
Explore the basics of prompt engineering in healthcare ai and how understanding prompts can democratize access to high-quality healthcare. Learn about common mistakes, platforms, and hands-on coding exercises.
Prompt engineering acts as a human AI bridge guiding large language models to produce precise outputs, from JSON lists to differential diagnosis, with multi-modal, few-shot, and chaining techniques.
Collaborate with clinicians, researchers, and IT to identify where prompts add value. Design and refine prompts, manage versions, integrate health record data, and ensure ethics and privacy.
Identify and fix common prompt mistakes to improve AI accuracy in healthcare prompts. Learn to specify clear goals, provide context, avoid jargon, set boundaries, manage model limitations, and verify outputs.
Explore sample platforms for prompt engineering, learn platform requirements, model behavior, and techniques like few-shot learning, tokenization, and system-level instructions across text, image, and audio models.
Explore larger issues in prompt engineering, including ethics, bias reduction through fair prompt design and diverse data, and ensuring explainability with attention visualization and reasoning steps.
Complete the extra credit exercises to build a scheduling system app for the Kaiser Permanente hospital system, using the cheat sheet, case study, and attached materials.
A Prompt Engineering Basics course equips students with the foundational skills to design effective prompts that guide AI language models to produce desired and accurate responses. The course covers essential topics such as crafting clear and specific instructions, iterative prompt refinement, understanding AI capabilities and limitations, and avoiding common pitfalls. Through practical exercises and real-world examples, students learn to optimize their interactions with AI for various applications, ensuring reliable and meaningful outcomes.
In addition to these core areas, the course delves into the nuances of language and context, teaching students how to frame questions and statements that elicit the most relevant and coherent AI-generated content. Students explore different prompt structures, including open-ended prompts, directive prompts, and situational prompts, to understand how each type influences the AI's responses. The curriculum also addresses ethical considerations in prompt engineering, emphasizing the importance of minimizing biases and ensuring fairness in AI interactions.
Interactive workshops and hands-on projects allow students to apply their knowledge in simulated environments, fostering a deeper understanding of how prompt adjustments can enhance AI performance. Collaborative assignments encourage teamwork and the sharing of diverse perspectives, mirroring real-world scenarios where prompt engineers work alongside developers, designers, and stakeholders. By the end of the course, participants are not only proficient in creating and refining prompts but also capable of evaluating and improving AI-driven solutions across various domains such as customer service, content creation, and data analysis. This comprehensive approach ensures that students are well-prepared to contribute effectively to the rapidly evolving field of AI and machine learning.