An AI customer support macro builder gives a support team a shared library of prompts that draft consistent, on-brand replies for every common ticket type. Traditional canned responses are rigid and feel robotic; asking each agent to free-prompt an LLM produces wildly different voices. This tool sits in between: it generates one structured AI prompt per ticket category, all sharing your brand voice and reply structure, so any agent gets a tailored draft that still sounds like your company.
How it works
You enter your product type and brand name for context, choose a tone (formal or casual), and list your common ticket categories, one per line. For each category the builder writes a macro: a complete prompt that tells the model who it is replying as, the tone to use, the structure of a good reply (acknowledge, address, next step), and a firm rule to insert bracketed placeholders for any account-specific fact rather than invent it. The output is a ready-to-use macro library your team can drop into a help-desk tool or paste into an LLM alongside the customer’s message. Everything is generated locally in your browser.
Why macros beat both canned responses and free prompting
The problem with canned responses is that they are too rigid. A billing canned response that fits 60% of billing questions fits 40% poorly, and agents either send the wrong response or spend time rewriting it anyway. Customers can also tell when they receive something pre-written with no reference to their actual situation.
The problem with free prompting is inconsistency. Give ten agents an LLM and ask them to handle a billing dispute and you get ten different tones, structures, and sometimes ten different policies described. Macro prompts solve this by encoding your brand voice, the reply structure, and the rules (always acknowledge the customer’s frustration; always state a next step; always use a placeholder for any account-specific fact you cannot verify) into the prompt itself. The agent still exercises judgement when reviewing the draft — they just start from a consistent, on-brand scaffold instead of a blank page.
How to define good ticket categories
The quality of a macro depends entirely on how specific the category is. For this builder, the categories are the ticket types you list — so the specificity of the list determines the quality of the output.
Too broad:
- “Billing” — one macro for every billing question regardless of whether it is a refund, an incorrect charge, a subscription change, or an invoice query.
Better:
- “Refund request” — customer wants money back, outcome is to assess eligibility and state the process
- “Incorrect charge” — customer believes a charge is wrong, outcome is to investigate and confirm or correct
- “Invoice or receipt request” — customer needs documentation, outcome is to provide or explain how to access
Each distinct category produces a macro that opens with the right acknowledgement, addresses the specific concern, and closes with the specific next step. A “billing” catch-all macro does none of these well.
Getting the most from the macro library
- Start with five to eight categories. These typically cover 80% or more of ticket volume, and you can always add more.
- Use the macros alongside the customer’s message, not instead of it. The agent pastes the macro prompt plus the customer’s original message into the LLM — the model then drafts a reply that responds to what the customer actually said, not a generic scenario.
- Review and fill placeholders before sending. The macros include explicit instructions to the model to insert bracketed placeholders for any account- specific details it cannot know. Agents must fill these in from the customer’s actual account before sending.
- Measure handle time and CSAT. The test of a good macro is that agents send the draft faster and customers rate responses better. Run both metrics before and after deploying the library.
Tips and examples
Begin with your highest-volume categories — billing questions, bug reports, refund requests, and how-to questions usually account for most tickets, and macros there save the most time. Keep categories distinct so each macro can be specific; a single “billing” macro is weaker than separate “refund” and “invoice question” macros. Set the tone deliberately: casual suits consumer apps, formal suits enterprise and regulated products. Always keep an agent in the loop to fill placeholders and approve the draft before sending — these macros accelerate the first draft, they do not replace human judgement on edge cases or upset customers.