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Lecturer(s)
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Holý Richard, Ing. Bc. Ph.D.
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Course content
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In this course, students will learn about a modern approach to the effective use of intelligent systems based on artificial intelligence through "prompt engineering". The goal is to learn how to formulate thoughtful and structured assignments for large language models and other AI tools in order to maximize the benefits for engineering practice. Through this course, students will gain skills that enable them to solve complex engineering problems, interact with AI tools, and produce high-quality documents for analysis, design, and new product development. Topics for 13 weeks: 1. Introduction to Prompt Engineering: Basic concepts, motivations, and importance in engineering practice. 2. Principles of Large Language Models (LLM): Structure, learning and limitations of models. 3. Formulating effective prompts: How to define the goal, context, style and scope of a response. 4. Optimizing Prompts for Engineering Computation. 5. Generating input data and interpreting results. 6. Creating technical reports, manuals and user guides using LLM. 7. Evaluating the quality of responses: Methods for measuring accuracy, relevance, and usability of results. 8. Troubleshooting and debugging of prompts: Modifications to prompts in response to unsatisfactory outputs. 9. Ethical and legal aspects: Liability, data protection and intellectual property in relation to prompt engineering. 10. Specific scenarios in engineering. 11. Automation and scripting. 12. Advanced prompt engineering techniques: Prompt chaining, role playing, use of system and user prompts. 13. Trends overview and future outlook: new generations of models, adaptive prompts, and emerging applications in industrial practice.
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Learning activities and teaching methods
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Lecture supplemented with a discussion, E-learning, Multimedia supported teaching, Students' portfolio, Individual study, Practicum
- Individual project (40)
- 13 hours per semester
- Team project (50/number of students)
- 12 hours per semester
- Presentation preparation (report) (1-10)
- 4 hours per semester
- Undergraduate study programme term essay (20-40)
- 8 hours per semester
- Contact hours
- 26 hours per semester
- Preparation for comprehensive test (10-40)
- 6 hours per semester
- unspecified
- 10 hours per semester
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| prerequisite |
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| Knowledge |
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| be familiar with basic technological terminology |
| have a general technical orientation and the ability to seek out and critically evaluate technical information |
| to know the basic operations in the operating system |
| to know the basics of working with the Internet |
| to know the basic functionality of the computer |
| Skills |
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| independently obtain and process professional information |
| be able to use simple tools for recording and clearly displaying information |
| have basic operating system skills |
| have basic internet skills |
| have basic PC skills |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| have an overview of generative artificial intelligence tools |
| have knowledge of basic ethical rules and the use of AI |
| have a basic understanding of how generative language models work |
| Skills |
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| have the ability to appropriately describe a generative AI task or requirement |
| have the ability to analyse the outputs of AI models and interpret their results for practical application |
| Ability to create effective and accurate prompts for various AI applications |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Practicum |
| Individual study |
| Lecture with visual aids |
| Project-based instruction |
| E-learning |
| Task-based study method |
| Students' portfolio |
| Discussion |
| Textual studies |
| Skills |
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| Practicum |
| Seminar classes |
| Interactive lecture |
| Textual studies |
| Project-based instruction |
| Cooperative instruction |
| Students' portfolio |
| E-learning |
| Task-based study method |
| Competences |
|---|
| Practicum |
| Seminar classes |
| E-learning |
| Cooperative instruction |
| Project-based instruction |
| Textual studies |
| Students' portfolio |
| Task-based study method |
| assessment methods |
|---|
| Knowledge |
|---|
| Skills demonstration during practicum |
| Seminar work |
| Individual presentation at a seminar |
| Self-evaluation |
| Peer evaluation of students |
| Test |
| Skills |
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| Skills demonstration during practicum |
| Peer evaluation of students |
| Test |
| Individual presentation at a seminar |
| Continuous assessment |
| Project |
| Competences |
|---|
| Skills demonstration during practicum |
| Test |
| Project |
| Seminar work |
| Individual presentation at a seminar |
| Peer evaluation of students |
| Self-evaluation |
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Recommended literature
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Fry, Hannah. Hello world : jak zůstat člověkem ve světě algoritmů. Vydání první. 2020. ISBN 978-80-7601-246-2.
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Kismet Dursun. How to Chat with ChatGPT: The Beginner?s Guide to Using AI Every Day with ChatGPT for Non-Technical. 2024. ISBN 979-8326964885.
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Lasse Rouhiainen. CHATGPT - 101 Things You Must Know Today About ChatGPT and Generative AI (Artificial Intelligence). 2024. ISBN 979-8329315905.
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