Writing Effective Prompts for Generative AI
As generative AI becomes more common, the ability to interact with large language models is shifting from niche knowledge to a necessary skill across many different industries and roles. In this course, you will learn the fundamentals of prompting large language models and exploring further techniques for improving the output from large language models.
What you will learn
- Explain the capabilities of generative AI and list possible use cases.
- Identify the differences between keyword search and prompting, and list the benefits of prompting.
- Describe and apply prompt design best practices.
- Describe and apply approaches to improve the accuracy of generative AI responses.
Prerequisites
- Attendees should have access to gemini.google.com to complete the course activities.
- There are no other prerequisites for this course. It aims to provide both introductory and advanced insights into generative AI, making it suitable for individuals with varying levels of AI expertise.
Target audience
- Business professionals who apply generative AI techniques to solve common day-to-day tasks
Training Program
5 modules to master the fundamentals
Objectives
- Explain the capabilities of generative AI and list possible use cases.
Topics covered
- →Definition of generative AI
- →Capabilities of generative AI
- →Tasks generative AI can help with
Activities
Group discussion
Objectives
- Identify the differences between keyword search and prompting, and list the benefits of prompting.
Topics covered
- →Differences between keyword search and prompting
- →Prompt examples
- →The benefits of prompting
Activities
Activity with Google Gemini
Objectives
- Describe and apply prompt design best practices.
Topics covered
- →The importance of prompt design
- →General best practices
- →Use prompt building blocks
- →Provide examples
- →Iterate through prompts
- →Define follow-up prompts
- →Chain-of-thought
- →Prompt editor
Activities
6x demos
Activity with Google Gemini
Objectives
- Describe and apply approaches to improve the accuracy of generative AI responses.
Topics covered
- →Hallucinations definition
- →Use prompt instructions
- →Grounding and citations
- →Double-check responses
- →Developer settings
Activities
3x demos
Activity with Google Gemini
Topics covered
- →Quiz questions
- →Course summary
- →Q&A
Activities
Quiz
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Group Discussion — Open exchange among peers and the instructor on a specific topic.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote