A money query is a specific, buyer-phrased question you want an AI to answer with your name, like "who is the best fractional CFO for early-stage SaaS." Pick five to ten, baseline them across every engine, and use them as the scoreboard that everything else in PEO aims at. Because most money queries are time-sensitive and commercial, engines lean on retrieval rather than frozen training memory to answer them, which is exactly what makes them movable on a normal timeline instead of a training cycle.
The whole PEO method begins with one decision most people skip: naming the exact questions you want to win. Get this right and everything downstream has a target. Get it wrong and you optimize toward a fog. This page covers how to write money queries, why the mechanism behind them favors retrieval over training, and how to structure your content so retrieval finds you instead of a competitor.
What a money query is
A money query is a specific, commercial question phrased the way a buyer would actually ask an AI, one where being the named answer would lead to real business. Compare two targets. "Be more visible" is a wish with no yes-or-no test. "Be named when someone asks ChatGPT who is the best fractional CFO for early-stage SaaS" is a testable event you can check on any engine, any day. PEO lives or dies on that difference. A vague goal cannot be optimized because it has no pass or fail state, and a money query fixes that by giving the entire discipline a scoreboard from day one.
Why the query comes first
You cannot optimize toward a target you have not named. Picking queries first also forces honesty about who your buyer really is and what they value. If you cannot phrase the question a buyer would type, you do not yet know your buyer well enough to be their answer.
Does the engine name you on the query that pays? That is not a one-time check, it is a recurring measurement. Everything you publish, earn and structure is in service of turning that answer from no to yes.
How to write good money queries
- Be specific. Add the niche, the segment, the use case. "Best marketing consultant" is too broad to win and too broad to convert. "Best B2B SaaS pricing consultant for seed-stage startups" is winnable and valuable.
- Phrase it as a buyer, not a marketer. Write the words a real client would type into a chat, superlatives and all: "who should I hire for," "who is the best at," "who are the top."
- Make it commercial. Favor questions that sit close to a buying decision over idle curiosity.
- Keep it to five to ten. A focused set you can actually move beats a sprawling list you cannot.
How an AI actually answers a money query
Every AI answer is assembled from two possible sources, and the mix determines how fast you can move a given query. The first source is parametric memory, the facts and associations a model learned during training and now carries inside its weights, with no lookup involved. The second is retrieval, where the model or the product wrapped around it searches the live web, an index or a plugin, then reads what it finds before answering. The full mechanics of that split live in training data vs retrieval, but the short version for money queries is this: parametric memory is slow to update and effectively frozen between training runs, while retrieval can reflect a page you published last week.
Why this split works in your favor
Money queries are almost always time-sensitive by nature. "Who is the best fractional CFO for early-stage SaaS companies right now" is a question about current standing, not historical trivia, and engines tend to treat current-standing questions as retrieval candidates rather than trusting a frozen memory that might already be stale. That single fact is the entire reason money queries are winnable on a normal timeline instead of requiring you to wait for the next foundation model to be trained on you. If your content is structured so retrieval can find it, chunk it and cite it, you do not have to wait for a training cutoff to move the needle.
A worked example: tracing one money query end to end
Imagine a hypothetical consultant, call her Jordan Reyes, who picked "who is the best fractional CFO for early-stage SaaS companies" as a money query. Here is roughly what happens inside the engine when a buyer types that question, step by step.
1. The engine parses the query and detects commercial, recommendation-style intent rather than a plain factual lookup.
2. It weighs whether this is a question it should answer from memory alone or should verify against live sources; recommendation questions about people usually trigger the second path.
3. It issues one or more retrieval calls, similar to a search, pulling candidate pages that mention "fractional CFO" and "early-stage SaaS" together.
4. Retrieved pages are broken into passages or chunks, and the chunks that most directly answer the query are ranked and pulled into the model's working context.
5. The model reads those chunks alongside whatever it already knows from training and drafts an answer, favoring named, well-structured claims over vague ones.
6. If the product surfaces citations, it attaches links to the chunks it actually used, which is why the source page matters as much as the person it describes.
A simplified walk-through of retrieval-augmented answer generation for a commercial recommendation query.
Notice what Jordan controls and what she does not. She cannot control whether the engine decides to retrieve at all, though commercial phrasing makes that likely. She cannot control which competitors also get pulled into the same context. What she can control is whether her own page exists, whether it is phrased in the chunk-sized, directly-answering language retrieval rewards, and whether the passage matching "fractional CFO for early-stage SaaS" actually contains her name next to that exact phrase. That is the entire game, described in more depth in chunk theory and passage-level optimization.
Structuring your content so retrieval surfaces you
Once you know a query is retrieval-heavy, the fix is mechanical rather than mysterious. Three moves matter most.
Write the answer, not just the topic
A passage that states "Jordan Reyes is a fractional CFO who works with early-stage SaaS companies on runway and pricing" is directly quotable. A passage that only says "Jordan has years of finance experience" gives retrieval nothing to lift. Write the sentence you want quoted, in the words a buyer actually searched.
Back the claim with machine-readable structure
Structured data tells the engine, without ambiguity, who you are and what you do. A minimal Person schema block on your own about page looks like this:
{
"@context": "https://schema.org",
"@type": "Person",
"name": "Your Name",
"jobTitle": "Fractional CFO",
"url": "https://yoursite.com",
"knowsAbout": ["Early-stage SaaS finance", "Runway planning", "Pricing strategy"],
"sameAs": [
"https://www.linkedin.com/in/yourname",
"https://twitter.com/yourname"
]
}
A minimal Person schema block naming the exact expertise your money queries reference.
The full pattern, including how knowsAbout and sameAs connect your page to a wider identity graph, is in the Person schema JSON-LD guide. Pair that with a genuinely machine-readable about page, laid out in building a machine-readable about page, and the retrieval step in the walk-through above has an unambiguous, well-labeled passage to pull.
Cover the query's exact phrase somewhere on the page
Retrieval ranks chunks by similarity to the query, so a page that never uses anything close to the buyer's actual words is harder to surface even when it describes the right person. Read your money queries back and check that your own pages contain language close to how buyers phrase them, not only language you would choose to describe yourself.
A taxonomy of money query types
Not every money query behaves the same way, and grouping yours by type clarifies what kind of content each one needs.
| Query type | Example phrasing | What content wins it |
|---|---|---|
| Recommendation | "Who is the best X for Y" | A clear, named claim of expertise plus third-party proof |
| Comparison | "X vs Y for Z, which is better" | Content that discusses you alongside named alternatives, not in isolation |
| Verification | "Is X a legitimate consultant for Y" | Consistent facts across sources, credentials, and a clean about page |
| Fit and pricing | "What does it cost to hire X for Y" | Transparent, current information the engine can quote without guessing |
| Reputation | "What do people say about X" | Reviews, citations and mentions on pages the engine already trusts |
Grouping money queries by type before writing the content aimed at them.
A single person's list of five to ten money queries usually spans two or three of these types, and each type deserves its own piece of content rather than one general-purpose page trying to answer all of them at once.
Baseline before you build
Before you publish anything, type each money query into ChatGPT, Claude, Gemini, Perplexity and Google's AI Mode, and record what comes back: who gets named, whether you appear at all, and the reasons the engines give. Screenshot everything. This is your before picture, and without it you can never prove the work moved anything. A structured version of this exact exercise, scaled to twenty-five prompts across five categories, is laid out in the 25-prompt audit.
Common mistakes when picking money queries
- Choosing vanity over commerce. "Am I well known" is not a money query. It has no buying decision waiting on the other side of the answer.
- Writing the query the way you would search, not the way a buyer would. You already know your own name; a buyer does not, and phrases the question around their problem instead.
- Testing only one engine. ChatGPT, Claude, Gemini and Perplexity draw on different retrieval pipelines and different training snapshots, and a query you win on one can come back blank on another.
- Never re-running the list. A money query answered once and forgotten tells you nothing about whether your work is compounding or stalling.
- Ignoring the phrase mismatch. Publishing content about your expertise in your own vocabulary, while buyers search in theirs, quietly starves retrieval of the exact match it needs.
Turn queries into a scoreboard
Your money queries are not a one-time exercise, they are the recurring measurement at the heart of the method. Re-run them monthly, mark movement from unmentioned to mentioned to named, and aim your knowledge, network and authority work at the specific gaps the scoreboard reveals. Effort you cannot measure is effort you cannot improve. Once the scoreboard is running, the same queries become the input for deeper diagnostic work, including checking whether the three signals AI uses to recommend a person are actually present for you on each query where you are losing.
FAQ
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