Prompts best practices

Learn about best practices to follow when creating Prompts.

Introduction to Prompts Best Practices

The ability to craft well-structured prompts is crucial for eliciting accurate and relevant responses from large language models (LLMs). Prompts act as the bridge between human intent and machine understanding, guiding AI to deliver outputs that align with our expectations.

The art of prompt crafting is not just about asking the right questions; it's about providing the right context, setting clear goals, and understanding how to format your requests to get the best possible results from the LLMs. Whether you're looking to generate creative content, extract specific information, or automate tasks, the principles of prompt best practices remain the same. By adhering to these guidelines, you can transform a simple question into a powerful tool that unlocks the full potential of AI language models.

5 specific ingredients to write the perfect prompts:

Context: Begin by providing clear context. This sets the stage for the prompt and helps the language model understand the background information.

Specific Goal: Define a specific goal for your prompt. What exactly do you want to achieve with the response?

A Specific Format: Decide on the format you want the answer to be in. This could be a list, a paragraph, bullet points, markdown format.

Task Breakdown: If the task is complex, break it down into smaller, manageable tasks.

Provide Examples: Provide examples to illustrate what you're looking for. This can significantly improve the accuracy of the responses.

Prompt FAQ’s:

How specific should the context be? The context should be as specific as possible to guide the language model towards the desired outcome.

Can I use multiple formats in one prompt? Yes, but ensure each part of the prompt is clear about which format to use. How do I know if my task breakdown is effective? If each subtask leads logically to the next and the overall goal is achieved, your breakdown is effective. Should examples be real or hypothetical? They can be either, as long as they clearly illustrate the desired outcome.

How do I handle prompts that return unexpected results? Review the prompt for clarity and specificity, and adjust as needed to guide the model more precisely.