Stanford CS193T Thinking with AI | Autumn 2026 | Managing Sources
For more information about Stanford’s graduate programs, visit:https://stanford.io/cs193t-autumn26
Lectures 1 and 2 introduced the context window and interactive prompting. Lecture 3 focuses on context, information, and prompt instructions that persist beyond a single chat session. This includes everything else sharing your context window — settings, remembered facts, uploaded files, and retrieved web pages - and introduces retrieval-augmented generation (RAG) as a solution for grounding context that would overflow the context window. Examples include how to use these tools to reduce sycophancy and “AI style,” understanding what your chat history is (and isn’t) used for, saving your best prompts as reusable Skills or Gems, and strategic withholding–how sometimes the smartest prompt leaves something out. We introduce Retrieval-Augmented Generation (RAG) for grounding AI in your own documents, and the documented ways trust in a RAG store can fail. We close on a cybersecurity note with prompt injection: why an AI can’t always tell your instructions apart from malicious text on the internet.
Speaker: Cynthia Bailey
Cynthia Bailey is a Senior Lecturer in the Computer Science Department at Stanford University. In 2023-2024, she served as an AI Policy Fellow in the United States Senate through the American Association for the Advancement of Science (AAAS). Her scholarship focuses on computer science education, AI/machine learning, and the social impacts of technology. This work includes creating groundbreaking courses such as Equity and Governance for Artificial Intelligence and Race and Gender in Silicon Valley. Her teaching and research awards include the Lloyd W. Dinkelspiel Award for exceptional contributions to undergraduate education at Stanford, the Lasting Impact Award from the International Computing Education Research conference, a "Top 10 Papers of All Time" award at the 50th anniversary of the ACM SIGCSE Technical Symposium, and the Stanford Society of Women Engineers' Professor of the Year. She has consulted for Apple, NASA, law firms, and machine learning startups. She holds a Ph.D. in Computer Science from UC San Diego.
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