Module 3 of 6Lesson 3 of 3~8 min
Specify the response format for the interface
As long as the assistant answers in free text, the interface can't show reliable sources, trigger a handoff or measure much at all. This lesson teaches you to specify a structured response, one that the interface and your indicators can finally rely on.
Lesson objective
By the end of this lesson, you will be able to specify the fields of a structured response (answer, sources, handoff needed, suggested actions) so that the interface and the metrics can rely on it.
Topics covered
- structured outputs
- JSON schema
- AI assistant
- conversational interface
- product specification
In the glossary
Where it fits
Design the experience and behavior
How should the assistant present itself, answer, say "I don't know" and hand over?
Lessons in this module
- Design the conversational experience
- Write a production system prompt
- Specify the response format for the interface (this lesson)
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