From Prompt to Process: Making AI Reliable for Your Business

A useful AI result can save a few minutes. A reliable process can keep saving time across Projects and team members.

In this webinar replay, Curate Head of Marketing Eugene Farber explains how to move from one-off prompts to reusable AI Skills. He also builds a Skill live in Curate AI Assist, tests it on a second Project, and finds one more instruction that needs to be made explicit.

Watch the webinar replay

The session is designed for caterers, florists, venues, and other event professionals who want AI to handle more repetitive preparation while people remain responsible for judgment, customer communication, exceptions, and approval.

Computers and people do different work well

Curate’s perspective on AI starts with the work itself. Computers are useful for searching, organizing, drafting, and repeating. People are still responsible for understanding the situation, earning trust, handling exceptions, and making consequential decisions.

That distinction matters in event work. A system can prepare context, but a person still needs to respond when a delivery changes, a team member is unavailable, or a customer needs confidence in a new plan.

The goal is to give computers more of the repetitive work so people have more time for the work that requires judgment and accountability.

A prompt asks once. A Skill captures the process.

Eugene uses the familiar story of a repair professional who charges for knowing exactly where to make one small adjustment. The physical action is simple. The valuable part is the judgment built through training, practice, and correction.

AI Skills work in a similar way. The first prompt points toward an outcome. Testing and revision reveal the source information, boundaries, definitions, formatting, and approval rules that make the process dependable enough to use again.

Building a personalized proposal introduction

For the live demonstration, Eugene asks Curate AI Assist to write a personalized proposal introduction using information available in a Project.

The first result is fluent, but it is too long and includes familiar signs of generic AI writing. Eugene refines the instructions so the introduction:

• addresses the Project Customer by first name
• stays within a useful length
• reflects meaningful Project details
• follows a distinctive customer-facing voice
• returns only the copy intended for the proposal

To make the effect of voice instructions easy to see, the final version becomes a narrative poem inspired by Edgar Allan Poe. The exaggerated format is playful, but the process behind it is practical: keep the source and business rules stable while changing how the result is expressed.

Saving the decisions as a reusable Skill

Once the result is in the right place, Eugene asks AI Assist to consolidate the conversation into a reusable Skill. The Skill is saved to the account so it can be called from another Project without repeating the entire refinement conversation.

The next test reveals why reuse matters. On a different Project, the Skill produces a placeholder instead of the current customer’s first name. That does not mean the process is finished. It identifies the next missing instruction: the Skill must explicitly read the Project Customer instead of returning a generic placeholder.

The Skill can then be updated for future use.

The first result is the test

Building a useful Skill can take longer than completing the task manually once. The return comes from repetition. Each correction that becomes part of the Skill reduces the need to reconstruct the same judgment from scratch on the next Project.

Start with a task your team performs regularly and already knows how to review. Make the source information explicit. Define what the output should include, what it should omit, and what should make it stop. Test it under different conditions before treating it as a reliable part of the workflow.

AI can prepare the work. A person still reviews the result and remains accountable for what reaches the customer.