Prompting for Precision: How to Define Categories for Reliable AI Sorting in Sheets

While understanding the academic foundations of text classification is valuable, putting that theory into practice reveals a common and frustrating challenge. You set up a workflow to sort incoming emails in Google Sheets, you write what feels like a clear prompt, and the AI starts working. But instead of clean, uniform data, you get chaos. One email is labeled "Sales Inquiry," the next is "Lead," and another is simply "Sales." You didn't ask for this, but the AI, in its effort to be helpful, has invented categories on the fly. This isn't just messy; it breaks any automation, chart, or filter you try to build.

This is the critical gap between a clever AI demo and a reliable business process. To get consistent, machine-readable output, you must shift your mindset from asking the AI for a favor to giving it strict, unambiguous instructions. The goal isn't to have a conversation; it's to force the AI to behave like a predictable data entry tool. This section will teach you how to define your categories and structure your prompts to achieve that precision, ensuring your Google Sheets columns are filled with the exact labels you expect, every single time.

The secret lies in a simple but powerful principle used in consulting and management: MECE (pronounced "mee-see"). It stands for Mutually Exclusive, Collectively Exhaustive. This framework is your best defense against AI creativity.

Mutually Exclusive means that each of your categories is distinct and an item can only belong to one category. An email cannot be both a "Billing Inquiry" and a "Technical Issue" simultaneously. If you find overlap, your definitions need refinement.

Collectively Exhaustive means that your set of categories covers all possible scenarios you want to track. If an email arrives that doesn't fit any of your defined labels, where does it go? A good system includes a fallback category like "General Inquiry" or "Uncategorized" to catch everything else, ensuring no data is left behind.

Imagine you're managing a shared inbox for a small software company. Initially, you might tell the AI to sort emails into vague categories like "Support," "Sales," and "Feedback." The AI will interpret these loosely, leading to the inconsistencies we mentioned. Now, let's apply the MECE principle. We can transform those vague labels into a precise, reliable system:

• Technical Issue: For bug reports or problems using the software.
• Billing Inquiry: For questions about invoices, payments, or subscription changes.
• Feature Request: For user suggestions on improving the product.
• Sales Lead (New): For inquiries from potential new customers.
• Partnership Opportunity: For collaboration requests from other businesses.
• Uncategorized: A fallback for anything that doesn't fit the above.

Notice how an email can't be both a "Technical Issue" and a "Billing Inquiry." The categories are mutually exclusive. And with the "Uncategorized" option, they are collectively exhaustive. This is the clear, logical map the AI needs to follow. Now, we just need to translate this map into a prompt.

A prompt designed for precision has four key components: a clear instruction, the defined categories with brief descriptions, a strict constraint, and a defined fallback. Think of it as building a cage for the AI's creativity, forcing it to choose only from the options you provide.

Let's break down why this prompt structure works so well. The opening line, "Categorize... into ONE of the following," sets a clear task. Providing the list with short descriptions acts as a reference guide, preventing the AI from guessing your intent. The crucial constraint, "You MUST choose only one category from this list," explicitly forbids the AI from inventing new labels. Finally, including "Uncategorized" gives the model a safe and correct option when faced with ambiguity, preventing it from making a poor-fit choice.

By mastering this technique—defining MECE categories and embedding them in a constrained prompt—you transform your AI from an unpredictable assistant into a reliable data processing engine. This is a foundational skill that moves you from simple summarization to building robust, automated workflows.

But what if you need more than just a category? Often, you'll want to pull out specific pieces of information, like a customer's name, company, or order number, and place them into separate columns in your spreadsheet. How do we modify our prompt to extract this structured data alongside the category? We'll dive into that powerful technique in the very next section.

References

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2. Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903.
3. OpenAI. (2023). GPT Best Practices. Retrieved from platform.openai.com/docs/guides/gpt-best-practices.
4. Minto, B. (2009). The Pyramid Principle: Logic in Writing and Thinking. Pearson Education.
5. Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. arXiv:2005.14165.