Generative AI
Professional Competency & Certification

Develop the Skills to Use, Govern and Secure Generative AI

Generative AI is transforming content creation, analytics, software development, customer service, research, automation, knowledge management, and enterprise decision support.

IBACTP® Generative AI training helps professionals understand how large language models and related technologies work, how to apply them effectively, and how to manage their risks.

What You Will Learn

IBACTP® Generative AI training helps professionals move beyond basic AI awareness and develop practical, strategic, and governance-focused competencies to use generative AI responsibly and effectively in real organizational environments. Depending on the training level, participants may learn how to evaluate, apply, integrate, secure, and govern generative AI systems across business, technical, operational, and leadership contexts.

Generative AI Foundations

Participants may develop an understanding of:

• What generative AI is and how it differs from traditional AI and machine learning
• Major generative AI use cases
• Foundation models
• Multimodal AI
• Text, image, audio, and code generation
• Enterprise applications of generative AI
• Benefits, limitations, and organizational risks

Large Language Models

Training may cover:

• How large language models work at a conceptual level
• Tokens, context windows, and model inference
• Model training and fine-tuning concepts
• Foundation models versus specialized models
• Model selection considerations
• Proprietary versus open-source models
• Model capabilities and limitations
• Evaluating model outputs

Prompt Engineering

Participants may learn how to design effective prompts for professional use cases, including:

• Instruction prompting
• Role prompting
• Context setting
• Few-shot prompting
• Structured output prompting
• Constraint-based prompting
• Iterative prompt improvement
• Prompt evaluation
• Prompt testing
• Prompt libraries

Prompt Design Patterns

Training may introduce reusable prompt strategies for:

• Summarization
• Classification
• Data extraction
• Analysis
• Brainstorming
• Decision support
• Report generation
• Coding assistance
• Research
• Customer service
• Knowledge retrieval
• Business process automation

Participants may also learn when complex prompt structures add value and when simpler prompts work better.

AI Copilots

Participants may explore how AI copilots can support:

• Knowledge workers
• Software developers
• Cybersecurity teams
• Data professionals
• Customer-service teams
• HR professionals
• Finance teams
• Procurement teams
• Executives
• Project managers

Training may address human oversight, workflow integration, data protection, and responsible use.

Retrieval-Augmented Generation

Participants may develop an understanding of Retrieval-Augmented Generation, or RAG, including:

• Why RAG is used
• Connecting AI models to organizational knowledge
• Document retrieval
• Knowledge grounding
• Context injection
• Search and retrieval concepts
• Reducing unsupported responses
• Enterprise knowledge assistants
• RAG architecture fundamentals

Embeddings

Training may introduce embeddings as a way to represent semantic meaning in a form AI systems can use.

Topics may include:

• Embedding concepts
• Semantic similarity
• Search
• Classification
• Clustering
• Knowledge retrieval
• Document matching
• Recommendation use cases

Vector Databases

Participants may learn the role of vector databases in modern generative AI systems, including:

• Vector storage
• Semantic search
• Similarity retrieval
• Document indexing
• RAG applications
• Knowledge-base integration
• Scalability considerations

AI Agents

Training may cover emerging AI agent concepts, including:

• Agent architecture
• Goal-directed AI behavior
• Tool use
• Planning
• Memory
• Multi-step workflows
• Human-in-the-loop controls
• Multi-agent systems
• Agentic automation
• Enterprise agent governance

Participants may also examine the risks associated with giving AI agents access to tools, systems, data, and decision-making authority.

Enterprise Generative AI

Participants may learn how organizations evaluate and deploy generative AI at scale. Topics may include:

• Enterprise use-case identification
• Business-value assessment
• AI maturity
• Model selection
• Platform selection
• Data integration
• Security requirements
• Governance
• Change management
• Adoption
• Measurement of business value
• Enterprise AI operating models

Generative AI Automation

Training may address how generative AI can be combined with workflow technologies to automate tasks such as:

• Document processing
• Email summarization
• Report creation
• Knowledge retrieval
• Customer support
• Data extraction
• Research
• Content generation
• Decision support
• Internal service workflows

Participants may also learn where automation should remain subject to human review.

Knowledge Assistants

Participants may explore the design and use of AI-powered knowledge assistants that can help users access organizational information.

Applications may include:

• Internal help desks
• Policy assistants
• Technical support
• Knowledge management
• Employee onboarding
• Customer service
• Training support
• Research assistance

Training may address data quality, access control, grounding, and content governance.

Responsible AI

IBACTP® training strongly emphasizes responsible generative AI use.

Participants may examine:

• Fairness
• Transparency
• Explainability
• Accountability
• Human oversight
• Privacy
• Safety
• Security
• Bias
• Accessibility
• Responsible deployment

Hallucination Management

Participants may learn how and why generative AI systems can produce inaccurate or unsupported content.

Training may address:

• Hallucination causes
• Factual verification
• Source grounding
• RAG
• Prompt design
• Human review
• Confidence awareness
• Output validation
• Risk-based use policies

The objective is to help professionals understand that fluent AI output should not automatically be treated as accurate.

Prompt Injection

Participants may be introduced to prompt injection and related security risks, including:

• Direct prompt injection
• Indirect prompt injection
• Malicious instructions embedded in content
• Instruction hierarchy
• Tool-use risks
• Data exfiltration
• Agent manipulation
• Defensive controls

Data Leakage

Training may address the risk of exposing confidential, regulated, proprietary, or personal information through generative AI systems. Topics may include:

• Sensitive-data handling
• Public versus enterprise AI platforms
• Data retention
• Access controls
• Employee usage policies
• Privacy considerations
• Data classification
• Secure AI deployment

Deepfakes and Synthetic Media

Participants may explore risks associated with:

• Synthetic video
• AI-generated images
• Voice cloning
• Synthetic identities
• Impersonation
• Fraud
• Misinformation
• Social engineering
• Reputation risk

Training may also discuss detection, verification, and organizational response strategies.

Model Misuse

Participants may examine intentional and unintentional misuse of generative AI, including:

• Fraud
• Phishing
• Social engineering
• Malicious code assistance
• Disinformation
• Identity impersonation
• Unauthorized automation
• Policy violations
• Unsafe decision support

Generative AI Governance

Training may include governance principles for organizational use of generative AI, such as:

• AI policies
• Acceptable-use standards
• Roles and responsibilities
• Risk classification
• Model approval
• Vendor assessment
• Data governance
• Human oversight
• Monitoring
• Incident management
• Auditability
• Performance review

AI Security Controls

Participants may learn about controls that can help protect generative AI applications and infrastructure. These may include:

• Authentication
• Authorization
• Least privilege
• Data encryption
• Secure APIs
• Input validation
• Output filtering
• Logging
• Monitoring
• Prompt protection
• Model access controls
• Secure RAG architecture
• Agent tool restrictions
• Incident response

Applied Generative AI Competencies

By the end of an appropriate IBACTP® Generative AI training program, participants may be better prepared to:

• Use generative AI effectively in professional workflows
• Develop stronger prompts
• Evaluate AI-generated outputs critically
• Identify appropriate enterprise use cases
• Understand RAG, embeddings, vector databases, and AI agents
• Recognize hallucination and misinformation risks
• Protect sensitive information
• Identify prompt injection and model misuse risks
• Apply responsible AI principles
• Support generative AI governance
• Communicate generative AI opportunities and risks to stakeholders
• Participate in enterprise generative AI implementation initiatives

From Prompting to Enterprise AI

IBACTP® Generative AI training is designed around a progression of:

• UNDERSTAND
• PROMPT
• BUILD
• INTEGRATE
• AUTOMATE
• SECURE
• GOVERN
• LEAD

The goal is not simply to teach professionals how to use an AI chatbot. It is to help them understand how to apply generative AI productively, integrate it responsibly, secure it appropriately, and govern it effectively within modern organizations.

Professional Skills Developed

Participants in IBACTP® Generative AI training programs may develop a combination of practical, technical, analytical, security, governance, and business competencies needed to use and manage generative AI effectively in modern organizations.

Depending on the program level, participants may develop competencies in:

• Understanding generative AI, foundation models, large language models, and multimodal AI systems
• Distinguishing generative AI from traditional AI, machine learning, predictive analytics, and automation
• Identifying high-value generative AI use cases across different business functions and industries
• Designing effective prompts using structured prompt-engineering techniques
• Developing reusable prompt templates and prompt libraries for organizational workflows
• Evaluating and improving the quality, relevance, consistency, and reliability of AI-generated outputs
• Using AI copilots and assistants to improve individual and team productivity
• Understanding Retrieval-Augmented Generation (RAG) architectures and their enterprise applications
• Applying concepts involving embeddings, semantic search, vector databases, and enterprise knowledge retrieval
• Understanding AI agents, agentic workflows, tool use, memory, planning, and human-in-the-loop controls
• Designing generative AI-supported workflows for research, analysis, customer service, knowledge management, and business operations
• Integrating generative AI into organizational processes and digital transformation initiatives
• Identifying opportunities for generative AI automation
• Recognizing AI hallucinations and implementing appropriate verification and validation approaches
• Identifying prompt injection, indirect prompt injection, and other generative AI security threats
• Recognizing data leakage, privacy, confidentiality, and intellectual property risks
• Understanding deepfakes, synthetic media, AI-enabled impersonation, and misinformation risks
• Recognizing inappropriate or malicious model use
• Applying responsible AI principles to generative AI development and deployment
• Understanding the importance of fairness, transparency, accountability, explainability, privacy, safety, and human oversight
• Supporting generative AI governance policies and acceptable-use standards
• Evaluating generative AI vendors, models, platforms, and enterprise solutions
• Applying security controls to generative AI applications
• Understanding secure RAG and AI-agent deployment considerations
• Communicating generative AI opportunities, limitations, costs, and risks to technical and non-technical stakeholders
• Supporting organizational AI adoption and workforce transformation
• Evaluating the potential business value and organizational impact of generative AI
• Preparing for applicable IBACTP® Generative AI certification examinations

Applied Business Skills

Participants may also strengthen their ability to apply generative AI to:

• Research and knowledge discovery
• Document analysis and summarization
• Report development
• Customer engagement
• Marketing and communications
• Software development
• Data analysis
• Cybersecurity
• Human resources
• Finance
• Procurement
• Project management
• Operations
• Training and education
• Decision support
• Business process automation

The objective is to help professionals move beyond basic chatbot use toward structured, secure, measurable, and responsible application of generative AI.

Professional-Level Skills

Professional-level training generally focuses on using, applying, evaluating, and supporting generative AI technologies.

Participants may develop skills in:

• Prompt engineering
• AI-assisted research
• AI productivity tools
• AI copilots
• Output validation
• RAG fundamentals
• Knowledge assistants
• AI workflow development
• Responsible AI
• AI security awareness
• Generative AI risk identification
• Business use-case development

The professional competency progression is:

• UNDERSTAND
• PROMPT
• APPLY
• ANALYZE
• VALIDATE
• SECURE
• DEMONSTRATE

Manager-Level Skills

Manager-level Generative AI training extends beyond individual use and focuses on enterprise adoption, governance, risk, investment, workforce transformation, and leadership.

Participants may develop competencies in:

• Developing organizational generative AI strategies
• Identifying and prioritizing enterprise AI opportunities
• Establishing generative AI governance structures
• Developing acceptable-use policies
• Managing generative AI risk
• Evaluating AI vendors and technology platforms
• Establishing human oversight requirements
• Managing AI-enabled workforce transformation
• Measuring generative AI performance and business value
• Managing generative AI projects and portfolios
• Establishing security and privacy requirements
• Managing organizational AI adoption
• Communicating AI strategy to executives and boards
• Developing responsible AI operating models
• Leading cross-functional AI teams
• Establishing generative AI performance indicators
• Managing regulatory and compliance considerations
• Developing organizational AI literacy programs

The manager-level progression emphasizes:

• EVALUATE
• PRIORITIZE
• INTEGRATE
• GOVERN
• MANAGE
• TRANSFORM
• LEAD

Training Formats

Flexible Generative AI Training for Individuals, Teams, and Organizations

IBACTP® Generative AI training may be offered through multiple learning formats to accommodate different professional schedules, learning preferences, certification objectives, and organizational requirements.

Depending on the program, participants may choose from the following delivery options.

Virtual Instructor-Led Training (VILT)

Live online Generative AI training gives participants direct access to instructors while letting them attend remotely.

Virtual instructor-led programs may include:

• Live instructor presentations
• Generative AI demonstrations
• Real-time prompt engineering
• Interactive exercises
• AI tool demonstrations
• Group discussions
• Case studies
• Breakout activities
• Question-and-answer sessions
• Certification exam preparation

This format is ideal for professionals who want structured instruction and instructor interaction without traveling to a physical classroom.

Self-Paced Online Training

Self-paced programs let participants progress through Generative AI content on their own schedules.

Programs may include:

• Recorded instructional modules
• Structured lessons
• Demonstration videos
• Reading materials
• Prompt-engineering exercises
• Knowledge checks
• Practice questions
• Case studies
• Applied activities
• Certification preparation resources

Self-paced learning is especially suitable for working professionals, international learners, and individuals who require scheduling flexibility.

Live Classroom Instructor-Led Training

IBACTP® Generative AI programs may also be delivered through traditional face-to-face classroom instruction.

Classroom training may include:

• Instructor-led lessons
• Live AI demonstrations
• Prompt-engineering exercises
• Team activities
• Case analysis
• Generative AI workshops
• Scenario-based exercises
• Peer collaboration
• Instructor coaching
• Certification preparation

Classroom delivery can be particularly effective for organizations seeking intensive team development.

Generative AI Bootcamps

Generative AI Bootcamps provide accelerated and intensive skills development over a concentrated training period.

Bootcamps may focus on areas such as:

• Generative AI fundamentals
• Prompt engineering
• AI productivity
• RAG
• AI agents
• AI automation
• Responsible AI
• Generative AI security
• AI governance
• Certification preparation

Bootcamp activities may include:

• Learn
• Demonstrate
• Practice
• Build
• Evaluate
• Prepare

Bootcamps suit professionals seeking rapid competency development and organizations implementing accelerated workforce upskilling.

Certification Preparation Training

Certification preparation programs are specifically structured around applicable IBACTP® Generative AI certification competency domains, Bodies of Knowledge, learning outcomes, and examination objectives.

Programs may include:

• Domain-by-domain instruction
• Examination topic reviews
• Scenario-based questions
• Practice examinations
• Applied exercises
• Knowledge assessments
• Instructor review sessions
• Examination-readiness strategies

Completing training does not automatically confer IBACTP® certification. Candidates must independently satisfy all applicable certification and examination requirements.

Hybrid Training

Hybrid programs combine multiple delivery approaches to provide flexibility and structured interaction.

A typical hybrid program may combine:

• Self-Paced Learning
• Virtual Instructor Sessions
• Applied Exercises
• Live Workshops
• Exam Preparation

Hybrid learning can be particularly effective for longer certification and corporate-development programs.

Hands-On Generative AI Workshops

Focused workshops provide practical training on specific Generative AI competencies.

Workshop topics may include:

• Prompt Engineering
• Advanced Prompt Design
• Generative AI for Business
• Generative AI for Cybersecurity
• Generative AI for Data Analytics
• AI Copilots
• RAG Fundamentals
• AI Agents
• Responsible Generative AI
• Generative AI Governance
• AI Security
• Prompt Injection Defense
• Generative AI Risk Management

These workshops are designed for participants seeking targeted development without completing a longer certification-preparation program.

Applied Generative AI Labs

Technical and professional programs may incorporate hands-on laboratory activities. Participants may work with:

• Large language models
• Prompting environments
• AI copilots
• RAG workflows
• Embeddings
• Vector search
• Knowledge assistants
• AI agents
• Workflow automation
• Model evaluation
• AI security scenarios

Applied labs help participants move from conceptual understanding to practical professional capability.

Executive Generative AI Education

Executive programs are designed for:

• Executives
• Directors
• Senior managers
• Business leaders
• Technology leaders
• Board members
• Government leaders
• Organizational decision-makers

Rather than focusing heavily on technical implementation, executive programs may address:

• Generative AI strategy
• Competitive impact
• AI investment
• Enterprise adoption
• AI governance
• Responsible AI
• AI risk
• Cybersecurity
• Workforce transformation
• Regulatory considerations
• Board oversight
• Measuring AI business value

The objective is to help leaders make informed decisions about how to adopt, govern, fund, secure, and scale generative AI.

Corporate Generative AI Training

IBACTP® may provide dedicated Generative AI training for corporate teams and organizations. Programs can be developed for:

• Organization-wide AI literacy
• Department-specific AI adoption
• Executive leadership
• Technical teams
• Cybersecurity teams
• Data teams
• HR teams
• Finance teams
• Marketing teams
• Operations teams
• Customer-service teams

Corporate programs may be delivered virtually, onsite, in a hybrid format, or through customized learning cohorts.

Customized Enterprise Generative AI Programs

Organizations with specialized requirements may request customized programs aligned with their:

• Industry
• AI strategy
• Technology environment
• Business processes
• Workforce roles
• Governance requirements
• Security requirements
• Organizational AI maturity
• Regulatory environment

Customized programs may combine:

• AI skills assessments
• Generative AI literacy
• Role-based training
• Prompt engineering
• AI security
• AI governance
• Executive education
• Certification preparation
• Applied workshops
• Team projects

Cohort-Based Training

IBACTP® may provide cohort-based Generative AI programs for organizations, universities, government agencies, professional associations, and other groups. Cohort programs may provide:

• Scheduled learning
• Instructor guidance
• Peer collaboration
• Group exercises
• Applied projects
• Progress milestones
• Practice assessments
• Certification preparation

Training Delivery Options at a Glance

IBACTP® Generative AI training may be available through:

• Virtual Instructor-Led Training (VILT)
• Self-Paced Online Training
• Live Classroom Training
• Generative AI Bootcamps
• Hybrid Training
• Certification Preparation Programs
• Hands-On Workshops
• Applied AI Labs
• Executive Education
• Corporate Training
• Customized Enterprise Training
• Cohort-Based Learning

Learn Generative AI Your Way

Whether you are beginning your Generative AI journey, preparing for professional certification, developing advanced technical skills, managing AI initiatives, or leading enterprise AI transformation, IBACTP® provides flexible training pathways designed around professional competency.

• UNDERSTAND
• PRACTICE
• APPLY
• SECURE
• GOVERN
• CERTIFY
• LEAD

Flexible Learning. Applied Skills. Responsible AI. Certification-Aligned Professional Development.

Who Should Attend

Suitable participants include:

• AI practitioners
• Business professionals
• Developers
• Data scientists
• Cybersecurity professionals
• Content and knowledge professionals
• Product managers
• Consultants
• Managers
• Executives
• Educators
• Technology leaders

Organizational Applications

Training may address applications in:

• Customer service
• Cybersecurity
• Data analysis
• Research
• Knowledge management
• Software development
• Marketing
• HR
• Procurement
• Finance
• Operations
• Education
• Executive decision support

Generative AI Risk & Governance

Special attention is given to:

• Privacy
• Security
• Bias
• Intellectual property considerations
• Model hallucinations
• Data governance
• Prompt injection
• Deepfakes
• Synthetic content
• Responsible deployment
• Human oversight

Career Relevance

Potential career-development areas include:

• Generative AI Specialist
• AI Prompt Professional
• AI Automation Specialist
• AI Governance Professional
• Generative AI Consultant
• AI Product Manager
• AI Risk Specialist
• AI Security Professional

Explore Generative AI Training

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