How SCRUBgpt Tackles Prompt Engineering and LLM Hallucination in a Vertical AI Solution

Why Vertical AI Matters Right Now

The future of AI isn’t about building massive, do-it-all models that try to solve every problem for every industry. It’s about going deep — understanding the nuances of a specific field and building tools that deliver real operational impact.

That’s where vertical AI solutions come in. And at The CoBuilders, we’re fully invested in this future with SCRUBgpt, an AI assistant built from the ground up for the cleaning industry.

Unlike general-purpose chatbots, SCRUBgpt is designed to handle practical, day-to-day workflows — from drafting branded social posts for a cleaning company, to helping managers pick the right eco-friendly supply bundles for specific types of facilities.

But going this deep comes with real challenges, especially around prompt engineering and controlling LLM hallucination (where language models start inventing details that simply aren’t true).

Getting Control Over LLM Hallucination

For us, building trust into SCRUBgpt is non-negotiable. When you’re supporting cleaning businesses that rely on precise inventory data, or drafting marketing copy that represents their brand voice, you can’t afford creative drift or inaccurate statements.

That’s why one of our first priorities was putting strong guardrails in place to reduce hallucination.

A key tactic? Adjusting the temperature settings of our language models. Lower temperatures mean the AI is less likely to spin off into overly creative or speculative responses. Instead, it focuses on the instructions and context it’s given — delivering more grounded, fact-based outputs.

Whether SCRUBgpt is recommending equipment packages to a commercial contractor, or advising on OSHA-compliant cleaning procedures for a government cleaning project, this kind of reliability is critical.

The Costs and Benefits of Rich Prompt Engineering

Another big lesson? Prompt engineering isn’t optional — it’s essential.

In the AI community, there’s a temptation to strip down prompts to the shortest version possible. It can be cheaper and sometimes faster. But in a vertical solution like SCRUBgpt, we’ve learned that rich, detailed instructions are necessary to produce quality, on-brand, and context-aware outputs.

That doesn’t mean we overload every query. Instead, we break down prompts by specific use case, so SCRUBgpt only pulls in the most relevant details needed to complete each task. This way, we maintain both high-quality outputs and operational efficiency.

For example:

• A cleaner running a small residential team might ask SCRUBgpt for a local marketing plan for house cleaning services.
• A strategic partner could use it to generate proposals tailored for property managers.
• A facilities manager working on a large government contract might need a checklist aligned to federal sustainability standards.

In each case, the prompts differ — but they’re always thorough enough to get precise, high-value results.

Why It All Matters

A general consumer chatbot can afford to be playful or slightly inaccurate — it’s not running someone’s business.

But SCRUBgpt is different. It’s built to support cleaning companies and service professionals managing inventory, marketing spend, and brand reputation. Even small inaccuracies could mean the wrong chemical order, or a social post that doesn’t fit a brand’s promise.

By prioritizing smart prompt engineering and actively reducing hallucination, we’re ensuring SCRUBgpt is a trusted, high-ROI tool for cleaning businesses of all sizes — from solo cleaners and franchisees to government facility contractors.

Final Thoughts

The most exciting impact of AI won’t come from tools that try to do everything for everyone. It’ll come from solutions that deeply understand individual industries — solving the most important problems with tailored, reliable intelligence.

That’s exactly what we’re building with SCRUBgpt at The CoBuilders.

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