GitHub Copilot can generate sample data by creating fictitious information based on patterns in its training data and by using suggestions based on API documentation within the repository.

"Because GitHub Copilot is trained on a large amount of publicly available code, it often suggests snippets that reflect common coding practices and idioms." This shows that Copilot reflects the most common practices it has seen in its training data.
References: GitHub Copilot documentation on training and suggestion patterns.

A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.
Reference: AI risk management documentation.

Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.

GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.
Reference: GitHub Copilot content exclusion documentation.