Keyword-centric SEO assumed a stable interface: users typed short queries, clicked a ranked list, and evaluated options on destination pages. Assistant-mediated discovery breaks that contract. In Google’s AI Overviews and conversational systems such as ChatGPT, Gemini, and Perplexity, the user increasingly receives a resolved answer (often with a shortlist) before any click occurs. As a result, the most valuable unit of optimisation shifts from the keyword to the prompt: a decision-shaped question that invites recommendations, comparisons, and justification.
This article introduces “money prompts” - a deliberately chosen set of commercially meaningful questions that an organisation decides to win - and presents a rigorous, repeatable method for selecting them. The method is designed to be pragmatic (fast enough to execute in weeks) but academically grounded (explicit criteria, falsifiable scoring, and benchmarkable outputs). It also avoids a common failure mode: optimising for prompts that are unwinnable because the model cannot safely recommend the brand, cannot verify the claims, or cannot reliably retrieve the necessary information.
Keyword strategies were optimised for document retrieval. In classical web search, the ranking system’s primary job is to select documents that satisfy an information need. The user then performs the expensive work: reading, comparing, and deciding. In contrast, modern assistants optimise for resolution: reduce uncertainty until the user can proceed to an action (choose, buy, book, contact) or the next question. This reallocation of cognitive work changes which queries matter most and how influence is exerted.
Two empirical trends make the shift visible. First, the web has moved steadily toward zero-click behaviour, where the user’s need is satisfied on-platform without navigating outward. Second, AI Overviews push synthesis to the top of the search experience and compress the attention available for organic results. In that environment, many high-value interactions happen before a site visit - meaning the optimisation target must be earlier than the click.
Keywords are primarily lexical objects: strings that approximate what a user might type. Money prompts are decision objects: questions that predict a choice. They tend to have three characteristics:
They invite a shortlist or recommendation (for example, best, top, recommend, alternatives, compare).
They express constraints (industry, geography, budget, compliance, timeline, integration requirements).
They imply a selection criterion that can be justified (proof, track record, safety, outcomes).
The practical consequence is that money prompt selection is closer to product strategy and buyer psychology than to keyword research. You are not trying to capture language at scale; you are choosing the questions whose answers most strongly shape revenue outcomes.
A money prompt is a commercially meaningful question that (a) your ideal customer profile (ICP) plausibly asks pre-purchase, (b) an assistant can answer by naming options, and (c) if your brand is included and framed correctly, the buyer is meaningfully closer to conversion.
Money prompts are not high volume by definition. In assistant-mediated discovery, leverage often concentrates in mid-volume, high-intent prompts where the assistant forms a shortlist or specifies evaluation criteria. This is consistent with established distinctions in web search intent. Broder’s taxonomy (informational, navigational, transactional) remains useful, but assistant prompts often blend categories - for example, informational questions that explicitly request transactional options (recommendations) or navigational questions disguised as evaluation (Is {brand} good for {use case}?).
The defining move is not discovering a large inventory of prompts - it is locking a small, explicit target set. At Recrawled, projects typically lock 10 to 30 money prompts after an initial diagnosis phase, distributed across the highest-value intent clusters. This list becomes a benchmark (for measuring model outputs over time) and a governance artifact (for what claims must be true, provable, and consistently presented).
In practice, prompt performance depends on how the assistant interprets intent and what retrieval and verification pathways are available. The same category can produce dramatically different outputs depending on constraint specificity and risk. A useful way to think about prompt design is as a structure with slots that shape resolution:
Category slot: what is being chosen (product or service type).
Use-case slot: what it’s for (industry, workflow, audience, problem).
Constraint slot: budget, timeline, location, integrations, compliance, scale.
Comparator slot: alternatives, incumbents, substitutes (X vs Y, alternatives to X).
Evidence slot: what counts as proof (certifications, outcomes, third-party validation).
Action slot: the desired next step (demo, quote, booking, trial).
Money prompts are rarely single-slot. The highest-leverage prompts typically include category plus use case plus at least one constraint, because that combination naturally invites a defensible shortlist.
Best {category} for {use case}
{category} providers for {industry} in {geo}
{category} alternatives to {incumbent}
{incumbent} vs {challenger}: which is better for {use case}?
How to choose a {category} provider for {constraint}
Is {service} worth it for {scenario}? What should I expect?
What are the risks of {approach}, and how do I mitigate them?
A money prompt programme should start from buyer language, not internal messaging. In B2B especially, internal teams often over-index on category terms they want to own, while buyers use problem language, constraints, and comparisons. The goal of a longlist is breadth: capture the plausible questions an ICP asks across awareness, discovery, and evaluation.
Sales call transcripts and discovery notes (what prospects ask when they are uncertain).
Customer support and onboarding questions (what buyers wish they knew earlier).
RFPs, procurement questionnaires, and security and compliance checklists (what must be answered to buy).
Internal site search logs (what visitors type when navigation fails).
Competitor review sites and directories (the language used in comparisons).
Industry forums and community Q and A (problem framing, constraints, substitute solutions).
Search console query data (useful, but often too keyword-like without qualitative interpretation).
If you only have time for one method, start with sales and support. Those channels surface the questions closest to purchase, including objections, constraints, and evaluation criteria.
A typical longlist contains hundreds of candidates. The mistake is attempting to select the best 30 directly. Instead, first cluster by intent. In Recrawled’s workflow, the canonical answer system (CAS) is drafted as intent-cluster modules - each module covers 10 to 30 prompt variants - before the final money prompt lock. This sequencing ensures that prompt selection is supported by an answer strategy and proof plan, rather than being an isolated research exercise.
The most expensive mistake is optimising for prompts you cannot plausibly win. In assistant-mediated discovery, winning requires more than relevance. The assistant must be able to retrieve your entity, disambiguate it, and verify enough proof to recommend it without taking on risk. We call this achievability the resolution fit.
Before scoring prompts, apply a fast pre-filter. For each candidate prompt, ask:
Retrieval: Do you have an accessible, quotable surface that directly answers the prompt (or could you publish one quickly)?
Entity clarity: Is your identity unambiguous across the surfaces assistants tend to use (website plus key profiles and directories)?
Proof: Can you support the key claims with defensible proof anchors and at least some independent corroboration?
Risk: Would answering this prompt require superlatives or regulated claims you cannot safely make?
Competition: Does the prompt structurally default to incumbents or aggregators (for example, ultra-broad best CRM) without constraints?
If the prompt fails on retrieval, proof, or risk, it should be revised into a more winnable form (add constraints, specify the use case) or rejected. This is the difference between strategic selection and vanity optimisation.
After the resolution-fit filter, scoring becomes a disciplined way to prioritise. The point is not mathematical precision; it is explicit trade-offs. Recrawled uses a worksheet approach: score each prompt on a small set of criteria (1 to 5) and apply weights if necessary to force prioritisation toward revenue-relevant prompts.
Score each candidate prompt on the following dimensions:
ICP recognisability: would your ICP actually ask this pre-awareness or pre-purchase?
Funnel stage weight: awareness, discovery, and evaluation prompts typically carry the most leverage.
Competitive nature: does it invite multiple options (recommendations, comparisons)?
Purchase intent: emerging or immediate intent to buy.
Achievability or resolution fit: can assistants plausibly surface you after optimisation?
Differentiation opportunity: can you win credibly (not just appear)?
Feasibility: can you supply proof and deploy necessary surfaces quickly?
Risk: lower risk (compliance, safety, overclaiming) scores higher.
The output is a locked set of 10 to 30 prompts distributed across the highest-value intent clusters. Distribution matters: if your list is dominated by a single archetype (for example, best X), you have created fragility. A robust list includes selection, comparison, how to choose, objections and risks, and use-case-specific prompts.
Once prompts are locked, they become a benchmark set. The benchmark is used to measure how assistants currently answer, and how those answers change after interventions. Crucially, this is measurement, not discovery. Prompt discovery happens earlier; benchmarking happens after lock.
For each prompt, define the minimum structure of a good answer. At a minimum:
Intent type: recommendation, comparison, how-to-choose, risk or objection, localised provider search, etc.
Good-answer criteria: what a helpful, buyer-aligned answer must include.
Must-mention facts: the proof points that are both true and defensible (for example, certifications, outcomes, scope constraints).
Disallowed claims: statements you must never allow the assistant to attribute to you (regulated or unprovable).
Notes on baseline outputs: whether you appear, how you’re framed, and which sources the assistant uses.
This structure creates two benefits. First, it makes measurement reproducible: you can score outputs consistently over time. Second, it forces governance: you cannot optimise for a prompt without specifying what must remain true and what must never be claimed.
Assistants are sensitive to phrasing. For a benchmark, it is usually sufficient to test each money prompt plus one or two paraphrases that preserve intent (for example, best vs recommended, or adding a clarifying constraint). The goal is not to brute-force every permutation; it is to detect whether your inclusion and framing is robust to normal human variation.
Teams often select prompts because they sound prestigious (best {category}) rather than because they reflect buyer behaviour. These prompts are frequently unwinnable for challengers because they default to incumbents, aggregators, or generic guidance. Fix: add constraints (use case, industry, geo, compliance) so the assistant can justify non-incumbent options.
If your prompt list is built from internal positioning language, you will optimise for questions buyers do not ask. Fix: prioritise language from sales and support, RFPs, and external comparisons; treat internal messaging as a mapping layer, not a discovery source.
Assistants are risk-averse. Prompts that force unverifiable superlatives (number one, most trusted, best in the world) invite conservative outputs or omission. Fix: revise prompts toward verifiable dimensions (for example, best for regulated healthcare workflows), and build proof anchors that travel.
If your list is dominated by either pure top-of-funnel curiosity or pure bottom-of-funnel brand navigation, you miss the leverage zone where assistants form a shortlist. Fix: overweight discovery and evaluation prompts - where the assistant is likely to recommend, compare, and justify.
A money prompt list only creates value if it is connected to an execution system. In Recrawled’s blueprint, locked prompts map to:
Canonical answer system (CAS) modules (the reusable answer plus proof package for an intent cluster).
A truth spine of high-integrity pages and profiles that assistants repeatedly retrieve.
A minimum viable entity graph (MVEG): the smallest set of authoritative nodes where identity and facts must be consistent.
A backlog: which surfaces and proof assets to publish or repair first, based on prompt leverage.
This mapping prevents a common trap: writing content that feels relevant but does not change assistant behaviour because it does not land on the retrieval surfaces the model actually uses.
In the answer economy, keywords remain useful, but they are no longer the primary strategic object. The strategic object is the prompt: a decision-shaped question whose answer forms a shortlist, establishes evaluation criteria, or resolves an objection. A money prompt list is therefore a commitment to win a defined set of revenue-relevant questions - supported by accessible surfaces, defensible proof, and a governance layer that keeps claims boringly correct over time.
Teams that treat money prompts as a benchmarkable system - rather than a brainstorming artifact - gain two compounding advantages: they can measure what assistants do, and they can reliably change it.
Broder, A. Z. (2002). A taxonomy of web search. SIGIR Forum, 36, 3 to 10.
Dubois, D., Dawson, J., and Jaiswal, A. (2025). Forget what you know about search. Optimize your brand for LLMs. Harvard Business Review (digital article).
McKinsey and Company. (2009). The consumer decision journey. McKinsey Insights.
Google. (2024). AI Overviews in Google Search: launch and global expansion updates (Google Blog).
Fishkin, R. (2024). Zero-click search study (Datos and SparkToro clickstream analysis). SparkToro.
Broder (SIGIR Forum PDF): https://sigir.org/files/forum/F2002/broder.pdf
Harvard Business Review (Dubois, Dawson, Jaiswal, 2025): https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms
McKinsey (consumer decision journey): https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-consumer-decision-journey
Google Blog (AI Overviews expansion): https://blog.google/products-and-platforms/products/search/ai-overviews-search-october-2024/
SparkToro (zero-click search study): https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/
eMarketer Research, January 2026
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At the heart of our work is our powerful multi-layer blueprint which continuously self-adapts to the rapid, ongoing developments in AI technology. Our blueprint both improves and augments each client's entire digital footprint with laser-focused targeting to increase visibility, trust and recommendations on AI assistants. The ultimate goal is to increase client revenue.
AI presence is becoming more important than search rankings
Products and services have to aim to be recommended on AI
AI platforms are replacing traditional brand loyalty
Brands have to aim to be trusted on AI platforms
AI overviews are reducing visits to company-owned media
Businesses increasingly have to compensate via AI visibility
AI assistants like ChatGPT and Gemini don’t rank websites in the same way search engines do. They typically resolve answers using signals like entity clarity (who you are), consistency (same facts everywhere), evidence (proof and specificity), machine accessibility (content they can parse), and external trust validation (credible third-party corroboration).
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Often we can commit to specific performance guarantees. We increase the probability and consistency of being cited and recommended by improving the signals that AI systems rely on, and we keep going until we achieve a meaningful competitive advantage for our clients (resulting in a multiple ROI). Customer success is extremely important to us - it's the reason we exist!
