Module 6 of 6Lesson 3 of 4~12 min
Launch and run in production
An assistant isn't launched to everyone at once, and message volume says nothing about its real quality. This lesson teaches you to prepare a staged launch, build a dashboard with alert thresholds and turn what users tell you into new test cases for the next release.
Lesson objective
By the end of this lesson, you will be able to define a staged launch plan and a dashboard (product, quality, cost and latency metrics), with alert thresholds and a loop that turns user feedback into evaluation cases.
Topics covered
- AI monitoring
- staged rollout
- product dashboard
- LLMs in production
- AI assistant
Where it fits
Evaluate, ship, monitor
How do you know the assistant is ready, launch it safely and improve it?
Lessons in this module
- Design the evaluation plan
- Choose how to build it
- Launch and run in production (this lesson)
- Assemble the design dossier
What you will learn in the course
This lesson is part of the course Build an AI assistant for your product
- Identify a use case that justifies an AI assistant and write its framing brief (problem, users, allowed actions, out of scope, success criteria).
- Design the conversational experience: entry point, tone, handling uncertainty, handoff to a human and response format.
- Choose and justify a knowledge strategy (instructions, injected context, RAG, fine-tuning) and a conversation memory strategy.
- Specify the assistant's tools and actions (data read, actions written, confirmation, permissions) and choose how to build it.
- Identify the risks (injection, leaks, excessive actions, costs) and design layered guardrails that go beyond the prompt.
- Design an evaluation plan with a reference dataset, criteria, grading methods, release thresholds and a regression rule.
- Define production metrics (product, quality, cost, latency), alert thresholds and the loop from user feedback to the evaluation set.
Related courses
- Design a RAG architecture that fits your productAdvanced · ~3 hr 30 min
- Evaluate an AI feature: test sets, metrics and LLM judgesAdvanced · ~3 hr
- Ship and monitor an AI feature in productionExpert · ~3 hr