Description
1-Day Training Program Focused on Enterprise AI Use
Enterprise AI Prompt Engineering Training equips participants with systematic and structured prompt-writing skills, enabling them to generate consistent, reliable and business-aligned outputs from generative AI systems.
Participants learn how to combine role, goal, context, constraints and output-format instructions to create clear, measurable and reusable prompts. This structured approach helps organizations move beyond individual trial and error and establish prompt-writing standards that can be applied consistently across teams.
By the end of the training, participants will be able to apply structured prompting techniques in enterprise scenarios, produce more verifiable and repeatable outputs and develop standardized prompt templates for shared organizational use.
The training has a strong hands-on focus. At the end of the program, participants design a prompt template tailored to their business domain and solve the same problem using both free-form and structured prompting. The resulting outputs are then compared in terms of quality, accuracy and consistency.
Training Outcomes
- A systematic and standardized prompt-writing approach
- The ability to use role, goal, context, constraints and output-format instructions
- An enterprise-ready prompt template framework
- The ability to create reusable prompt libraries
- Improved consistency and reliability in AI-generated outputs
- Techniques for reducing the risk of hallucinations
- Prompt versioning and standardization across teams
- Increased awareness of secure and responsible enterprise AI use
Who Should Attend?
- Professionals using generative AI in business processes
- Managers and team leaders
- Business analysts and project managers
- Strategy and corporate development teams
- Marketing, content and corporate communication professionals
- Human resources, finance, risk and operations teams
- Software development and technology teams
- Organizations seeking to establish consistent AI usage standards
Outline
1. Understanding Large Language Models
- High-level working principles of large language models
- How generative AI systems produce outputs
- Token, context window and temperature concepts
- Differences between deterministic and creative outputs
- AI hallucinations and their common causes
- How prompts influence model behaviour
- Why AI-generated outputs require human review
2. Prompt Engineering Fundamentals
- Role definition
- Goal and task definition
- Providing relevant context
- Defining constraints
- Identifying the target audience
- Defining the required output format
- Establishing success and quality criteria
- Few-shot and example-based prompting
- Plan-first prompting
- Step-by-step prompting
- Breaking complex tasks into smaller steps
3. Enterprise Prompt Engineering Best Practices
- Creating reusable prompt templates
- Designing templates with variable fields
- Building a reusable prompt library
- Recording and categorizing successful prompts
- Prompt versioning
- Comparing different prompt versions
- Output-validation techniques
- Asking the model to identify assumptions
- Requesting clarification when information is incomplete
- Token efficiency and context management
- Separating instructions, data and examples
- Enforcing output formats
- Defining length, tone and writing style
- Enterprise data-security principles
4. Prompt Design by Business Function
- Software development and code-explanation scenarios
- Error analysis and test-case generation
- Business analysis and requirements definition
- Document summarization and information extraction
- Structuring meeting notes
- Risk analysis and decision-support outputs
- Comparing alternatives against defined criteria
- Prompt design for management and strategy use cases
- Creating executive summaries
- Producing outputs for different stakeholder groups
5. Risks and Secure Enterprise Use
- Hallucination-mitigation techniques
- Requesting disclosure of uncertain information
- Checking AI outputs for accuracy and consistency
- Requesting sources and supporting reasoning
- Human validation of AI-generated outputs
- Protecting sensitive enterprise data
- Avoiding the disclosure of personal and confidential information
- Anonymizing data before using AI systems
- Compliance with organizational policies and applicable regulations
- Using approved AI tools
- Maintaining human oversight and accountability
- Establishing an enterprise AI usage framework
6. End-of-Day Hands-on Exercise
- Selecting a use case from the participant’s own business domain
- Creating a free-form prompt for the selected task
- Creating a structured prompt using role, goal, context, constraints and output format
- Comparing the outputs produced by both approaches
- Evaluating quality, accuracy, consistency and usability
- Improving the prompt based on the evaluation results
- Creating a reusable enterprise prompt template
Prerequisites
No software development, data science or advanced artificial intelligence knowledge is required to attend this training.
Basic computer skills and a general awareness of generative AI use cases are sufficient.
For the hands-on exercise, participants are encouraged to identify a sample use case from their own business processes that does not contain sensitive, personal or confidential information.
Frequently Asked Questions
What is Enterprise AI Prompt Engineering Training?
Enterprise AI Prompt Engineering Training teaches participants how to create structured prompts that generate more consistent, reliable and business-aligned outputs from generative AI systems.
How long is the training?
The training lasts one day and combines essential prompt-engineering concepts with hands-on enterprise exercises.
Who should attend the training?
The training is suitable for managers, team leaders, business analysts, project managers, marketing professionals, human resources teams, finance and operations teams, software developers and other professionals who use generative AI at work.
Are technical or programming skills required?
No. Software development, data science or advanced AI knowledge is not required. Basic computer skills and general awareness of generative AI are sufficient.
Which prompting techniques are covered?
The course covers role, goal, context, constraints and output-format instructions, as well as few-shot, plan-first and step-by-step prompting techniques.
Does the training cover enterprise data security?
Yes. The course addresses sensitive data protection, data anonymization, approved AI tools, organizational policies, human oversight and the validation of AI-generated outputs.
Will participants create their own prompt template?
Yes. During the end-of-day exercise, participants create and improve a structured, reusable prompt template tailored to a business use case.