Grounding, not training
In almost every practical deployment you are not training a model — you are supplying it with context at the moment it answers. The distinction matters because it means quality is a function of curation, and curation is a thing your team can do this week without a data science hire.
Start with resolved conversations
Your best material is work your team has already done well: resolved tickets, answered emails, closed deals. It contains the questions people really ask, in their words, with answers your business stood behind. Nothing you write from scratch will match it for realism.
Filter for outcome before you index. Feeding it every conversation teaches it your average; feeding it the good ones teaches it your standard.
Then the decision rules nobody wrote down
When do you offer a refund? What size deal needs approval? Which requests always go to a specialist? These live in people’s heads and are the difference between an assistant that sounds right and one that acts right. Extracting them is an interview exercise, not a data migration.
The most valuable input isn’t your documentation. It’s the rules your team follows without noticing.
Voice comes from examples, not adjectives
Telling a model to be “warm, confident, and concise” produces a generic imitation of those words. Giving it eight real messages you were happy to send produces your voice. Adjectives describe a style; examples transmit one.
Explicitly include what not to say
Claims you can’t support, competitors you don’t discuss, discounts nobody may offer, topics that always escalate. Prohibitions are as much a part of the job description for an AI teammate as for a human one, and they are easier to enforce when stated as rules rather than hoped for as tone.
Keep it fed
Every correction a human makes is fresh training material, and a system that captures those improves weekly while one that doesn’t degrades as your business changes. The maintenance loop is the product; the initial corpus is just the starting position.
Assign an owner. An AI employee with no one responsible for its knowledge decays exactly like documentation with no maintainer.