is only as useful as the questions inside it. Asked builds every brief from independent signals, labels each question with the evidence behind it, and tells the writer how to answer it in a form answer engines can lift and cite. Everything below is the working logic of the tool, published in full so anyone can inspect it, challenge it or build on it.
The method at a glance
AI search engines rarely answer a question by searching for it directly. Google describes how AI Mode breaks a question into subtopics and runs many searches at once on the user's behalf, then assembles one answer from the pages that address each part. Google's own engineers have explained that these sub-queries often show zero volume in traditional keyword tools, which is why a keyword list built on search volume misses most of what an answer engine actually looks for.
The same explanation carries a second implication that shapes this method: answer engines extract and cite individual chunks of a page, not the page as a whole. A piece gets cited section by section, so the plan has to be built question by question.
Search Demand
Asked collects the questions people type into Google and Bing autocomplete around a keyword, expanded with question words and B2B modifiers such as pricing, integration and compliance. Autocomplete returns phrases ranked by popularity, not volume, so it shows what people ask without pretending to know exact numbers.
Predicted Fan-Out
The built-in engine breaks the keyword down along the B2B evaluation path below, the way an answer engine would. With a free Gemini key, a language model does the same work with more nuance. Fan-out is generative, so the exact sub-queries change from run to run, and Asked treats this signal as a prediction of the territory an answer engine covers, never as a record of what it searched.
Software buyers research in a fairly predictable order, and Asked follows it when predicting fan-out. Every keyword is tested against the facets below.
Fit And Function
What the solution is, and whether it suits a team of this size and type.
Cost
How vendors charge, by seat, usage or volume, and what that means for next year's bill.
Security And Compliance
The standards a buyer has to verify, such as SOC 2 Type II or data residency.
Integration And Rollout
Whether it connects to the existing stack and how much effort setup takes.
Alternatives And Trade-Offs
Named competitors, head-to-head comparisons and honest limitations.
Proof And Outcomes
Benchmarks, case results and the return a buyer can take to budget sign-off.
A narrow technical keyword may open only a few facets, while a category keyword like data warehouse migration usually opens every one of them.
Every question in a brief carries a label, so the writer can see why it is there.
Confirmed - the question appears in real autocomplete and also falls inside a predicted fan-out facet. These form the backbone of the piece.
Search Demand - people type the question today, but it sits outside the predicted fan-out. It often deserves a short answer or an FAQ entry rather than a full section.
Predicted Fan-Out - the question follows the evaluation path, but nobody types it word for word yet. These are the questions competitors miss, carrying the most upside alongside the most uncertainty.
Questions are ordered first by label. Within each label, they follow the evaluation path from fit through to proof, so the ranked list doubles as a natural reading order for the finished piece. Near-duplicates are merged into one question, keeping the clearest phrasing.
Answer engines lift answers that match the shape of the question. Asked reads the wording of each question and assigns the format its answer should take.
A method that cannot be tested is an opinion. After a piece goes live, users can compare its brief against free first-party data.
Bing Webmaster Tools - its AI Performance report shows citations across Copilot and partners, listed by the grounding queries that triggered them.
Google Search Console - its generative AI report shows how often pages appear in AI Overviews and AI Mode.
Asked matches those queries against the questions it predicted and shows which surfaced in real AI answers. Users who choose to share the result add it to an open dataset, and each label's track record is published as the data grows.
Being clear about limits is part of the method. Asked does not report search volume, because autocomplete only signals relative popularity. It cannot see the exact prompts people type into AI assistants, and no public source offers that. Bing's citation report does not cover ChatGPT or other outside assistants, so validation reflects part of the AI search landscape rather than all of it. And because fan-out is generative, a prediction describes likely territory, never a guaranteed list.
Published under CC BY-NC 4.0, so anyone can share and adapt it for non-commercial work with credit. The code behind Asked is open source under AGPL-3.0.
Panda, A. (2026). The Asked Method: Evidence-Backed Question Planning For AI Search. https://doi.org/10.5281/zenodo.23732497