Mastering Prompt Engineering for GenAI - Practical Workshop
Learn generative ai & chatgpt through practical, hands-on projects and real-world applications.
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About This Course
What's Covered in this Course. The "Mastering Prompt Engineering for GenAI - Practical Workshop" course is designed to accommodate learners of all levels, from beginners to experienced professionals looking to explore the world of GenAI with effective Prompt Engineering. This course serves as a foundational pillar in Effective Prompt Engineering for GenAI, enabling you to extract accurate and meaningful responses from AI systems.
You will start with the basics of Prompt Engineering, including fundamentals of LLMs, Prompt Anatomy, and essential concepts. The course also covers Prompt Engineering Frameworks, Refining Prompts, and Responsible Prompt Engineering. Practical, hands-on use cases across various domains like HR, Development, L & D, and Art are demonstrated with tools including ChatGPT, Copilot, Visme, LimeWire, and Gemini.
It concludes with best practices, safety considerations, and ethical guidelines. Whether you're new to GenAI/Prompt Engineering or aiming to deepen your understanding, this course is tailored to meet your needs. What is Prompt Engineering.
Prompt engineering is a crucial aspect of working with GenAI systems. It involves crafting clear and specific instructions or queries to interact with GenAI models effectively. By formulating precise prompts, users can extract accurate and meaningful responses from AI systems, enhancing their performance and usability.
Topics Covered
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Course Details
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$109.99
list price when the catalogue was read (2026-07-07); Udemy sale prices run lower
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Concepts behind this area
Responsible AI
Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.
Machine Learning
Machine learning is the branch of computing in which a program works out its rule from examples instead of being handed that rule by a programmer, and is judged on how well the rule holds on cases it was never shown.
Neural Networks
A neural network is a stack of layers of very simple arithmetic units whose connection strengths are adjusted by training until the whole arrangement turns the inputs you have into the outputs you want.
The field, explained: Artificial Intelligence & Machine Learning
Wider context: the fields we cover, the glossary and certification exams, all written by us rather than pulled from a provider feed.