Generative AI Learning Library
Generative AI Learning Library
This page contains outside resources for learning about generative AI, at all levels of AI Literacy. These resources have been vetted by the ATLIS team. For resources created by the ATLIS and other Barnard departments, please see the resources on our Using Generative AI at Barnard page.
Understanding AI
These resources explain how generative AI works, as well as introducing the vocabulary and concepts that you may come across in your AI exploration. Some are more technical while others are more conceptual; we invite you to use the ones that match your own interests, skills, and expertise.
Short article that demonstrates how text generators determine what they output
Conceptual overview from MIT discussing what generative AI is and how it is different than other types of AI
Guide from UNC that defines basic AI terminology, showcases AI tools, and discusses using AI in academic context
Free, self-paced courses that offers an introduction to all things AI - from the history of how it developed to the algorithms that power it
One-hour video from 2023 that explains the technical components of chatbots for a general audience. Note that some of the models and applications have been updated since this video was created, but the underlying concepts introduced here remain the same
An expanded and updated version of the "Intro to LLMS" video for those who wanted to understand the technology more in-depth, produced in 2025
Using & Applying AI
These resources provide information on how to effectively make use of generative AI tools.
Robust guide offering explanations and examples of different prompting techniques, as well as targeted guidance for specific models and tasks
Article by Leo Lo at the University of New Mexico, which introduces the CLEAR framework for effectively prompting generative AI and analyzing it's output
Libguide from Georgetown University that briefly summarizes how to use the CLEAR framework for writing prompts
Though aimed at instructors, this webpage from the University of Pennsylvania showcases a prompting framework called RICO that can be utilized by anyone
Video showing practical strategies for effectively using LLMs such as ChatGPT
Analyze & Evaluate AI
These resources provide guidance on how to critically evaluate AI tools and their outputs, with a focus on accuracy, bias, ethical implications, and environmental impact.
This paper provides a comprehensive analysis of the ethical and social risks associated with large-scale language models.
This library guide from Amherst on Generative AI highlights ethical concerns such as bias, labor exploitation, environmental harm, and intellectual property issues.
This article explores how generative AI models can reinforce and even amplify racial and gender biases through image generation.
This article profiles women who sounded early warnings about AI’s dangers, including bias, inequality, and societal harm, long before ChatGPT emerged.
This article delves into research explaining why large language models generate fictitious or misleading content, demonstrating how internal structures can override safety mechanisms and lead to confident errors.
This webpage outlines the environmental consequences of developing and deploying powerful generative AI models, including increased electricity demand and water consumption.
Create AI
These resources focus on building, customizing, and deploying AI models.
This is a beginner-friendly course that teaches how to use, fine-tune, and deploy pre-trained AI models for natural language processing and more.
The cookbook offers practical code examples and guides for using OpenAI models.
This webpage from Anthropic explains how to set clear, measurable goals for AI applications. It details how to define, quantify, and align evaluation criteria before building or deploying prompts.
This free online course offers a hands-on introduction to practical AI methods like machine learning and neural networks.