Gen AI Dev- Implement Evaluation Systems for Generative AI

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Gen AI Dev- Implement Evaluation Systems for Generative AI

This course is part of AWS Generative AI Developer Advanced Specialization

Instructor: AWS Instructor

Included with Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

What you'll learn

Assess generative AI outputs using metrics for relevance, factual accuracy, consistency, and fluency
Design quality assurance processes integrating continuous evaluation workflows and regression testing
Apply testing methods including A/B testing and multi-model comparison for output quality

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This course is part of the AWS Generative AI Developer Advanced Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 5 modules in this course

Welcome to the fifth part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In this part, you will learn critical skills for ensuring reliable generative AI outputs. You'll learn to build evaluation frameworks measuring relevance and accuracy, implement testing methodologies like A/B testing and multi-model comparison, and develop user-centered quality assurance processes. The curriculum covers troubleshooting techniques for AI-specific

What's included

6 readings

What's included

16 readings

16 readingsTotal 17 minutes

Introduction1 minute
Key metrics for generative AI evaluation1 minute
Developing a framework based on a business use case1 minute
Introduction1 minute
Assessment systems for generative AI1 minute
RAG evaluation components1 minute
Implementing RAG evaluation1 minute
LLM-as-a-Judge implementation1 minute
Implementation best practices1 minute
Introduction1 minute
Amazon Bedrock model evaluations1 minute
A/B testing strategies1 minute
Multi-model evaluation1 minute
Amazon Nova model family evaluation2 minutes
Cost-performance analysis1 minute
Implementation best practices1 minute

What's included

15 readings

15 readingsTotal 15 minutes

Introduction1 minute
Quality assurance for generative AI1 minute
Continuous evaluation workflows1 minute
Regression testing for model outputs1 minute
Automated quality gates for deployment1 minute
AI-specific output validation1 minute
Agent-specific quality validation1 minute
Implementation best practices1 minute
Introduction1 minute
Retrieval quality fundamentals1 minute
Relevance scoring techniques1 minute
Context matching verification1 minute
Measuring and optimizing retrieval latency1 minute
Monitoring and continuous improvement for retrieval systems1 minute
Use-case specific optimization strategies1 minute

What's included

15 readings

15 readingsTotal 18 minutes

Introduction1 minute
Agent performance fundamentals1 minute
Measuring task completion rates1 minute
Multi-step workflow assessment for agent performance1 minute
Tool usage effectiveness evaluation1 minute
Strands Agents evaluation framework1 minute
Amazon Bedrock AgentCore evaluations3 minutes
Best practices for agent performance monitoring1 minute
Use case-specific performance frameworks1 minute
Introduction1 minute
Reporting systems fundamentals1 minute
Visualization tools and dashboard development1 minute
Automated reporting mechanisms1 minute
Model comparison visualizations2 minutes
Stakeholder-specific reporting frameworks1 minute

What's included

4 readings1 assignment

Earn a career certificate

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Instructor

AWS Instructor

Amazon Web Services

187 Courses366,231 learners

Offered by

Amazon Web Services

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