Jeremy Utley from Stanford highlights a common frustration with AI: when you give it a quick, vague prompt, the response often sounds like AI. The wording can feel generic or robotic because the system doesn’t know enough about your business. His solution is context engineering—feeding AI the background details it needs so it can act more like a trained employee rather than a chatbot.

For a pest control company, context can include your brand voice, pricing structure, service checklists, and even typical customer questions. Instead of just saying “write a service-estimate email,” you might add details like your preferred greeting, the usual treatment plan for termites, and your standard follow-up timeline. The more relevant information you share, the more natural and accurate the AI’s reply becomes.

Context engineering also cuts down on rework. When the AI knows your territory’s regulations, technician schedules, and seasonal pest patterns, it can create quotes or inspection notes that are ready to send, not just a starting draft. This saves time for office staff and reduces the back-and-forth editing that makes AI feel like an untrained intern.

One powerful technique is reverse prompting. Instead of you trying to guess every piece of information upfront, you ask the AI to start by asking you questions. For example, you could say, “Before writing a proposal, ask me what details you need.” The AI might then ask about the type of pest, square footage, or customer preferences. This back-and-forth builds a stronger foundation and ensures the final output matches your exact needs.

Another strategy is assigning the AI a perspective or role. For instance, you might tell it, “You are our operations manager creating a weekly technician schedule,” or “You are our customer service lead writing a follow-up email to a homeowner with a recurring ant issue.” Giving the AI a clear identity helps it focus its language and recommendations, making the results feel more like something your own staff would write.

You can also provide examples to guide its tone—such as past service reports, sample invoices, or marketing emails. Pairing these samples with a defined role tells the AI not just what to write but how to write it. This combination of reverse prompting and perspective keeps outputs on-brand and professional, eliminating that “sounds like AI” problem.

With these practices, AI becomes a true productivity booster for pest control companies. Reports, proposals, follow-up emails, and even technician routing plans can be generated faster and with fewer mistakes. By giving AI the right context—and letting it clarify details before it starts—you move beyond generic results and gain a virtual team member that speaks your company’s language.