GAL - Generative AI Leader Study Guide 2026

Key Competencies (5)

• 1

  Exploring Large Language Models (LLMs)

  Understand the structure and function of LLMs in generative AI.

• 2

  Understanding Core Generative AI Concepts

  Grasp the fundamental principles and terminology of generative AI.

• 3

  Model Capabilities and Limitations

  Assess the strengths and weaknesses of generative AI models.

• 4

  Foundation Models and Their Applications

  Examine the role of foundation models in generative AI solutions.

• 5

  Prompt Engineering Techniques

  Learn methods to effectively interact with generative AI models.

Key Competencies (5)

• 1

  Utilizing Vertex AI for Generative AI

  Explore how Vertex AI facilitates the creation and deployment of generative models.

• 2

  Understanding Model Garden and Generative AI Studio

  Learn about tools for accessing and customizing generative models.

• 3

  Exploring Prebuilt Generative AI Applications

  Examine Google's ready-to-use generative AI applications.

• 4

  Overview of Google Cloud's AI Platform

  Understand the components and capabilities of Google Cloud's AI offerings.

• 5

  Integrating Generative AI with Google Cloud Services

  Combining generative AI models with other Google Cloud services.

Key Competencies (5)

• 1

  Applying Grounding Techniques

  Enhance model responses by incorporating external information.

• 2

  Fine-Tuning Models for Specific Tasks

  Adapt generative AI models to meet particular business needs.

• 3

  Evaluating Model Performance

  Assess and monitor the effectiveness of generative AI models.

• 4

  Addressing Model Limitations

  Identify and mitigate common issues in generative AI outputs.

• 5

  Implementing Effective Prompt Engineering

  Utilize advanced prompting techniques to guide model behavior.

Key Competencies (5)

• 1

  Measuring AI Impact and ROI

  Evaluate the effectiveness and value of AI initiatives.

• 2

  Ensuring Responsible AI Practices

  Implement ethical guidelines and frameworks in AI deployment.

• 3

  Managing AI Governance and Compliance

  Establish policies and procedures for AI oversight.

• 4

  Driving Organizational Change with AI

  Lead and manage the transformation associated with AI adoption.

• 5

  Developing an AI Adoption Strategy

  Plan and execute the integration of AI into business processes.

Key Competencies (3)

• 1

  Understand Generative AI Concepts

  Explain core principles and components of generative AI.

• 2

  Terminology and Key Principles

  Familiarity with the fundamental terms and principles of generative AI.

• 3

  Generative AI Model Types

  Recognize different generative AI model types and their uses.

Key Competencies (3)

• 1

  Google Cloud AI Product Portfolio

  Identify and describe Google Cloud's AI products and services.

• 2

  Real-World Use Cases

  Apply Google Cloud tools in generative AI applications.

• 3

  Integration and Deployment

  Understand deployment and integration strategies for generative AI solutions on Google Cloud.

Key Competencies (3)

• 1

  Model Fine-Tuning

  Implement strategies for optimizing generative AI model performance.

• 2

  Optimization Strategies

  Apply strategies to optimize model efficiency and effectiveness.

• 3

  Prompt Engineering

  Understand the role of prompting in enhancing model outputs.

Key Competencies (3)

• 1

  AI Governance and Ethics

  Ensure responsible generative AI usage within organizational frameworks.

• 2

  Strategic Implementation

  Develop and execute strategies for deploying generative AI solutions.

• 3

  Risk Management

  Identify and mitigate risks associated with generative AI solutions.