Quiz Question 3

When using output_schema, what must you include in your instruction?

Это упражнение является частью курса

Google: Optimize Agent Behavior

Посмотреть курс

Практическое интерактивное упражнение

Превратите теорию в практику с помощью одного из наших интерактивных упражнений

Начать упражнение

Это упражнение является частью курса

Google: Optimize Agent Behavior

SkillTag.level.intermediateSkillTag.label

4.9+

21 reviews

Начать курс бесплатно

This module transforms basic instructions into professional agent guidance systems. You’ll learn structured patterns for defining agent persona, role, boundaries, and communication style—turning simple prompts into sophisticated behavioral frameworks grounded in ADK best practices.

Exercise 1: The problem: Vague instructions create unpredictable behavior Exercise 2: The solution: Structured instructions Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2

This module transforms unpredictable text responses into reliable, structured JSON output that systems can parse and use. You’ll learn how to use Pydantic schemas to enforce output format, making your agents production-ready for integration with applications, databases, and workflows.

Exercise 1: The problem: Unpredictable output breaks integrations Exercise 2: The solution: Structured output with pydantic Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2 Exercise 5: Quiz Question 3

Текущее упражнение

This module empowers you to strategically select models and configure generation parameters for optimal performance. You’ll learn when to use Flash versus Pro models, how to set safety thresholds, and how to fine-tune temperature and token limits—transforming basic agents into production-optimized systems.

Exercise 1: The problem: One size doesn’t fit all Exercise 2: The solution: Strategic model configuration Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2 Exercise 5: Quiz Question 3

This module adds multi-step reasoning to your agents, transforming them from reactive responders into thoughtful problem-solvers. You’ll learn how to enable Gemini’s built-in thinking capabilities using BuiltInPlanner, allowing agents to plan their approach before executing—essential for complex, multi-step tasks.

Exercise 1: The problem: Complex tasks need structured thinking Exercise 2: The solution: Planning with BuiltInPlanner Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2

Reading List

Exercise 1: Reading List