The Conversational Query Optimizer is a free GrowthGPT tool that takes one topic and returns 20 conversational queries people actually ask AI assistants about it, each with a copy-ready answer, a funnel stage, a query type pattern, the conversation context that precedes it, and a warning when the phrasing is ambiguous.
Most content plans are still built from keyword strings, and most content therefore answers questions nobody types anymore. People ask assistants full sentences with context attached. This guide walks through running the Conversational Query Optimizer, reading every field it returns, and turning 20 queries into a content plan that AI search can actually cite. For the strategy behind it, pair this with keyword research for AI search.
Image: A single topic input expanding into twenty conversational query cards, sorted into three columns labelled Curiosity, Comparison, and Decision
What is the Conversational Query Optimizer?
It is a query research tool built for the way people talk to ChatGPT, Perplexity, Gemini, and Google AI Mode. You give it one topic. It gives you back 20 questions phrased the way a real person would type them into a chat box, and for each one it drafts the answer an assistant would want to find on a page.
That second half is the part most teams skip. Knowing the question is half the job. Retrieval systems pull passages, not pages, so the page has to contain a passage that answers the question cleanly enough to be lifted. Google’s own documentation on AI features is explicit that there is no special markup that gets you into AI answers, only content that is clear and extractable. The AI-ready answer field is a starting draft of exactly that passage.
What each field means
- Query. The full conversational question, in natural phrasing rather than a keyword string.
- Stage. Curiosity, Comparison, or Decision. This decides which page type the query belongs on.
- Query type.The structural pattern behind the question: “Is it…”, “How do I…”, “What’s the difference…”, “Should I…”, or Other. Patterns repeat across topics, so they tell you which answer formats you are systematically missing.
- AI-ready answer. A concise, liftable passage that answers the query directly. Edit it with your own data, then place it on the page immediately under the matching heading.
- Previous context. What the user most likely asked just before this query. Chat is a thread, not a single shot, and this field tells you which questions cluster onto one page.
- Disambiguation trigger. A warning that the phrasing is ambiguous, with the specific reason. Treat these as instructions to be more specific in your heading, not as queries to avoid.
How do I use the Conversational Query Optimizer?
Step 1: Enter one topic, not a keyword list
The input takes a single target topic of up to 200 characters. Good inputs look like “email marketing automation for ecommerce” or “standing desks for small offices”: a category plus the qualifier that describes who you serve. Bad inputs are either too broad (“marketing”) or already a query (“best email tool 2026”), because the tool is what generates the queries.
Run it once per topic cluster rather than once per page. Twenty queries is usually a full cluster worth of planning, and the export makes it durable.
Step 2: Read the stage tabs before reading the queries
The results header shows counts for All, Curiosity, Comparison, and Decision. The distribution itself is a finding. A topic that returns 14 Curiosity queries and 2 Decision queries is early-market: people are still learning the problem exists, and a pricing page will not get cited because nobody is asking that question yet. A Decision-heavy topic means the buying conversation is live and comparison pages will earn their keep faster.
Step 3: Expand each query and check the AI-ready answer against reality
Every card expands to show the drafted answer, the previous context, and any disambiguation warning. The draft is a shape, not a source of truth. Replace generic claims with your own numbers, name the specific products and standards involved, and keep the length: the value of these passages is that they are short enough to be lifted whole. If you want the structural rules behind that, how AI search engines decide what to cite covers what gets picked and why.
Step 4: Use previous context to group queries onto pages
Sort the queries by their previous-context field and the clusters appear on their own. Questions that share a predecessor belong in the same conversation, which usually means the same page with sequential headings, not five thin pages competing with each other. This is the fastest fix for sites that publish a page per keyword and wonder why none of them get retrieved.
Step 5: Resolve every disambiguation flag
An ambiguous query is a query an assistant has to guess about, and guessing usually favours the biggest brand rather than the best answer. The fix is specificity in the first 50 words: name the meaning, the audience, and the context explicitly. Verify the page reads unambiguously afterwards with the AEO Ready Checker.
Step 6: Export to CSV and turn it into a build queue
The CSV export carries stage, query type, query, answer, previous context, and disambiguation trigger. Add two columns of your own, target URL and status, and it becomes the content backlog. Feed the same topic into the Entity Cluster tool to check that the queries cover the entities an authority is expected to address, and to the Content Gap Analyzer to see which of them competitors already answer.
Which stage maps to which page type?
Stage is the field that decides what you build. Mapping it wrong is the most common reason a well-researched query set produces content that never converts.
| Stage | What the person wants | Page type to build | Answer shape that gets cited |
|---|---|---|---|
| Curiosity | To understand the problem and the vocabulary | Definition guide, explainer, glossary entry | A 40 to 60 word definition in the first paragraph |
| Comparison | To weigh options against each other | Versus page, alternatives page, buying guide | A comparison table plus a clear “pick X if” verdict |
| Decision | To act with confidence and avoid a mistake | Product page, pricing page, implementation guide | Specific steps, requirements, and named constraints |
The pattern holds across engines because it is a property of the question, not the model. The Princeton GEO study found that citation-worthy content is defined by structure and evidence rather than keyword density, with quotations, statistics, and citations lifting visibility by up to 40 percent in generative answers.
How is this different from keyword research?
Keyword tools tell you how many people typed a string into a search box last month. That is still useful for sizing classic demand, and the SEO Keyword Research tool exists for exactly that job. What volume data cannot do is tell you the shape of a conversation, because most conversational phrasings show zero volume even when the underlying intent is enormous.
Run both and the workflow gets simple. Keyword research sets priority, query optimization sets structure. One tells you which cluster deserves the next two weeks, the other tells you what the H2s and the opening paragraphs need to say. If you are still deciding how much weight to give each, do Google rankings still matter makes the case for keeping both engines in play.
How do I turn 20 queries into published pages?
Group by previous context, then by stage. Most topics collapse into four to six pages: one or two Curiosity explainers, two Comparison pages, and one or two Decision pages. Each page gets the grouped queries as H2s in the order the conversation moves, each H2 followed immediately by the edited AI-ready answer, and the long-tail leftovers folded into an FAQ block built with the FAQ Generator and marked up with the FAQ Schema Generator.
Before publishing, confirm the page is crawlable and the markup is valid. Google’s Search Essentials remain the baseline for both classic and AI surfaces, and OpenAI documents its crawler behaviour in the OpenAI bots documentation, which matters because a page blocked from OAI-SearchBot cannot be cited no matter how good the answer is. Run a GEO Audit on the finished page, then track the queries you targeted with the AI Visibility Score so you can prove the cluster moved.
Frequently Asked Questions
What is the Conversational Query Optimizer?
The Conversational Query Optimizer is a free GrowthGPT tool that takes one topic and returns 20 conversational queries people actually ask AI assistants about it. Each query arrives with a copy-ready AI answer, a funnel stage of Curiosity, Comparison, or Decision, a query type pattern, the conversation context that precedes it, and a disambiguation warning when the phrasing is ambiguous.
How do I use the Conversational Query Optimizer?
Enter a single target topic of up to 200 characters, such as a product category or a problem you solve, and generate. The tool returns 20 queries you can filter by stage, expand to read the AI-ready answer for each, copy individually, or export as CSV. Use the Curiosity queries for top-of-funnel content, Comparison queries for versus and alternatives pages, and Decision queries for product and pricing pages.
What do the Curiosity, Comparison, and Decision stages mean?
Curiosity queries come from people still learning the problem exists and want definitions and explanations. Comparison queries come from people evaluating options and want differences, trade-offs, and alternatives. Decision queries come from people close to acting and want recommendations, pricing clarity, and implementation specifics. Each stage needs a different page type and a different answer shape.
What is a disambiguation trigger?
A disambiguation trigger is a flag on queries whose wording could mean more than one thing, so an assistant has to guess which meaning you intended or ask a follow-up. These queries are risky to target blindly. Either name the specific meaning in your heading and opening line, or build separate pages for each meaning so retrieval has an unambiguous match.
How is this different from a keyword research tool?
A keyword tool returns strings ranked by search volume. The Query Optimizer returns full conversational questions with the answer an assistant would want to lift, because AI search retrieves by meaning and composes an answer instead of listing links. Use both: keyword research to size classic search demand, query optimization to shape the answers AI assistants cite.
How many free runs do I get?
The Conversational Query Optimizer runs three times per day free without an account and ten times per day when signed in. There is no email gate and no credit card. Results are exportable as CSV so one run per topic is usually enough to plan a full content cluster.