These are my personal notes for the Google Cloud Generative AI Leader Certification, taken during following the Cloud Skills Boost Generative AI Leader path.

Overview:

  • Fundamentals of Generative AI (~30%): Understanding basic concepts and definitions related to AI and ML.
  • Google Cloud’s Generative AI Offerings (~35%): Familiarity with Google Cloud tools and services that support generative AI.
  • Techniques to Improve Model Output (~20%): Knowledge of methods to enhance the performance of generative AI models.
  • Business Strategies for Successful Gen AI Solutions (~15%): Strategies for implementing generative AI in business settings.

Helpful resources:

1. Data and Machine Learning Fundamentals

Data as the Foundation of AI

Data is the foundation of any AI system. Data quality and accessibility are essential for effective AI development.
Data can be structured or unstructured, each requiring different analysis techniques.

Key dimensions of data quality:

  • Accuracy

  • Completeness

  • Consistency

  • Relevance

  • Availability

  • Cost

  • Format

Understanding the types and quality of your data is crucial for successful AI initiatives.

Machine Learning Approaches

Machine learning models can be trained using:

  • Supervised learning

  • Unsupervised learning

  • Reinforcement learning

The choice of approach depends on the specific task and the nature of the data available.

The ML Lifecycle

The ML lifecycle encompasses several key stages:

  • Data ingestion and preparation

  • Model training

  • Model deployment

  • Model management

Google Cloud provides a comprehensive suite of tools to support each stage of this lifecycle.

Vertex AI helps with model training and deployment, while various data tools support ingestion, preparation, and management.

By understanding and effectively managing this lifecycle, organizations can maximize the value of their initiatives and ensure long-term success.

2. Model Development with Vertex AI

Model Training

The process of creating your ML model using data is called model training.

Vertex AI provides:

  • A managed environment for training ML models

  • Prebuilt containers for popular frameworks

  • Custom training jobs

  • Tools for model evaluation

  • Powerful computing resources to speed up training

Model Deployment

Model deployment is the process of making a trained model available for use.

Vertex AI simplifies this with:

  • Tools to deploy models for generating predictions

  • Options to scale deployments by adjusting resources based on demand

Model Management

Managing and maintaining your models over time is critical.

Google Cloud offers:

  • Versioning: Track different model versions

  • Performance Tracking: Monitor model metrics

  • Drift Monitoring: Watch for accuracy changes over time

  • Data Management: Use Vertex AI Feature Store to manage data features

  • Storage: Vertex AI Model Garden to organize models

  • Automation: Vertex AI Pipelines to automate ML tasks

3. Foundation Models and Generative AI

Deep learning provides the core technology.
Foundation models are powerful architectures built on deep learning.
Generative AI is the application of these models to create new, original content.

Vertex AI for Generative AI

Vertex AI streamlines integration of advanced AI capabilities into business applications:

  • Seamless discovery, deployment, and customization

  • Access to many models without extensive in-house development

These models empower businesses to enhance customer experiences, increase productivity, foster innovation, and improve decision-making.

Google-Developed Models on Vertex AI

  • Gemini: Multimodal; processes text, images, audio, and video.

  • Gemma: Lightweight, open models for local deployments and specialized AI applications.

  • Imagen: Text-to-image generation.

  • Veo: Video generation.

Gemini is designed to handle multiple data types, while Gemma is optimized for lighter, specialized deployments.

Considerations for Choosing Generative AI Models

  • Modality

  • Context window

  • Security

  • Availability

  • Cost

  • Performance

  • Fine-tuning

  • Ease of integration

Google Cloud offers a suite of foundation models with unique strengths and capabilities.

4. Limitations of Foundation Models

Common Limitations

  • Data Dependency

    Performance depends on large, high-quality datasets. Biases or incompleteness in the data will seep into outputs.
    Example: It’s like asking a student to write an essay on a book they haven’t read.

  • Knowledge Cutoff

    AI models are only aware of information up to their training date.
    Example: A model trained in 2022 won’t know about events after 2022.

  • Bias

    LLMs can amplify biases present in their training data.
    Even subtle biases can be magnified in outputs.

  • Fairness

    Defining fairness is complex.
    Fairness assessments can miss some forms of bias.

  • Hallucinations

    Models may produce plausible-sounding but incorrect or nonsensical answers.
    This is a major concern in accuracy-critical applications.

  • Edge Cases

    Rare or unusual scenarios can reveal model weaknesses and lead to errors.

5. Techniques to Overcome Limitations

Grounding

Connect the AI’s output to verifiable sources—like giving AI a reality check.

Benefits:

  • Reduces hallucinations

  • Anchors responses in real data

  • Builds trust with citations and confidence scores

Retrieval-Augmented Generation (RAG)

  • Retrieval: Search engine finds relevant information using semantic understanding.

  • Augmentation: Retrieved data is added to the prompt.

  • Generation: The model uses this context to produce informed, accurate responses.

RAG grounds outputs in real, verifiable sources, improving accuracy and relevance.

Prompt Engineering

The most rapid, straightforward approach to guide models.

  • Involves crafting precise prompts

  • Limited by the model’s existing knowledge

Fine-Tuning

When prompting isn’t enough, fine-tuning adapts a model to specific needs.

  • Further trains a pre-trained model on task-specific data

  • Adjusts parameters for specialized performance

Use Cases:

  • Generating content in a specific style

  • Code generation in specific languages

  • Domain-specific translation

Vertex AI provides tooling to facilitate tuning.

6. Humans in the Loop (HITL)

Even the best models benefit from human oversight.

Key use cases:

  • Content Moderation: Ensures accurate, appropriate filtering of user-generated content.

  • Sensitive Applications: Provides oversight in healthcare, finance, etc.

  • High-Risk Decisions: Adds accountability for decisions with serious consequences.

  • Pre-Generation Review: Validates outputs before deployment.

  • Post-Generation Review: Continuous human feedback to improve models over time.

7. Secure AI

Preventing intentional harm to AI applications.

  • Protect AI systems from malicious attacks and misuse.

  • Ensure security throughout the entire lifecycle, from development through deployment.

Key risks:

  • Data poisoning

  • Model theft

  • Prompt injection

Google Cloud’s SAIF framework provides tools to help build and maintain secure AI systems.

8. Responsible AI

Ensuring AI avoids both intentional and unintentional harm.

Transparency

Users need to know how their information is used and how AI systems work.

  • Includes data handling, decision-making processes, and potential biases.

Privacy

Protecting privacy often involves anonymization or pseudonymization.

  • Prevents models from leaking sensitive information in their outputs.

Data Quality, Bias, and Fairness

High-quality data is essential for ethical AI.

  • Poor data quality can lead to biased, unfair outcomes.

  • AI systems can amplify societal biases.

Example: A resume-screening tool favoring certain demographics due to biased training data.

Accountability and Explainability

Fairness requires accountability.

  • Know who is responsible for AI outputs.

  • Make AI decision-making transparent and understandable.

Vertex Explainable AI helps:

  • Debug errors

  • Uncover hidden biases

  • Build user trust

AI development is governed by evolving legal frameworks.

Key considerations:

  • Data privacy

  • Non-discrimination

  • Intellectual property

  • Product liability

Legal compliance is essential for building trustworthy AI systems.

9. Agents and Gen AI Applications

What Can Agents Do?

Gen AI agents process information, reason over complex concepts, and take action.

Applications include:

  • Customer service

  • Employee productivity

  • Creative tasks

Defining a Gen AI Agent

An application that observes the world and acts on it using its tools to achieve goals.

Capabilities:

  • Understanding and responding to natural language

  • Automating complex tasks

  • Personalization

Agent Workflows

Conversational Agents

  • Input: User types or speaks

  • Understand: AI interprets meaning and intent

  • Call Tool: Searches web, accesses databases, triggers actions

  • Generate Response: Produces a relevant answer

  • Deliver: Provides the output

Workflow Agents

  • Input: User triggers a task (form submission, upload, event)

  • Understand: Defines steps needed

  • Call Tool: Executes integrations, transformations

  • Generate Result: Compiles output

  • Deliver: Sends via email, dashboard, database

Advanced Prompt Engineering Frameworks

  • Rule-based calculations

  • Thought chains

  • Machine learning algorithms

  • Probabilistic reasoning

Examples include ReAct and Chain-of-Thought (CoT).

10. Vertex AI MLOps Tools

Manage the ML lifecycle with built-in tools.

  • Feature Store: Share and serve ML features consistently.

  • Model Registry: Track changes, manage versions.

  • Model Evaluation: Compare model performance.

  • Workflow Orchestration: Automate processes with Vertex AI Pipelines.

  • Model Monitoring: Detect performance degradation and drift.

11. Building Models with Vertex AI

Two main options:

  • Fully Custom: Train at scale with any framework (PyTorch, TensorFlow, scikit-learn, XGBoost).

  • AutoML: Minimal effort, guided training.

12. Gemini Nano

Google’s most efficient, compact AI model for edge deployment.

  • Designed for smartphones, embedded systems.

  • Runs locally for real-time responsiveness and data control.

Tools: Lite Runtime (LiteRT), Gemini Nano

13. Gemini for Google Workspace

Access Gemini’s generative AI features within Gmail, Docs, Sheets, Meet, and Slides.

14. Prompting Techniques

  • Zero-shot: No prior examples.

  • One-shot: Single example.

  • Few-shot: Multiple examples to improve understanding.

Role Prompting

Guide the model by assigning a persona.

Examples:

  • Business analyst

  • Shakespearean actor

  • Customer service agent

Prompt Chaining

Create complex interactions where each prompt builds on the last.

Grounding

Ensures outputs are based on verifiable, specific sources.

Retrieval-Augmented Generation (RAG)

  • Accesses external knowledge sources.

  • Produces more accurate, relevant, transparent outputs.

  • Cites sources used for generation.

15. NotebookLM

An AI-first notebook grounded in your own documents.

Capabilities:

  • Summarize findings

  • Identify connections and contradictions

  • Generate outlines and drafts

  • Answer questions about content

Plus: Adds capacity, customization, usage analytics.
Enterprise: Extra privacy, compliance, IAM controls.

Learn more

16. Sampling Parameters and Settings

  • Token Count: Controls conversation length.

  • Temperature: Controls randomness and creativity.

  • Top-p: Limits probability spread to most likely tokens.

  • Safety Settings: Filters harmful or inappropriate content.

  • Output Length: Defines maximum generated text length.

17. Google AI Studio vs. Vertex AI Studio

Feature Google AI Studio Vertex AI Studio
Audience Experimenters, early-stage users Developers building production systems
Features Easy Gemini API access Advanced tools for the ML lifecycle

18. Prompt Engineering Techniques

ReAct Framework

Combines reasoning and action.

Steps:

  • Think: Generate thoughts about the problem.

  • Act: Take actions (e.g., search the web).

  • Observe: Receive feedback.

  • Respond: Formulate an answer.

Benefits:

  • Dynamic problem-solving

  • Reduced hallucination

  • Increased trustworthiness

Chain-of-Thought (CoT) Prompting

Guides the model through step-by-step reasoning.

Benefits:

  • Improved problem-solving

  • Better accuracy

  • Enhanced explainability

Techniques:

  • Self-consistency

  • Active prompting

  • Multimodal CoT

19. Reasoning Loop with Tools

ReAct Cycle:

  1. Reasoning (Tool Selection)

  2. Acting (Tool Execution)

  3. Observation

  4. Iteration

20. How RAG Works with Tools

  • Retrieval:

    • Data stores

    • Vector databases

    • Search engines

    • Knowledge graphs

  • Augmentation:

    • Incorporate retrieved info into the prompt.

  • Generation:

    • Produce an informed, accurate response.

21. Conversational Agents and Playbooks

Define step-by-step behaviors using linked external tools and data stores.

22. Metaprompting

Enables dynamic, adaptable prompt creation and interpretation.

23. Agentspace

Centralized platform to manage AI agents using company data.

  • Integrates with internal websites and dashboards.

  • Acts as personal research assistants for employees.

Agentspace vs. NotebookLM

Feature NotebookLM Agentspace
Purpose Deep dive into specific documents Enterprise AI assistant across systems
Scope Only user-provided sources All connected business systems
Integration Can connect with NotebookLM Enterprise Unified search and automation

Additional Helpful Resources: