Efficient Prompt Engineering: Getting the Right Answers

OpenAI’s GPT-3 and GPT-4 are powerful tools that can produce human-like text, answer questions, and provide insights. However, the quality of these outputs depends heavily on how you build the input or prompt. Efficient prompt engineering guides artificial intelligence through design input to produce relevant, clear and useful responses, ensuring you get the right answer. Let’s walk through an example to learn how to create effective reminders.

phosphorusRapid engineering is the art of creating cues that guide artificial intelligence models to achieve specific and desired outcomes. The way the question is asked can significantly affect the AI’s response. By carefully building and refining your prompts, you can minimize ambiguity and maximize relevance. Here is an example.

• Simple tips: “Explain climate change.”
• Engineering Tips: “Explains how human activity contributes to climate change, with a special focus on carbon emissions and deforestation.”

In design tips, we add more details and specific topics for AI to cover, resulting in more targeted answers.

How normal prompts work and their difficulties

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

• Normal prompt: “Tell me about renewable energy.”

Responses to this prompt may contain unnecessary information or may not focus on the renewable energy aspects that interest you.

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

• Engineering Tips: “Explain the environmental benefits of renewable energy using examples of how wind and solar energy reduce carbon emissions.”

Prepare your comments
To build an effective prompt, the first step is to prepare your input. This includes clearly defining what you want the AI ​​to focus on and adding any necessary context to narrow down the response. Here is an example.

• Before prompting the project: “What is artificial intelligence?”

• After the project: “A brief description of artificial intelligence, focusing on its use in healthcare for diagnostic purposes.”

Note how the designed prompts include the specific application of AI (health care) and context (diagnostic purposes), which will guide the model to more targeted answers.

Important tips are:

• The topic should be specific.
• Provide relevant background information.
• Define the focus area or scope of the response.

Plan output

Planning output involves thinking about what you expect back from the AI, including the length, structure, or format of the response. An example is given below.

• no plan: “Summary of the history of computers.”
• Have a plan: “Summarize the history of computing in three bullet points, focusing on key technological milestones of the 20th century.”

By planning the output, you can guide the AI ​​to produce responses that fit your needs – whether you want lists, comparisons, or summaries.

The key considerations here are:

• length: Specify whether you want a short or detailed answer.
• Format: Requires output in the form of bullet points, paragraphs, or numbered lists.
• tone/style: Indicate whether the reply should be technical, formal, or conversational.

Adjust output by testing different inputs

Trying out various prompts can help you understand how different wording or constraints affect the AI’s responses. A slight change in the way you ask a question can result in a very different answer. Let’s look at two examples.

Example 1

• first tip: “What are the benefits of exercise?”
• reply: A general answer that lists benefits ranging from weight loss to mental health to heart health.
• Adjustment Tips: “What are the mental health benefits of regular exercise, especially for people with anxiety disorders?”
• reply: A more focused answer, focusing on the connection between exercise and anxiety management.

Example 2

• first tip: “Machine Learning Explained.”
• Adjustment Tips: “Machine learning explained in simple terms for high school students.”

By changing the inputs, you can guide the AI ​​to achieve different levels of complexity or different types of answers.

Here are some useful lab tips.

• Reword the question to see how different wording affects the output.
• Try adding restrictions, such as asking for specific examples or a specific tone of voice.
• Use multiple versions of the question to test the range of answers.

Efficient and timely engineering

Efficient prompt engineering saves time and helps you get the right answers without unnecessary back and forth. You can do this by applying best practices that enhance clarity, specificity, and context. Here is an example.

• Inefficient tips: “How does the Internet work?”
• Productivity Tips: “Explain how the Internet works, focusing on the role of routers and IP addresses, and describe the process in 200 words.”

Effective prompts are specific, setting clear expectations for topic focus and even defining the required response length.

Here are the best practices.

• clear: Clearly define the scope, length, and format of the response.
• iterate: If the first response isn’t what you wanted, refine and test new prompts.
• Test changes: Explore different ways of asking the same question to find the most effective wording.

Get structured output: JSON formatted response

In some cases, you may want OpenAI to provide a structured response, especially if you are building an application that requires data to be formatted in a specific way. OpenAI can generate responses in formats such as JSON, which is useful for assessments, forms, or APIs.

When requesting answers in JSON format, structure and content requirements must be explicitly specified in the prompt. This ensures that the AI ​​understands the required format and the type of material it needs to contain.

Below is a sample prompt in JSON format.

• Quickly: “Generate three multiple-choice AI questions for assessment, along with four answer options for each question, and provide the correct answers in JSON format. The JSON format should follow the following structure:

{
“questions”: [
{
“question”: “Question text”,
“options”: [“Option A”, “Option B”, “Option C”, “Option D”],
“correct_answer”: “Correct option”
}
]
}

Provide data in this format for each question.

A sample response is:

{

“questions”: [

{

“question”: “What is a neural network?”,

“options”: [

“A system designed to mimic the human brain”,

“A type of network used for data transmission”,

“A social media platform”,

“An algorithm for decision trees”

],

“correct_answer”: “A system designed to mimic the human brain”

},

{

“question”: “Which of the following is a type of machine learning?”,

“options”: [

“Supervised learning”,

“Quantum computing”,

“Database management”,

“Firewall configuration”

],

“correct_answer”: “Supervised learning”

},

{

“question”: “Which company developed GPT-3?”,

“options”: [

“Google”,

“Microsoft”,

“OpenAI”,

“Amazon”

],

“correct_answer”: “OpenAI”

}

]

}

Why does this work? By specifying the exact structure of the JSON in the prompt, you can ensure that OpenAI produces a response that can be easily parsed by the system or used in automated workflows. This approach is particularly useful when using artificial intelligence in application development or form-based assessments.

Best practices are:

• Specify format: Be clear about the data structure (JSON in this case) and the keys you want to use in the response.
• Test output: If necessary, make sure the AI ​​response fits the format by adjusting the prompts.
• Use with app: This is ideal for integrating AI-generated data directly into APIs or repositories that require structured input.

Getting the right answer from an OpenAI model depends on how you ask the question. Through efficient prompt engineering, you can guide AI to provide clear, relevant, and targeted responses. By preparing thoughtful input, planning the desired output, and experimenting with various prompts, you can unlock the full potential of OpenAI language models. Remember, the more specific and structured your prompts are, the better answers you will receive.

Disclaimer: This article makes heavy use of OpenAI services to generate content and improve quality, because OpenAI understands the context of the prompt project better than we do to get the right answers. Ha ha!