Recently, I shared Dr. Andrew Ng’s series of Prompt courses. If this is your first time watching Dr. Ng’s original videos, you might find them somewhat content-light and a bit dry, and perhaps you would prefer to watch summaries from popular content creators on good Prompt writing techniques. However, that’s not the case; when you watch each video at least five times, you’ll discover something different—a wonderful experience.
For more details on Dr. Andrew Ng’s Prompt series, please refer to:
Part One “Introduction” can be found in historical shares: Dr. Ng Teaches You to Write Prompts – Part 1 – Introduction
Part Two “Key Principles” can be found in historical shares: Dr. Ng Teaches You to Write Prompts – Part 2 – Key Principles
Part Three “Iterative Methods” can be found in historical shares: Dr. Ng Teaches You to Write Prompts – Part 3 – Iterative Methods
Compilation: Comprehensive Analysis of Dr. Ng’s Prompt Engineering
Starting today, I will continue to share Dr. Ng’s series of courses:Multi-Agent Systems Based on crewAI.
This course aims to provide a comprehensive introduction and in-depth exploration of agent knowledge, with the goal of equipping everyone with the skills to build agent systems through theory and practical cases, ultimately enabling them to construct complex agent teams independently.
The course content is rich, covering aspects such as the operational principles of agents, role-playing, attention focusing, tool usage, boundary setting, memory utilization, and their collaboration methods.
First, the course will introduce the basic definitions and operational mechanisms of agents, helping students understand the foundational knowledge of agents.Next, the course will discuss the application of agents in role-playing, introducing the roles and functions of different agents and how they achieve goals through collaboration.
The course will also delve into how agents focus attention, use tools, and set boundaries to ensure the normal operation and effectiveness of agents.
In the section on agents’ memory and collaboration methods, the course will explain how agents utilize memory to improve efficiency and introduce different collaboration methods such as sequential, hierarchical, and asynchronous.
Subsequently, the course will gradually build different types of agent teams, starting from simple research and writing teams, and progressively constructing more complex teams such as customer support teams, customer promotion teams, and financial analysis teams.
The course also includes a case study on resume optimization, showcasing how to use agents to optimize a resume by analyzing Noah’s resume, thereby increasing the chances of job success. Students will learn how to leverage agents to highlight key skills and experiences in the resume to better match job requirements.
Special Note:Value comes from sharing; please visit https://www.deeplearning.ai/ for the original video.
Deeplearning.ai:is a high-quality learning website created by Dr. Andrew Ng, gathering cutting-edge news, exploratory courses, practical cases, and insights from activities in the field of artificial intelligence. We thank Dr. Ng for his contributions to the field of artificial intelligence.
Today’s sharing ends here; watch a few more times, and you will discover a different beauty.
Stay tuned for the continued sharing: 6 Key Elements of AI Agents
In the AI era, we need to become individuals who think independently.
