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Deploying an AI assistant on your own data
AI prompt patterns that hold up in production
Evaluating AI output before it shipsContinue learning
Deploying an AI assistant on your own data
From the first workflow to a live assistant your team trusts, with the sources connected and the answers grounded.
AI prompt patterns that hold up in production
The handful of patterns that survive contact with real users, and the clever ones that quietly stop working.
Evaluating AI output before it ships
Build a small evaluation set from real questions, then use it to decide whether a change is an improvement.
Data handling and privacy in AI projects
What leaves your systems, what is retained, and how to answer the questions your security team will ask.
Rolling out AI to a support team
The rollout plan that works: one queue, one week, one owner, and the review that follows it.
Measuring what an AI rollout changed
Pick the before numbers first. Four measures that hold up in a board review and three that never do.
Ground the answers in your own documents
An assistant that answers from the open web is a demo. An assistant that answers from your own documents is a colleague, and the difference is entirely in what you connected before anyone typed a question.