Efficient Prompt Engineering for Librarians

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ACRL Online Course
August 5-30, 2024

This four-week program is tailored specifically for librarians, focusing on the practical application of generative AI (GenAI) and large language models. It aims to seamlessly integrate these AI principles into the evolving landscape of librarian work, enhancing their skill set for various library services.

Week 1 delves into the basics of generative AI, large language models, and prompt fundamentals. Participants will learn how to tailor prompts to various task needs and advance their ability to interact with AI chatbots. 

In Week 2, participants will learn different prompt designs and techniques to instruct AI chatbots to respond with comprehensive answers and effectively enhance responses to complex tasks. They will also understand how to structure prompts and use examples that direct AI in synthesizing information from various sources and tailoring responses to specific user needs. 

In Week 3, participants will learn advanced prompt engineering techniques. This week involves using prompt patterns to create effective prompts that adeptly handle library-specific scenarios, focusing on in-depth subject matter expertise in various library contexts, thereby enhancing the librarians' proficiency and overall productivity. 

In Week 4, participants consolidate their learning by developing a collection of prompt 'recipes' for different library operations and scenarios. This hands-on week encourages the practical application of AI and prompt engineering skills gained throughout the course. Librarians will create tailored prompts for tasks like research assistance, cataloging, strategic planning, data analytics, project management, and user support, resulting in a toolkit of prompt strategies and solutions for various library functions. 

Throughout the course, participants will engage with online course materials, assignments, discussions, and Q&A sessions, fostering a comprehensive learning environment. They will build a solid foundation in AI and prompt engineering, acquiring actionable strategies and prompts tailored for librarianship in the contemporary AI landscape. This course aims to equip librarians with the necessary tools and insights, enabling them to navigate confidently the evolving demands of the librarian profession.

Learning Outcomes

  • Gain knowledge in generative AI and prompt engineering applicable to library settings.
  • Apply prompt engineering techniques to efficiently use GenAI applications such as ChatGPT or Copilot for library-related tasks.
  • Develop AI prompts suitable for a range of library tasks and operations

Target Audience

Academic librarians, public librarians, instruction and outreach librarians 


Dr. Yinlin Chen is the Assistant Director, Center for Digital Research and Scholarship, at Virginia Tech University Libraries. He earned his B.S. and M.S. degrees in Computer Science from National Tsing Hua University, Taiwan, and his Ph.D. in Computer Science & Application from Virginia Tech. His research interests include Digital Libraries, Machine Learning, Deep Learning, Cloud Computing, Natural Language Processing, and Information Retrieval. Passionate about cloud-based applications using AWS and GCP, Dr. Chen has taught Introduction to Artificial Intelligence and Database Management Systems courses. He has served on program committees and reviewers for conferences such as CIKM, SIGIR, JCDL, iPRES, ICADL, TPDL, and DLF Forum. 

Tech Requirements

The course will be offered in the ALA LMS; a computer with Internet connection is required. Your participation will require approximately 3-5 hours per week of primarily asynchronous activities to:  

  • Read the online course materials
  • Post to online discussion boards and/or participate in optional Zoom chats
  • Complete online exercises and/or final assignment

Participants who complete the course requirements will receive a certificate of completion.

Contact Margot Conahan at mconahan@ala.org with questions or requests for special assistance or accommodations.