Leveraging AI with Prompting

We can leverage AI more effectively by using two key techniques: in-context learning and prompt engineering. 

In-context learning is a feature of AI models like ChatGPT where the model learns and adapts to the information given within a conversation. It doesn’t store information permanently but uses the context provided in the current session to generate more relevant and accurate responses. For example, if you first explain that you’re studying accounting and then ask about journal entries, the AI will tailor its explanations to that context.

Prompt engineering involves crafting your inputs (prompts) strategically to get the best results from AI. This includes being clear, specific, and context-rich, breaking complex questions into smaller parts, or even asking the AI to take on a specific role (e.g., “Explain this like you’re an accounting professor”). Effective prompt engineering guides the AI to give more accurate, relevant, and detailed answers.

You can use prompt engineering more effectively by leveraging in-context learning by gradually building context, layering questions from general to specific, and specifying roles to guide the AI’s perspective. Use iterative refinement by asking for clarifications or examples, and occasionally recap previous discussions to maintain context. This approach helps the AI understand your needs more deeply, resulting in more accurate and tailored answers.