Working Practices
Keep trying things out
When reading the materials, try out the examples and search for more information online. Learning about large language models in the context of software engineering goes hand in hand with exploring their capabilities and working on software engineering tasks.
Research on learning has shown over and over again that distributed practice, i.e. dividing your work over multiple days, leads to better learning outcomes than massed practice.
Coursework
The course materials include small exercises such as programming exercises and quizzes. Trying out the examples will help you in solving the coursework assignments. There is also a final project where you leverage large language models for a larger task.
The coursework is submitted for evaluation through the online course platform.
Academic integrity
Coursework and exercises can be completed with your colleagues and friends. Like in any learning, you are responsible for your own learning.
As the exercises are designed to help everyone learn, do not share your solutions or store them in any publicly available location.
Interacting with LLMs
In these materials, we provide LLMs to work and interact with. Any data sent to the LLMs will be stored and studied. Do not disclose any sensitive (or company data) to the LLMs, as the LLMs are external and the data sent to them can be used as training data for subsequent LLM generations.
The use of generative AI and large language models such as ChatGPT for completing coursework is allowed and encouraged. When instructed, also outline how you used them and what you observed while using them. Keep in mind that you should not send any sensitive data (or company data) to the LLMs.