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Bob Ziroll

This course delves into the intricate process of ReAct prompting, a critical component in the design of AI agents. Through dedicated modules, learners will explore the nuanced approach to crafting prompts that effectively guide AI responses in a reactive manner. This technique is essential for developing AI agents that can interact dynamically with users and environments.

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This course delves into the intricate process of ReAct prompting, a critical component in the design of AI agents. Through dedicated modules, learners will explore the nuanced approach to crafting prompts that effectively guide AI responses in a reactive manner. This technique is essential for developing AI agents that can interact dynamically with users and environments.

The curriculum offers an in-depth examination of ReAct prompting, teaching students how to plan and construct prompts that lead to desired actions and responses from the AI. Participants will learn the principles of prompt engineering, which combines elements of psychology, linguistics, and data science to shape the AI's interactions.

In addition to theoretical knowledge, the course emphasizes practical experience. Students will engage in hands-on exercises to refine their skills in building ReAct prompts, understanding the "loop" mechanism that enables continuous and context-aware interactions, and setting up code that facilitates these sophisticated dialogues.

By focusing on ReAct prompting, the course prepares learners to create AI agents that are not only responsive but also proactive in their engagements, resulting in more natural and effective human-AI interactions. This specialized training is particularly beneficial for those interested in enhancing user experience and creating more intuitive AI systems.

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What's inside

Syllabus

Learn AI Agents
This course offers a deep dive into the development of AI agents, with a particular focus on ReAct prompting, a technique for creating responsive, context-aware AI interactions. Participants will learn to craft dynamic prompts, manage continuous loop processes, and develop action functions that allow AI agents to behave in a sophisticated and user-responsive manner. By the end of the course, learners will be equipped with practical skills to build and implement intelligent AI agents capable of complex, real-world interactions.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores common development dilemmas, providing insights for learners
Provides essential information for building robust AI agents
Teaches up-to-date techniques in AI agent design

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn AI Agents with these activities:
Explore the ReAct Prompting Framework
Gain a deeper understanding of ReAct prompting and its applications through guided tutorials.
Browse courses on ReAct Prompting
Show steps
  • Access and review online tutorials on ReAct prompting.
  • Follow along with the tutorials to build and evaluate ReAct prompts.
  • Experiment with different prompt variations to observe their effects on AI responses.
Construct AI Agents
Practice creating AI agents to reinforce understanding of ReAct prompting techniques.
Browse courses on AI Agents
Show steps
  • Review the principles of AI agents and their components.
  • Identify the essential elements of ReAct prompting.
  • Develop a plan for constructing an AI agent using ReAct prompting.
  • Implement your plan by coding the AI agent.
  • Test and refine the AI agent's performance.
Show all two activities

Career center

Learners who complete Learn AI Agents will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
This course dives deep into the process of ReAct prompting, which is a foundational skill for Machine Learning Engineers. Prompt engineering is essential for developing natural language processing (NLP) systems and is a key part of designing AI agents. Therefore, this course provides valuable training for those who hope to work as Machine Learning Engineers.
Artificial Intelligence Engineer
This course is an essential foundation for those who hope to be AI Engineers, particularly those who focus on natural language processing (NLP). Participants in this course learn ReAct prompting, a specific technique for designing intelligent agents trained to interact dynamically with human users.
NLP Engineer
NLP Engineers build and maintain NLP systems, which power conversational AI, chatbots, and machine translation, among other things. This course can help one become an NLP Engineer by providing training in ReAct prompting, a critical skill in NLP.
Data Scientist
Those who wish to become Data Scientists need an understanding of AI coupled with a deep knowledge of statistical methods, machine learning, and big data analysis. Techniques covered in this course like neural networks and prompt engineering will enhance your skills as a data scientist.
Software Engineer
Software Engineers who work with NLP or who code AI agents should be able to craft well-structured prompts to optimize performance and minimize unexpected consequences. This course provides valuable training for those Software Engineers in the rapidly growing field of NLP.
Product Manager
Product Managers who work with conversational AI will benefit from this course. ReAct prompting is a critical skill for Product Managers who need to be able to translate user needs into technical requirements during the product development process.
UX Designer
Conversational AI is an increasingly important area of User Experience (UX) design. This course is particularly valuable for those UX Designers who hope to work with and evaluate conversational AI systems.
Natural Language Processing Specialist
This course may be useful to those hoping to become NLP Specialists. NLP Specialists focus on building and implementing NLP solutions to business problems. The concepts learned in this course - particularly the focus on ReAct prompting - will benefit those in this role.
Technical Writer
Technical Writers who cover AI will benefit from this course. It will help them learn about new developments and trends, as well as provide insights into how developers design and refine AI agents.
Linguist
This course may be useful to those hoping to become Linguists. Computational Linguistics, a subfield of linguistics, is increasingly important in the development of AI systems.
AI Researcher
This course may be useful to those hoping to become AI Researchers. By teaching ReAct prompting, a method for creating reactive AI agents, this course will provide a solid foundation for continued research.
AI Architect
This course may be useful to those hoping to become AI Architects. AI Architects design, build, and maintain AI systems, so understanding techniques such as ReAct prompting is helpful.
Data Analyst
This course may be useful to those hoping to become Data Analysts. Data Analysts need an understanding of the role of AI in transforming data into valuable insights and actionable outcomes.
Mathematician
This course may be useful to those hoping to become Mathematicians. Theoretical Mathematicians have long been interested in AI and contribute to its development.
Computer Scientist
This course may be useful to those hoping to become Computer Scientists. Computer Scientists design and build computer systems and artificial intelligences.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Learn AI Agents.
This comprehensive textbook on human-computer interaction provides a solid foundation in the principles of designing and evaluating user interfaces for AI agents. It complements the course's focus on creating AI agents that can interact effectively with human users.
This renowned textbook on deep learning provides a comprehensive overview of the field. While not specifically focused on AI agents, it offers valuable insights into deep learning algorithms and their applications, which are becoming increasingly relevant in AI agent development.
This practical guide to natural language processing (NLP) techniques is highly relevant to AI agent development. It provides valuable insights into NLP concepts and tools, enhancing the course's focus on effective communication with AI agents.
This classic textbook on reinforcement learning provides a solid foundation in the theory and practice of reinforcement learning algorithms. While not specifically focused on AI agents, it offers valuable insights into the principles of learning and decision-making in AI systems.
This influential book on domain-driven design provides valuable insights into modeling complex business domains. It complements the course's focus on designing AI agents that can effectively understand and interact with real-world scenarios.
This classic book on design patterns provides valuable insights into reusable object-oriented design solutions. It complements the course's focus on designing AI agents that are modular, extensible, and maintainable.
This comprehensive textbook provides a solid foundation in statistical learning techniques. While it is not directly focused on AI agents, it is valuable for understanding the statistical principles underlying AI algorithms and their applications.
This classic book on agile software development provides valuable insights into the iterative and incremental approach to software development. It complements the course's focus on developing AI agents in a collaborative and adaptive manner.

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