As buyers increasingly use ChatGPT, Claude and Gemini to evaluate products before making purchasing decisions, marketers are experimenting with strategies designed for AI-assisted research. Good At Marketing, a Google Partner agency based in Boynton Beach, Florida, is calling its approach “Verdict Prompting,” a sales technique that gives prospects a structured prompt and lets their preferred AI assistant evaluate the underlying buying question.

The traditional sales pitch assumes the seller controls the conversation. A salesperson presents the problem, explains the solution and attempts to persuade the prospect to take the next step.

AI-assisted buying is changing that dynamic.

Instead of relying exclusively on a company's website, advertising or sales presentation, buyers can now ask an AI assistant to investigate a product category, compare alternatives, identify weaknesses and explain whether a proposed solution makes sense.

Good At Marketing believes that behavior creates a new opportunity for marketers. The agency has coined the term “Verdict Prompting” for a technique that effectively moves part of the sales conversation into the buyer's own AI environment.

The concept is relatively straightforward. Rather than telling prospects why they should purchase a product, a marketer provides a ready-made prompt that the prospect can paste into ChatGPT, Claude, Gemini or another AI assistant. The prompt asks the model to evaluate a specific technical or commercial question.

The resulting response comes from the AI rather than the seller.

That distinction is central to the agency's argument. Good At Marketing founder Donnie Strompf says buyers increasingly place significant trust in AI-generated recommendations, making it more effective to facilitate the research process than attempt to control it.

The approach also comes with an important limitation: the claims included in the prompt need to be accurate.

If the prompt contains misleading assumptions, an AI model may challenge them or produce an unfavorable conclusion. Good At Marketing therefore positions Verdict Prompting as a strategy that works best when the underlying product or service can withstand scrutiny.

That makes the technique different from conventional prompt engineering designed primarily to influence an AI response. The agency's stated approach is less about manipulating an AI model and more about framing a legitimate question that directs the buyer toward an evidence-based evaluation.

“Ask your AI about your own product first,” Strompf said, arguing that a negative AI verdict should be treated as a product problem rather than simply a marketing problem.

The concept arrives as generative AI increasingly becomes part of the buyer research journey. Google remains a major source of commercial discovery, but AI assistants are adding another layer between a prospect and the companies competing for their attention.

For marketers, this creates a new visibility challenge. Traditional search engine optimization focuses on helping webpages rank for queries. AI-driven discovery can involve a model synthesizing information from multiple sources before presenting an answer to a user.

That makes third-party coverage, authoritative content and consistent factual information increasingly relevant to how companies are represented in AI-generated answers.

Good At Marketing's approach combines those two elements. The agency says its proprietary software can generate earned media coverage for clients, while Verdict Prompting gives prospects a structured way to ask their AI assistants about the problem a product addresses.

The agency developed the approach partly through gocta.ai, an AI lead-intake software product founded by Strompf. One of its Verdict Prompts asks prospects to have their AI analyze why embedded iframe forms can disrupt paid advertising attribution and how native, single-line script implementations can preserve attribution.

The example illustrates how the strategy works. Instead of simply claiming that a particular implementation is better, the prompt asks the buyer's AI to investigate the technical problem and explain its implications.

There are clear parallels with answer engine optimization and generative engine optimization, although Verdict Prompting is more directly focused on the buyer's behavior than on optimizing content for AI crawlers.

The competitive landscape is also evolving. Google is integrating generative AI into search, Microsoft has embedded Copilot across its products, and OpenAI, Anthropic and Google are competing to become the interfaces through which consumers and business buyers conduct research.

That means marketers increasingly have two related visibility problems: appearing in traditional search results and becoming part of the information ecosystem AI systems use when generating recommendations.

Verdict Prompting does not solve the latter automatically. A prompt cannot guarantee a favorable AI response, and different models may reach different conclusions depending on their available information, system instructions and sources.

Its potential value lies elsewhere: it encourages marketers to design sales messaging around questions rather than claims.

That shift could prove important as buyers become more skeptical of traditional promotional content. A prospect who independently asks an AI to evaluate a technical problem may be more engaged than one who simply consumes a conventional advertisement.

The model also creates a useful feedback mechanism for marketers. If prospects repeatedly receive unfavorable answers about a product, the problem may reveal weaknesses in positioning, documentation, customer reviews or the product itself.

For enterprise marketing teams, that suggests a broader lesson. AI search optimization should not be treated solely as a content-generation exercise. Companies need accurate product information, credible third-party coverage and clear evidence that can survive independent evaluation by AI systems and human buyers.

Market Landscape

The emergence of AI-assisted purchasing is creating a new layer in the digital customer journey. Search engines remain important, but AI assistants can now summarize product categories, compare vendors and answer technical questions before a buyer visits a company's website.

Google, Microsoft, OpenAI, Anthropic and other technology companies are competing to shape that research experience.

This changes the role of marketing content. A webpage optimized for a keyword may attract a click, while information cited or synthesized by an AI assistant can influence a buyer before the company is directly contacted.

Verdict Prompting sits within this broader shift toward conversational buying. Its distinguishing feature is that the marketer provides the question while allowing the buyer's AI assistant to provide the explanation.

Strategic Outlook

The technique highlights a potentially important transition from persuasion-led marketing to evidence-led discovery.

As AI becomes a more prominent research intermediary, brands may increasingly need to optimize not only for rankings but also for factual consistency, authoritative third-party references and questions that AI systems can answer accurately.

The long-term advantage may belong to companies whose products, data and reputation remain credible when independently examined. In that environment, marketing can open the conversation, but the product itself has to survive the verdict.

Top Insights

• Good At Marketing's Verdict Prompting gives buyers structured AI questions, shifting part of the sales conversation from traditional advertising into ChatGPT, Claude and Gemini.

• The strategy depends on accurate claims, positioning AI scrutiny as a test of product quality rather than a mechanism for manufacturing favorable recommendations.

• AI-assisted buying adds another layer to search behavior, requiring marketers to consider conversational discovery alongside traditional SEO and paid advertising.

• Good At Marketing combines Verdict Prompting with earned media, aiming to influence both the questions buyers ask and information AI systems may encounter.

• The approach reflects a broader move toward evidence-led marketing as buyers increasingly use AI assistants to research products, services and technical decisions.

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