Generative AI Capability Foundation

GenAI Landscape Foundation®

Build a strong foundation in the Generative AI landscape and understand the technologies, models, data, prompting, applications, opportunities, limitations, and responsible practices shaping the AI-enabled workplace.

Talk to WorKnoW Explore course

GenAI FoundationsUnderstand the AI landscape

AI EcosystemExplore models, data and applications

PromptingBuild practical AI interaction skills

Responsible AIUnderstand risks and responsible adoption

Understand the GenAI landscape before you start building with it.

Generative AI is more than a chatbot. It is an evolving ecosystem of models, data, infrastructure, applications, interfaces, workflows, and human interaction. This foundation program provides a structured way to understand that ecosystem and its practical implications.

Build AI literacy

Understand the essential concepts and terminology needed to navigate the rapidly changing Generative AI landscape.

Understand the ecosystem

Explore the relationship between models, data, prompts, AI tools, applications, users, and business workflows.

Apply AI responsibly

Recognize the opportunities, limitations, risks, governance needs, and human responsibilities associated with Generative AI.

Designed for professionals who want a strong GenAI foundation.

No advanced programming or data science background is required. The focus is on building practical AI literacy and understanding.

Business & professional roles

Project Managers
Program and Portfolio Professionals
Business Analysts
Consultants
Team Leaders
Operations Professionals
Managers and Business Leaders

Technology & transformation roles

Digital Transformation Professionals
Product Professionals
Technology Professionals
Innovation Teams
AI Adoption Teams
Change Professionals
Professionals beginning their AI journey

See how the major pieces of Generative AI fit together.

Develop a connected view of the technologies and concepts that make modern Generative AI systems possible.

Artificial Intelligence

Understand AI as the broader field that includes machine learning, language technologies, computer vision, and other approaches.

Generative AI

Explore systems designed to generate new content such as text, images, audio, video, and other outputs.

Foundation Models

Understand the role of broad-purpose models that can support multiple AI-powered tasks and applications.

Large Language Models

Build a practical understanding of models capable of processing and generating natural language.

Data

Understand the role of data, information, training, context, and knowledge in AI systems.

Prompts & Context

Explore how users provide instructions and contextual information to guide AI-generated outputs.

AI Applications

Understand how models are integrated into products, workflows, assistants, and organizational processes.

Human Interaction

Explore the role of users, domain experts, review, judgment, and feedback in AI-enabled work.

From data to model to application.

Data

Information and data supporting AI systems.

Models

AI models that process inputs and generate outputs.

Prompts

Instructions and context provided to AI.

Applications

AI capabilities embedded into useful tools.

Workflows

AI integrated into real-world professional work.

Build your GenAI foundation step by step.

The learning journey progresses from AI fundamentals to the wider Generative AI ecosystem, prompting, applications, risks, and responsible use.

Module 1: Introduction to Artificial Intelligence

What is Artificial Intelligence?
AI versus traditional software
Machine learning fundamentals
AI applications across industries
Evolution toward Generative AI

Module 2: Understanding Generative AI

What is Generative AI?
How Generative AI differs from traditional AI
Types of generated content
Common GenAI applications
Current opportunities and limitations

Module 3: The Generative AI ecosystem

AI models
Foundation models
Large language models
Multimodal AI
AI applications and platforms
AI ecosystem participants

Module 4: Data and Generative AI

Role of data in AI
Training and data concepts
Structured and unstructured data
Context and information
Data quality
Data-related AI limitations

Module 5: AI models and Large Language Models

Understanding AI models
Large language models
Inputs and outputs
Model capabilities
Model limitations
Understanding AI-generated responses

Module 6: Prompt engineering fundamentals

What is prompt engineering?
Role and context
Instructions and objectives
Constraints
Output formatting
Iterative prompting

Module 7: Generative AI applications

Text generation
Image generation
Audio and speech applications
Video generation
Summarization and transformation
AI assistants
Knowledge and productivity workflows

Module 8: AI in business and the workplace

AI-assisted knowledge work
Productivity opportunities
Business process applications
Workflow transformation
AI adoption considerations

Module 9: AI risks and limitations

AI hallucinations
Bias
Accuracy and reliability
Data limitations
Security considerations
Human verification

Module 10: Responsible Generative AI

Responsible AI principles
Privacy and confidentiality
Human oversight
Governance
Accountability
Ethical considerations

Module 11: Building an AI-ready mindset

Identifying useful AI opportunities
Evaluating AI use cases
Working effectively with AI
Human + AI collaboration
Continuous learning
Preparing for AI-enabled work

Learn the foundation of effective human–AI interaction.

Prompting is one of the fundamental skills for working effectively with Generative AI. Learn how to provide context and structure requests for better results.

Prompt structure

Understand the building blocks of a useful prompt.

RoleContextTaskInstructionsConstraintsExpected output

Prompt refinement

Learn how to improve results through iterative interaction.

Ask follow-up questionsImprove contextChange output formatsRequest alternativesChallenge assumptionsValidate outputs

Where can GenAI create practical value?

Content creation

Draft, transform, summarize, and improve professional content.

Knowledge work

Explore information, organize knowledge, and support research tasks.

Communication

Create and adapt communications for different audiences.

Analysis

Support structured analysis, comparisons, brainstorming, and synthesis.

Automation

Identify repetitive knowledge-work tasks that may benefit from AI.

Decision support

Explore alternatives, assumptions, scenarios, and questions.

Learning

Use AI as a learning assistant, explainer, practice partner, and knowledge companion.

Productivity

Support everyday professional tasks and information workflows.

Innovation

Explore ideas, alternatives, concepts, and new ways of working.

Build the foundation you need to navigate Generative AI.

✓Explain the fundamentals of Artificial Intelligence and Generative AI.

✓Describe the major components of the Generative AI ecosystem.

✓Understand the role of data, models, prompts, context, and applications.

✓Understand the basics of foundation models and large language models.

✓Create structured prompts for common professional tasks.

✓Identify practical Generative AI applications across the workplace.

✓Recognize common AI risks, limitations, and reliability concerns.

✓Apply responsible AI thinking when evaluating potential use cases.

Understanding AI also means understanding its limits.

Generative AI can be powerful, but AI-generated content can contain errors, unsupported claims, bias, or misleading information. Responsible use requires human judgment, appropriate validation, and consideration of privacy and governance.

Accuracy

Understand that AI-generated information should be evaluated and verified.

Privacy

Consider how sensitive or confidential information is handled.

Bias

Recognize that AI systems and generated outputs can contain bias.

Human oversight

Keep people responsible for important decisions and outcomes.

Build AI capability, not just AI awareness.

Structured learning

Move systematically from AI fundamentals through models, data, prompting, applications, risks, and responsible adoption.

Business context

Understand Generative AI from the perspective of professional work, workflows, productivity, and organizational transformation.

Practical application

Connect concepts with real workplace activities rather than treating Generative AI as an abstract technology topic.

Responsible adoption

Enrol now

For groups larger than 10, contact us for a quote.

Frequently asked questions

Do I need a technical AI background?

No. This is a foundation-level learning experience designed to develop practical AI literacy. Advanced programming or data science knowledge is not required.

What is covered in the GenAI Landscape Foundation?

The program covers Generative AI fundamentals, the AI ecosystem, models, data, large language models, prompting, applications, workplace use cases, limitations, risks, and responsible AI.

Is this course only for technology professionals?

No. The course is intended for professionals across business, project management, operations, consulting, transformation, technology, and other knowledge-work environments.

Will I learn prompt engineering?

Yes. Prompt engineering fundamentals are included, with emphasis on context, instructions, objectives, constraints, output formats, and iterative refinement.

Is this a certification course?

Any certification or credential associated with the course should be confirmed against the current course and credential information provided by the relevant issuing organization.

Build your foundation for the GenAI era.

Understand the Generative AI landscape, explore practical applications, develop prompting skills, and build the confidence to work with AI responsibly.

Talk to WorKnoW

GenAI Landscape Foundation® is presented as a training and learning program. Trademark ownership and certification relationships, if any, should be confirmed against the applicable course or credential owner.