Prompt Engineering for Brands
Understanding how users query AI models about your category—and how to position your brand in those conversations.
The New Customer Journey Starts With a Prompt
Your potential customers aren't typing "best CRM software" into Google anymore. They're having conversations with AI models: "I need a CRM that integrates with our existing tools, is affordable for a 20-person team, and doesn't require technical expertise to set up."
This shift changes everything. Instead of optimizing for keywords, you need to understand the intent patterns and query structures that lead AI models to recommend solutions in your category.
Mapping Your Category's Prompt Landscape
The first step is understanding how people are actually prompting AI models about your category. This requires systematic research across several dimensions:
- Problem-focused prompts – "How do I solve X problem?"
- Solution comparison prompts – "What's the difference between X and Y?"
- Use case prompts – "What's the best tool for X situation?"
- Feature-specific prompts – "Which platforms offer X feature?"
Understanding AI Recommendation Logic
When an AI model decides which brands to mention, it's drawing from its training data to identify the most relevant, authoritative, and appropriate matches for the query. Your positioning needs to create clear signals that make your brand the obvious choice for specific prompt patterns.
This means building a comprehensive footprint that clearly establishes:
- What problems you solve
- Who you're best suited for
- What makes you different
- How you compare to alternatives
Positioning for Prompt Patterns
Different prompt patterns require different positioning strategies. If users often ask about "affordable" solutions, your content ecosystem needs to clearly establish your pricing positioning. If they ask about integrations, you need comprehensive integration documentation.
The goal is to create such clear, consistent signals about your positioning that AI models can confidently include you in relevant recommendations.
The Specificity Advantage
Here's a counterintuitive insight: being hyper-specific about what you're best at often leads to more recommendations, not fewer. AI models are trying to provide helpful, accurate answers. They're more likely to recommend a brand that's clearly positioned for a specific use case than one that claims to be everything for everyone.
Consider: "We're the project management tool built specifically for creative agencies managing client work" vs. "We're a project management tool for businesses." The former will dominate prompts from creative agencies. The latter will get lost in a sea of alternatives.
Building a Prompt Strategy
A comprehensive prompt strategy involves:
- Prompt research – Systematically testing how AI models respond to category-relevant prompts
- Gap analysis – Identifying which prompt patterns don't surface your brand
- Positioning optimization – Creating content and positioning that addresses those gaps
- Continuous monitoring – Tracking how your presence in AI recommendations evolves over time
The Future Is Conversational
As AI interfaces become more sophisticated, users will engage in increasingly nuanced conversations about their needs. The brands that understand these conversation patterns—and position themselves to be the natural answer—will dominate the next era of brand discovery.
Your prompt strategy isn't just about being found. It's about being understood, recommended, and chosen in the moments that matter most.