When curating context for working with Chat AI like Microsoft Copilot, it's essential to provide ample information and feed it with references to enhance its understanding. Here's a summary of key points to effectively curate context:

• Detailed Information: Furnish Copilot with comprehensive details regarding the task, problem, or topic at hand. The more information you provide, the better Copilot can comprehend and generate relevant responses.

• Clear Objectives: Clearly outline your objectives and expectations to Copilot. Specify what you aim to achieve or the outcome you desire from its assistance. This clarity helps Copilot tailor its responses accordingly.

• Specific References: Supply Copilot with references relevant to the subject matter. These could include documents, articles, code snippets, or any other resources pertinent to the task. Referencing allows Copilot to draw upon existing knowledge and context to produce accurate and insightful outputs.

• Contextual Insights: Provide contextual insights or background information related to the task or conversation. This could include historical data, previous discussions, or any relevant context necessary for Copilot to understand the current situation better.

• Feedback Loop: Establish a feedback loop to refine Copilot's understanding and performance over time. Provide feedback on its responses, correct any misunderstandings, and offer guidance to help Copilot continuously improve its capabilities.

By following these guidelines and actively curating context, you can maximize the performance and effectiveness of Chat AI like Microsoft Copilot, enabling it to provide more accurate, relevant, and valuable assistance in various tasks and scenarios. Always keep in my mind that AI is continuously learning and should never be assumed as accurate right away.

In the next tip, we will discuss in more detail what it means to use AI as a collaborate tool rather than a standalone solution.