Efficient Prompt Engineering: Getting the Right Answers

OpenAl's GPT-3 and GPT-4 are powerful tools that can generate human-like text, answer questions, and provide insights. However, the quality of these outputs depends heavily on how you frame the input, or prompt. Efficient prompt engineering ensures you get the right answers by designing inputs that guide the AI towards relevant, clear, and useful responses. Let's find out how to craft effective prompts with examples.

Prompt engineering is the art of creating prompts that guide AI models towards specific and desired outcomes. The way a question is framed can significantly affect the AI’s response. By carefully structuring and refining your prompt, you can minimise ambiguity and maximise relevance. Here’s an example.

Simple prompt: “Explain climate change.”

Engineered prompt: “Explain how human activities contribute to climate change, specifically focusing on carbon emissions and deforestation.”

In the engineered prompt, we’ve added more details and specific topics for the AI to cover, resulting in a more focused answer.

How the normal prompt works and its difficulties

When people first interact with AI models, they tend to use simple, broad prompts. While these can produce useful results, they often lead to overly general or vague answers. Let’s take an example.

Normal prompt: “Tell me about renewable energy.” The response to this prompt may include a lot of unnecessary information, or it may not focus on the aspect of renewable energy you’re interested in.

The key challenge here is that broad prompts lead to responses that may be too generic or unfocused. By adding specificity and structure, you can avoid this.

Engineered prompt: “Explain the environmental benefits of renewable energy, with examples of how wind and solar power reduce carbon emissions.”

Prepare your input

To create an effective prompt, the first step is to prepare your input. This involves clearly defining what you want the AI to focus on and adding any necessary context to narrow down the response. Here’s an example.

Before prompt engineering: “What is artificial intelligence?”