Description

Build trustworthy Generative AI solutions by applying responsible AI principles across the full lifecycle. This GenAI course covers the foundations of Responsible AI, the unique risks of generative models, and practical approaches to evaluation, development, monitoring, and governance. After taking this training, learners will confidently manage quality, safety, fairness, privacy, and regulatory considerations to deliver AI systems that are both effective and responsible.

By the end of this course, participants will be able to:

Describe the purpose of Responsible AI and the five pillars (safety/security, robustness, fairness, explainability, privacy/governance).
Cassify generative AI issues into the six GenAI risk categories (including hallucinations, prompt injection, leakage, bias, extraction).
Evaluate GenAI output quality using core metrics and structured test cases.
Measure harm prevention, privacy/security, and bias using safety, privacy, and fairness metrics.
Implement responsible GenAI development practices across data governance, preprocessing, model choice/training, and prompt engineering.
Monitor GenAI systems in production by tracking key metrics, using a three-tier framework, and setting alert thresholds (e.g., in LangSmith).
Translate regulatory and organizational expectations into product requirements, AI policies, and ethical culture practices.

Course Overview

Building Responsible AI: Principles and Practices

Introduction to Responsible AI
The Foundation: Why Responsible AI Matters
Pillar 1: Safety and Security
Pillar 2: Robustness and Reliability
Pillar 3: Fairness
Pillar 4: Explainability and Interpretability
Pillar 5: Privacy and Data Governance

Unique and Increased Risks of Generative AI

Why Gen AI Risks Are Different
The Six Risk Categories
Risk 1: Hallucinations & Content Quality
Risk 2: Prompt Injection & Input Attacks
Risk 3: Privacy & Data Leakage
Risk 4: Bias & Fairness
Risk 5: Security & Model Extraction

Model Evaluation and Validation for Generative AI

The Quality Imperative for Generative AI
What Makes Generative AI Evaluation Different
The Evaluation Framework
Core Metrics: Output Quality
Safety Metrics: Preventing Harm
Privacy and Security Metrics
Fairness and Bias Metrics

Developing Generative AI Applications Responsibly

The Foundation of Responsible AI Development
Introduction to Responsible Development
Data Governance: Quality Control for AI
Data Preprocessing: Engineering Fairness
Model Selection: Choosing Responsible Architectures
Model Training: Building Reliable Models
Prompt Engineering: Guiding Reliable Outputs

Basics of Generative AI Monitoring

Introduction: Why Monitoring Matters
Evaluation vs. Monitoring
Introduction to LangSmith
Key Monitoring Metrics
Three-Tier Monitoring Framework
Monitoring Workflow: Step-by-Step
Setting Alert Thresholds

Legal and Organizational Implications of Generative AI

Introduction: Why Legal & Organizational Readiness Matters
Global Regulatory Landscape: Common Principles
Regulatory Principles Drive Product Requirements
Privacy-Preserving Techniques for AI
Case Study: Legal Failure—AI Art Copyright Disputes
Developing Responsible AI Policies
Building an Ethical AI Culture

Practicing Responsible Generative AI Virtual Classroom Live July 30, 2026

Price: $1,900

Enroll today to reserve your spot!

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