Prompt engineering involves the process of providing instructions for an AI model to elicit the desired response, using carefully chosen words and context. If your first encounter with a Large Language Model (LLM) like ChatGPT was through a simple question-and-answer format, you might think prompt engineering is just about asking questions and receiving answers. However, it's more sophisticated than that.

AI models are designed not just to answer questions but to complete human thoughts. For example, consider the prompts "It's a beautiful day" versus "Is today beautiful?" The first is a statement, and the second is a question. The AI model attempts to strategically generate the best-fitting sequence of words to follow each prompt, effectively completing the human thought. This generated response is known as a "completion."

In essence, prompt engineering involves formulating questions, statements, or commands that guide the AI model to produce responses that are accurate, relevant, and aligned with your intended purpose. Understanding the salient features of prompt engineering

Finding the perfect balance

When you interact with an AI model like ChatGPT or DALL-E-3, you need to provide the right amount of information to get the desired result. If you give it too many words or too much context, it will struggle to provide the best possible answers. Conversely, too little context will result in a generic and broad answer. Good prompt engineering involves finding that perfect balance to guide the AI model in delivering the desired response.

When creating prompts, many people end up writing a lengthy story about what they want. This approach wastes time and energy with unnecessary words. Most Large Language Models (LLMs) will either limit or alter the length of your prompt to process it accurately. Therefore, you need to make your prompt concise and to-the-point to get the correct response.