For the Certified Strategic Generative AI Professional (CSGAIP) certification, the objectives list for testing would focus on the practical, real-world application of generative AI technologies.
Domain 1: Foundational Concepts of Generative AI
- 1.1 Differentiate between Generative and Discriminative Models: The test will assess a candidate's ability to explain the core difference between models that generate new data (generative) and those that classify or predict outcomes from existing data (discriminative).
- 1.2 Identify Common Generative Model Architectures: Candidates must recognize and describe the purpose of key architecture such as Large Language Models (LLMs), Diffusion Models, and Generative Adversarial Networks (GANs).
Domain 2: Prompt Engineering and Model Interaction
- 2.1 Apply Prompting Techniques to Optimize Output: A candidate should be able to write effective prompts to achieve specific results from a generative AI model, including using a variety of formats and instructions.
- 2.2 Demonstrate Advanced Prompting Strategies: The test will evaluate the ability to use more complex methods like few-shot prompting, zero-shot prompting, and chain-of-thought prompting to improve the quality of responses.
- 2.3 Recognize Model Limitations and Hallucinations: Candidates must understand the concept of "hallucinations" in AI and be able to identify and mitigate them in a practical setting.
Domain 3: Practical Application and Business Integration
- 3.1 Select Appropriate Generative AI Tools: Candidates should be able to analyze a business problem and choose the most suitable generative AI tool or service for the task.
- 3.2 Integrate Generative AI into Workflows: The test will measure a candidate's understanding of how to use generative AI to automate and enhance common business functions, such as writing marketing copy, summarizing reports, or creating training content.
- 3.3 Use Generative AI for Data-Driven Tasks: A candidate should be able to apply generative AI to analyze and visualize data, turning raw information into actionable insights.
Domain 4: Ethical, Legal, and Responsible AI
- 4.1 Identify Ethical Considerations in AI Use: The test will require candidates to recognize and explain the ethical implications of using generative AI, including issues of bias, fairness, and data privacy.
- 4.2 Address Legal and IP Concerns: Candidates must understand the basic legal and intellectual property challenges related to content created by generative AI.
- 4.3 Implement Best Practices for Responsible AI: The candidate should be able to outline a framework for the safe and responsible deployment of AI within an organization.
Domain: Certified Generative AI Professional (C-GAIP)
The domain of the Certified Generative AI Professional (C-GAIP) certification covers the fundamental concepts, practical applications, and ethical considerations of generative artificial intelligence. This certification is designed for professionals who need to understand, evaluate, and responsibly deploy generative AI technologies to solve business problems and enhance workflows. It is not intended for deep-level AI research or model development, but rather for practical, real-world application.
Objectives
Upon successful completion of the C-GAIP certification, a professional will be able to:
- Comprehend Core Generative AI Concepts: Explain the fundamental principles of generative AI, including the differences between discriminative and generative models, and describe the common architectures such as Large Language Models (LLMs), Diffusion Models, and Generative Adversarial Networks (GANs).
- Apply Prompt Engineering Techniques: Effectively use prompt engineering to guide and optimize the output of generative AI models for various tasks, including content creation, summarization, and data analysis. This includes understanding techniques like few-shot learning and chain-of-thought prompting.
- Evaluate and Select Generative AI Tools: Critically assess and choose appropriate generative AI tools and platforms (e.g., APIs, open-source models) for specific business needs and use cases.
- Implement Generative AI Solutions in Workflows: Integrate generative AI tools into existing professional workflows to automate tasks, improve efficiency, and foster innovation across different functions such as marketing, software development, and customer service.
- Address Ethical and Responsible AI Practices: Identify and mitigate the ethical risks and challenges associated with generative AI, including issues of bias, data privacy, intellectual property, and transparency.
- Measure and Communicate Business Impact: Define key performance indicators (KPIs) to measure the success of generative AI initiatives and effectively communicate the value and ROI to stakeholders.
