Prompt to Agent Engineering: The Next AI Skill

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Master prompt engineering by learning key techniques for designing high-quality AI prompts. Explore the theory behind autonomous AI systems, their components, and business applications, without engaging in practical agent engineering. This course begins by exploring the foundational concepts of AI and prompt engineering, emphasizing text, image, and video generation techniques. You will gain a conceptual understanding of how prompts work in AI systems, focusing on their design and optimization for optimal performance. As you progress, you will dive into the conceptual differences between AI systems and chatbots. The focus will be on autonomy, goal-setting, and how these frameworks apply to AI workflows. Business applications will be discussed, such as automating workflows and using AI for decision-making, with an emphasis on understanding their theoretical aspects rather than practical implementation. This course is designed for AI enthusiasts, data scientists, and professionals looking to deepen their understanding of prompt engineering. It's ideal for those who want to grasp the theoretical foundations of AI systems and their potential applications in business, without focusing on practical agent engineering. By the end of the course, you will have a strong grasp of prompt engineering techniques and the conceptual frameworks behind autonomous AI systems. You’ll be equipped to apply these theoretical insights to real-world scenarios, enhancing your skills and knowledge in the AI space. This course is designed for AI enthusiasts, data scientists, and technical professionals who want to deepen their understanding of AI systems and enhance their prompt engineering skills. It is ideal for those interested in exploring the conceptual foundations of AI systems and improving their ability to work with them. A basic understanding of AI concepts and programming is recommended for anyone looking to fully benefit from this course. This course takes a conceptual and theoretical approach to learning. Each section builds on the foundational knowledge of prompt engineering, focusing on understanding the principles and theoretical applications of AI systems. The course is designed to provide clarity on how AI concepts apply to real-world scenarios, without requiring practical implementation or hands-on engineering. This course is based on Prompt Engineering to Agent Engineering - The Next AI Skill Path, by Anton Voroniuk. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Getting Started with AI and Prompt Engineering
- This module introduces learners to the foundational concepts of AI interaction and prompt engineering, exploring their evolution and significance in modern AI systems. It outlines key learning objectives and provides an overview of how these skills shape AI design and development. Learners will gain a clear understanding of the theoretical and practical aspects of working with AI systems.
Prompt Engineering Techniques and Evaluation Strategies
- This module explores techniques for creating and optimizing prompts across various AI content generation domains, including text, images, and video. Learners will gain an understanding of theoretical frameworks and practical strategies to improve AI outputs. The focus is on identifying best practices, troubleshooting issues, and refining prompt design for maximum effectiveness.
Structured Prompting Frameworks and Techniques
- This module provides an in-depth exploration of generative AI fundamentals, focusing on prompt engineering, conceptual frameworks, and evaluation strategies. Learners will gain a clear understanding of how to structure and optimize prompts for effective AI interaction and performance. It also covers theoretical aspects of prompt architecture and context engineering.
Real-World Business Applications of Prompt Engineering
- This module explores the conceptual application of ChatGPT in various business functions such as knowledge management, email automation, crisis communication, and strategic planning. Learners will gain an understanding of how AI tools can enhance productivity and decision-making in real-world scenarios. The focus is on theory and conceptual strategies rather than technical implementation.
AI Platforms and Tools for Building Intelligent Workflows
- This module explores key concepts in AI platforms, focusing on how to navigate and personalize AI tools, understand integrations, and utilize AI for team collaboration. Learners will gain a theoretical foundation for using AI in workflows and recognize the differences between AI versions.
From Prompt Engineering to Conceptual AI Design
- This module explores the evolution of AI systems from basic prompt engineering to advanced conceptual design. Learners will gain a deep understanding of autonomy, goal-setting, and the distinctions between AI systems and chatbots. The focus is on theoretical frameworks and conceptual workflows for real-world AI applications.
AI Ethics, Governance, and Career Acceleration
- This module covers key aspects of AI ethics, including responsible AI use, data privacy, output verification, and regulatory compliance. Learners will gain insights into the theoretical and practical dimensions of creating trustworthy AI systems and how to leverage AI for career growth.
Conclusion and Next Steps
- This module helps learners reflect on the key concepts covered in the course and develop a practical plan for applying AI techniques in real-world scenarios. It emphasizes the transition from theoretical knowledge to actionable strategies, focusing on building conceptual AI systems.