EXAM CODES S03-101
Prompt engineering optimizes language model responses by strategically designing clear and effective prompts. It involves carefully crafting the wording, structure, and context of prompts to effectively communicate desired tasks or queries to the model. It unlocks the model's full potential by providing explicit instructions for precise outputs. Prompt engineering, a widely recognized and discussed practice, has garnered significant interest among researchers, developers, and industry experts. Its versatile application spans research, data science, AI products, Conversational AI Designs, quality assurance, and AI policy analysis, contributing to the advancement of the AI revolution.
The Star Prompt Engineering certification program aims to help the learner acquire an understanding of the basics of prompt engineering, language models, Chain of thought prompting, Zero-Shot and Few-Shot Prompting crafting effective prompts, guiding model behaviour, evaluating model outputs, practical applications, ethical implications of prompt engineering, and the future of language models.
Audience
Beginners in either IT or non-IT domains who possess limited or no prior knowledge of AI may benefit from this. Familiarity with AI Essentials, basic programming & communication skills is preferred.
Course Objectives:
In this course, you will learn about:
Understanding language models
Exploring prompt engineering
Crafting Effective prompts
Evaluating model outputs
Experimenting with prompts
Concepts of reinforcement learning
Discussing the ethical implications of prompt
Exploring practical applications
Course Outcomes:
After completing this course, you will be able to recognize and/or demonstrate:
Understand how ML and DL work
Explain prompt engineering and its future
Explore language models with prompts
Describe the four pillars of prompt perfection
Craft effective prompts and Demonstrate image prompting techniques
Explain different OpenAI Applications and Evaluate model outputs
Experiment with Prompts and enhance Prompt Reliability
Explain real-world applications
Table of Contents Course Outline
1. Introduction to Large Language Models
2. AI, ML, DL, and NLP Basics
3. Introduction to prompt engineering
4. Understanding prompts
5. The four pillars of prompt perfection
6. Crafting effective prompts
7. Techniques of Text Prompting
8. Applications of Effective Prompting
9. Experimenting with prompts
10. Zero-Shot & Few-Shot Prompting
11. Different OpenAI Applications
12. Techniques of Image Prompting
13. Enhancing Prompt Reliability
14. Guiding model behaviour
15. System messages and tone
16. Evaluating model outputs
17. Chain of thought prompting
18. Advanced Prompt Engineering
19. LLM Embedding and Fine-tuning
20. Introduction to reinforcement learning
21. Responsible AI and ethical considerations
22. Practical applications and case studies
23. Future of language models and prompt engineering
| Exam Codes | Star Prompt Engineering S03-101 (Academy customers use the same codes) |
|---|---|
| Launch Date | Jan 15 2024 |
| Number of Questions | 60 |
| Type of Questions | Multiple choice questions |
| Length of Test | 90 Minutes |
| Passing Score | 70 |
| Recommended Experience | Beginners level |
| Languages | English |
